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CN113282699B - Road network matching method for noisy and unidentified parameter bicycle track data - Google Patents

Road network matching method for noisy and unidentified parameter bicycle track data Download PDF

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CN113282699B
CN113282699B CN202110827926.8A CN202110827926A CN113282699B CN 113282699 B CN113282699 B CN 113282699B CN 202110827926 A CN202110827926 A CN 202110827926A CN 113282699 B CN113282699 B CN 113282699B
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蒋熙
苗建瑞
王书灵
初众甫
胡莹
刘缵杰
肖元轶
孙鹏飞
马洁
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Abstract

本发明涉及一种针对有噪声且参数不明的自行车轨迹数据的路网匹配方法，其中方法包括步骤：采集自行车的骑行轨迹线的轨迹点序列和路网中由骑行轨迹线的距离阈值S范围内的所有路段形成的近邻路段集合，构建轨迹线与路网之间的匹配概率网络T‑R‑N；构建从单轨迹点映射到路段的随机事件概率模型，计算节点事件发生概率；构建自行车前后轨迹点对的路段映射条件概率模型，计算节点事件之间的转移概率；构建自行车轨迹与路段之间的马尔可夫链，根据最大组合概率的自行车轨迹与路段之间的马尔可夫链，得到轨迹线的最优匹配结果。本发明的方案，是一种充分考虑当前自行车轨迹数据实际噪声状况的解决方案，可突破现实系统中因共享单车数据质量制约了骑行轨迹数据应用的实际难题。

Figure 202110827926

The invention relates to a road network matching method for bicycle trajectory data with noise and unknown parameters, wherein the method comprises the steps of: collecting a trajectory point sequence of a bicycle's cycling trajectory and a distance threshold S from the cycling trajectory in the road network The set of adjacent road segments formed by all road segments within the range, construct the matching probability network T-R-N between the trajectory line and the road network; construct the random event probability model mapped from a single trajectory point to the road segment, and calculate the occurrence probability of node events; construct Conditional probability model of road segment mapping of front and rear track point pairs of bicycles, calculating the transition probability between node events; constructing a Markov chain between the bicycle trajectory and the road segment, according to the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability , to obtain the optimal matching result of the trajectory line. The solution of the present invention is a solution that fully considers the actual noise condition of the current bicycle trajectory data, and can break through the practical problem that the application of the riding trajectory data is restricted by the quality of the shared bicycle data in the real system.

Figure 202110827926

Description

针对有噪声且参数不明的自行车轨迹数据的路网匹配方法A Road Network Matching Method for Bicycle Trajectory Data with Noisy and Unknown Parameters

技术领域technical field

本发明涉及一种自行车轨迹数据路网匹配技术，尤其是一种针对较大噪声且误差参数不明确条件下自行车轨迹数据的路网匹配方法。The invention relates to a road network matching technology for bicycle trajectory data, in particular to a road network matching method for bicycle trajectory data under the condition of relatively large noise and unclear error parameters.

背景技术Background technique

在全社会倡导绿色交通趋势下，共享单车的兴起不仅推动了慢行交通的发展，而且为交通管理领域的多种不同应用提供了大量的自行车骑行轨迹数据。由于轨迹数据与城市路网数据具有不同的来源，在二者之间构建空间关联是使用轨迹数据的必然条件，因此，将自行车轨迹数据准确匹配到路网上重要意义。Under the trend of advocating green transportation in the whole society, the rise of shared bicycles not only promotes the development of slow traffic, but also provides a large amount of cycling trajectory data for various applications in the field of traffic management. Since the trajectory data and the urban road network data have different sources, it is a necessary condition to use the trajectory data to establish a spatial correlation between the two. Therefore, it is of great significance to accurately match the bicycle trajectory data to the road network.

交通领域中，当前轨迹数据匹配的研究成果主要针对汽车车辆类交通主体。相比自行车轨迹数据，汽车车辆定位数据的规范性与精度更高、质量更好，且定位误差参数更容易获得，在此基础上便于进行基于噪声特性与动力学特性的轨迹纠偏与路网匹配。而自行车轨迹数据则受到骑行者主观因素、定位设备差异性及外界多种干扰因素的叠加影响，具有更复杂、偏差更大的数据噪声。另外，当前实际可用的骑行轨迹数据并非来源于专业GPS设备，而是来源于共享单车运营平台采集并存储到的骑行用户手机GPS定位数据，使得定位数据噪声特性更加不明确，同时还面临着骑行过程的“真值”数据难以获取的限制，不具备先将轨迹数据修正后再进行路网匹配的条件。这种情况下，当前车辆轨迹数据匹配方法均不适用，需要根据骑行特性及轨迹数据的特点研究适宜的轨迹数据路网匹配方法。In the field of transportation, the current research results of trajectory data matching are mainly aimed at vehicle traffic subjects. Compared with bicycle trajectory data, vehicle positioning data has higher standardization and accuracy, better quality, and the positioning error parameters are easier to obtain. On this basis, it is convenient to perform trajectory correction and road network matching based on noise characteristics and dynamic characteristics. . The bicycle trajectory data is affected by the subjective factors of cyclists, the differences of positioning equipment and the superposition of various external interference factors, and has more complex and biased data noise. In addition, the currently available cycling track data does not come from professional GPS equipment, but from the GPS positioning data of cycling users’ mobile phones collected and stored by the shared bicycle operation platform, which makes the noise characteristics of the positioning data more unclear, and also faces the problem of Due to the limitation that the "true value" data of the riding process is difficult to obtain, it does not have the conditions to first correct the trajectory data and then perform the road network matching. In this case, the current vehicle trajectory data matching methods are not applicable, and it is necessary to study the appropriate trajectory data road network matching method according to the riding characteristics and the characteristics of the trajectory data.

发明内容SUMMARY OF THE INVENTION

本发明的目的在于以充分利用共享单车运营平台的轨迹大数据，为城市慢行系统的交通管理应用提供数据基础，提供一种针对有噪声且参数不明的自行车轨迹数据的路网匹配方法、系统、电子设备及计算机可读存储介质。The purpose of the present invention is to make full use of the trajectory big data of the shared bicycle operation platform to provide a data basis for the traffic management application of the urban slow-moving system, and to provide a road network matching method and system for the bicycle trajectory data with noise and unknown parameters , electronic devices, and computer-readable storage media.

为实现上述目的，本发明提供一种针对有噪声且参数不明的自行车轨迹数据的路网匹配方法，包括：In order to achieve the above object, the present invention provides a road network matching method for bicycle trajectory data with noise and unknown parameters, including:

步骤1：采集自行车的骑行轨迹线的轨迹点序列

Figure 492090DEST_PATH_IMAGE001
和路网中由所述骑行轨迹线的距离阈值S范围内的所有路段形成的近邻路段集合
Figure 939252DEST_PATH_IMAGE002
，N为所述骑行轨迹线上的轨迹点的总数，M为所述近邻路段集合中的路段总数，构建轨迹线与路网之间的匹配概率网络T-R-N；Step 1: Collect the track point sequence of the cycling track line of the bicycle
Figure 492090DEST_PATH_IMAGE001
and the set of nearby road segments formed by all road segments within the range of the distance threshold S of the cycling trajectory line in the road network
Figure 939252DEST_PATH_IMAGE002
, N is the total number of trajectory points on the riding trajectory line, M is the total number of road sections in the set of adjacent road sections, and a matching probability network TRN between the trajectory line and the road network is constructed;

步骤2：根据所述轨迹点序列和所述近邻路段集合，构建从单轨迹点映射到路段的随机事件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件发生概率

Figure 597767DEST_PATH_IMAGE003
；Step 2: According to the trajectory point sequence and the set of adjacent road sections, construct a random event probability model mapped from a single trajectory point to a road section, and calculate the matching probability between the trajectory line and the road network. The occurrence of node events in the network TRN probability
Figure 597767DEST_PATH_IMAGE003
;

步骤3：根据所述轨迹点序列中的相邻轨迹点对（即一对相邻轨迹点），构建自行车前后轨迹点对映射到路段的条件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件之间的转移概率

Figure 418961DEST_PATH_IMAGE004
；Step 3: According to the adjacent trajectory point pairs in the trajectory point sequence (that is, a pair of adjacent trajectory points), construct a conditional probability model in which the front and rear trajectory point pairs of the bicycle are mapped to the road sections, and calculate the distance between the trajectory line and the road network. The transition probabilities between node events in the matching probability network TRN
Figure 418961DEST_PATH_IMAGE004
;

步骤4：在所述轨迹线与路网之间的匹配概率网络T-R-N上构建自行车轨迹与路段之间的马尔可夫链，根据所述节点事件发生概率

Figure 391596DEST_PATH_IMAGE005
和所述节点事件之间的转移概率
Figure 958844DEST_PATH_IMAGE006
，得到最大组合概率的自行车轨迹与路段之间的马尔可夫链，其所对应的路段序列
Figure 37527DEST_PATH_IMAGE007
为轨迹线的最优匹配结果。Step 4: Build a Markov chain between the bicycle trajectory and the road segment on the matching probability network TRN between the trajectory line and the road network, according to the probability of occurrence of the node event
Figure 391596DEST_PATH_IMAGE005
and the transition probability between the node events
Figure 958844DEST_PATH_IMAGE006
, the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability is obtained, and the corresponding road segment sequence
Figure 37527DEST_PATH_IMAGE007
is the optimal matching result of the trajectory line.

根据本发明的一个方面，构建轨迹线对路网的匹配概率网络T-R-N为：According to one aspect of the present invention, the matching probability network T-R-N for constructing the trajectory line-to-road network is:

通过单点角度提取轨迹点与路段空间映射的不确定性关系和前后点间关联角度提取骑行过程在路段上空间关联的不确定性关系，构建轨迹线与路网之间的匹配概率网络T-R-N。Extract the uncertainty relationship between the trajectory point and the road segment spatial mapping through the single-point angle and the correlation angle between the front and rear points to extract the uncertainty relationship of the spatial association of the riding process on the road segment, and construct a matching probability network T-R-N between the trajectory line and the road network. .

根据本发明的一个方面，构建从单轨迹点映射到路段的随机事件概率模型为：According to one aspect of the present invention, constructing a random event probability model mapped from a single trajectory point to a road segment is:

通过利用轨迹点与近邻路段集合R中各个路段的垂直距离关系，构建基于横向距离分布的从单轨迹点映射到路段的随机事件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件发生的概率

Figure 300012DEST_PATH_IMAGE008
。By using the vertical distance relationship between the trajectory point and each road segment in the set R of adjacent road segments, a random event probability model from a single trajectory point to a road segment is constructed based on the horizontal distance distribution, and the matching probability network between the trajectory line and the road network is calculated. Probability of node events in TRN
Figure 300012DEST_PATH_IMAGE008
.

根据本发明的一个方面，构建前后轨迹点对映射到路段的条件概率模型为：According to one aspect of the present invention, constructing a conditional probability model for mapping front and rear track point pairs to road segments is:

通过利用所述相邻轨迹点对所在路段间的空间关联关系，构建所述前后轨迹点对的路段映射条件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件之间的转移概率

Figure 122344DEST_PATH_IMAGE009
。By using the spatial correlation between the road segments where the adjacent trajectory point pairs are located, a road segment mapping conditional probability model of the front and rear trajectory point pairs is constructed, and the matching probability between the trajectory line and the road network is calculated. Node events in the network TRN transition probability between
Figure 122344DEST_PATH_IMAGE009
.

根据本发明的一个方面，获得轨迹线的最优匹配结果为：According to one aspect of the present invention, the optimal matching result of the trajectory line is obtained as follows:

在所述轨迹线与路网之间的匹配概率网络T-R-N上，对每种匹配方案构建所述自行车轨迹与路段之间的马尔可夫链，根据所述节点事件发生概率

Figure 544098DEST_PATH_IMAGE010
和所述节点事件之间的转移概率
Figure 747677DEST_PATH_IMAGE011
，通过网络路径搜索算法搜索出所述最大组合概率的自行车轨迹与路段之间的马尔可夫链，其所对应的路段序列
Figure 543464DEST_PATH_IMAGE012
为轨迹线的最优匹配结果。On the matching probability network TRN between the trajectory line and the road network, a Markov chain between the bicycle trajectory and the road segment is constructed for each matching scheme, according to the probability of occurrence of the node event
Figure 544098DEST_PATH_IMAGE010
and the transition probability between the node events
Figure 747677DEST_PATH_IMAGE011
, the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability is searched through the network path search algorithm, and the corresponding road segment sequence
Figure 543464DEST_PATH_IMAGE012
is the optimal matching result of the trajectory line.

根据本发明的一个方面，构建轨迹线与路网之间的匹配概率网络T-R-N包括：According to one aspect of the present invention, constructing the matching probability network T-R-N between the trajectory line and the road network includes:

根据所述轨迹点序列

Figure 654639DEST_PATH_IMAGE013
中的任一轨迹点
Figure 930900DEST_PATH_IMAGE014
，得到轨迹点i在所述近邻路段集合
Figure 554648DEST_PATH_IMAGE015
中路段
Figure 854042DEST_PATH_IMAGE016
的不确定性事件节点
Figure 831226DEST_PATH_IMAGE017
，同一轨迹点i的所有事件节点构成集合
Figure 148943DEST_PATH_IMAGE018
，得到所有的事件节点构成所述路网匹配概率网络T-R-N中的节点集合
Figure 428746DEST_PATH_IMAGE019
；According to the trajectory point sequence
Figure 654639DEST_PATH_IMAGE013
any track point in
Figure 930900DEST_PATH_IMAGE014
, get the trajectory point i in the set of adjacent road sections
Figure 554648DEST_PATH_IMAGE015
middle section
Figure 854042DEST_PATH_IMAGE016
The uncertainty event node of
Figure 831226DEST_PATH_IMAGE017
, all event nodes of the same trajectory point i constitute a set
Figure 148943DEST_PATH_IMAGE018
, get all the event nodes to form the node set in the road network matching probability network TRN
Figure 428746DEST_PATH_IMAGE019
;

根据所述轨迹点序列

Figure 543333DEST_PATH_IMAGE020
中的前后相邻轨迹点对
Figure 511158DEST_PATH_IMAGE021
，得到在任一轨迹点
Figure 371797DEST_PATH_IMAGE022
所属的任意路段
Figure 212714DEST_PATH_IMAGE023
的不确定性事件节点
Figure 735968DEST_PATH_IMAGE024
和轨迹点
Figure 930321DEST_PATH_IMAGE025
所属的任意路段
Figure 301259DEST_PATH_IMAGE026
的不确定性事件节点
Figure 234449DEST_PATH_IMAGE027
之间设置的有向连接弧
Figure 933415DEST_PATH_IMAGE028
，其代表事件之间的转移关系，所有的事件转移关系构成了所述轨迹线与路网之间的匹配概率网络T-R-N中的有向连接弧集合
Figure 56091DEST_PATH_IMAGE029
，构建所述轨迹线与路网之间的匹配概率网络T-R-N。According to the trajectory point sequence
Figure 543333DEST_PATH_IMAGE020
pair of adjacent trajectory points in
Figure 511158DEST_PATH_IMAGE021
, get at any trajectory point
Figure 371797DEST_PATH_IMAGE022
any road segment
Figure 212714DEST_PATH_IMAGE023
The uncertainty event node of
Figure 735968DEST_PATH_IMAGE024
and track points
Figure 930321DEST_PATH_IMAGE025
any road segment
Figure 301259DEST_PATH_IMAGE026
The uncertainty event node of
Figure 234449DEST_PATH_IMAGE027
Directed connection arcs set between
Figure 933415DEST_PATH_IMAGE028
, which represents the transition relationship between events, and all event transition relationships constitute the set of directed connection arcs in the matching probability network TRN between the trajectory line and the road network
Figure 56091DEST_PATH_IMAGE029
, and construct the matching probability network TRN between the trajectory line and the road network.

根据本发明的一个方面，计算节点事件发生的概率包括：According to one aspect of the present invention, calculating the probability of occurrence of a node event includes:

根据各个轨迹点和近邻路段集中各个路段的坐标数据，得到任一轨迹点

Figure 202908DEST_PATH_IMAGE030
与所述近邻路段集合中任一路段
Figure 260994DEST_PATH_IMAGE031
的中心点的垂直距离
Figure 571889DEST_PATH_IMAGE032
，得到所述轨迹点
Figure 419628DEST_PATH_IMAGE033
的所有近邻路段中最远的近邻路段的垂直距离
Figure 374946DEST_PATH_IMAGE034
，计算得到任一近邻路段距离
Figure 649938DEST_PATH_IMAGE035
与所述最远的近邻路段的垂直距离
Figure 448130DEST_PATH_IMAGE036
的差值
Figure 787976DEST_PATH_IMAGE037
：According to each trajectory point and the coordinate data of each road section in the set of adjacent road sections, any trajectory point is obtained
Figure 202908DEST_PATH_IMAGE030
with any road segment in the set of adjacent road segments
Figure 260994DEST_PATH_IMAGE031
The vertical distance of the center point of
Figure 571889DEST_PATH_IMAGE032
, get the trajectory point
Figure 419628DEST_PATH_IMAGE033
The vertical distance of the farthest neighbor of all neighbors
Figure 374946DEST_PATH_IMAGE034
, calculate the distance of any nearby road segment
Figure 649938DEST_PATH_IMAGE035
the vertical distance from the farthest neighbor
Figure 448130DEST_PATH_IMAGE036
difference
Figure 787976DEST_PATH_IMAGE037
:

Figure 988013DEST_PATH_IMAGE038
；
Figure 988013DEST_PATH_IMAGE038
;

其中，

Figure 433907DEST_PATH_IMAGE039
表示表示任一轨迹点
Figure 329181DEST_PATH_IMAGE040
与所述近邻路段集合中路段
Figure 518723DEST_PATH_IMAGE041
的中心点的垂直距离，
Figure 573267DEST_PATH_IMAGE042
表示轨迹点
Figure 144057DEST_PATH_IMAGE043
的所有近邻路段中最远的近邻路段的垂直距离，
Figure 838212DEST_PATH_IMAGE044
表示
Figure 378915DEST_PATH_IMAGE045
与所述最远的近邻路段的垂直距离
Figure 225648DEST_PATH_IMAGE046
的差值，
Figure 216607DEST_PATH_IMAGE047
表示第
Figure 211108DEST_PATH_IMAGE048
个轨迹点，
Figure 696447DEST_PATH_IMAGE049
表示第
Figure 646954DEST_PATH_IMAGE049
个路段；in,
Figure 433907DEST_PATH_IMAGE039
Represents any trajectory point
Figure 329181DEST_PATH_IMAGE040
The road segment in the set with the neighbor road segment
Figure 518723DEST_PATH_IMAGE041
The vertical distance from the center point of ,
Figure 573267DEST_PATH_IMAGE042
Represents a track point
Figure 144057DEST_PATH_IMAGE043
The vertical distance of the farthest adjacent road segment among all the adjacent road segments of ,
Figure 838212DEST_PATH_IMAGE044
express
Figure 378915DEST_PATH_IMAGE045
the vertical distance from the farthest neighbor
Figure 225648DEST_PATH_IMAGE046
difference of ,
Figure 216607DEST_PATH_IMAGE047
means the first
Figure 211108DEST_PATH_IMAGE048
track points,
Figure 696447DEST_PATH_IMAGE049
means the first
Figure 646954DEST_PATH_IMAGE049
a road segment;

根据所述轨迹点

Figure 684180DEST_PATH_IMAGE050
与各路段垂直距离的定位偏差是独立同分布随机变量，得到轨迹点
Figure 41343DEST_PATH_IMAGE051
在所述近邻路段集合中的路段
Figure 376379DEST_PATH_IMAGE052
的所述节点事件发生概率
Figure 260021DEST_PATH_IMAGE053
与该轨迹点距所述路段
Figure 343515DEST_PATH_IMAGE054
的垂直距离相关，
Figure 233979DEST_PATH_IMAGE055
越小，则所述节点事件发生概率
Figure 920175DEST_PATH_IMAGE056
越大，得到轨迹点
Figure 799270DEST_PATH_IMAGE057
在所述近邻路段集合中的其他各个路段k的所述
Figure 365249DEST_PATH_IMAGE058
相关，采用公式计算
Figure 556059DEST_PATH_IMAGE059
：According to the trajectory point
Figure 684180DEST_PATH_IMAGE050
The positioning deviation of the vertical distance from each road section is an independent and identically distributed random variable, and the trajectory point is obtained.
Figure 41343DEST_PATH_IMAGE051
road segments in the set of neighbor road segments
Figure 376379DEST_PATH_IMAGE052
The node event occurrence probability of
Figure 260021DEST_PATH_IMAGE053
Distance from the track point to the road segment
Figure 343515DEST_PATH_IMAGE054
is related to the vertical distance,
Figure 233979DEST_PATH_IMAGE055
The smaller the probability of occurrence of the node event
Figure 920175DEST_PATH_IMAGE056
bigger, get track points
Figure 799270DEST_PATH_IMAGE057
In the set of neighboring road segments, the other respective road segments k
Figure 365249DEST_PATH_IMAGE058
Correlation, calculated by formula
Figure 556059DEST_PATH_IMAGE059
:

Figure 983629DEST_PATH_IMAGE060
；
Figure 983629DEST_PATH_IMAGE060
;

其中，

Figure 435339DEST_PATH_IMAGE061
表示节点事件发生的概率，
Figure 922952DEST_PATH_IMAGE062
表示轨迹点
Figure 601058DEST_PATH_IMAGE063
的所有近邻路段中最远的近邻路段的垂直距离，
Figure 81587DEST_PATH_IMAGE064
表示任一轨迹点
Figure 325487DEST_PATH_IMAGE065
与所述近邻路段集合中任一路段
Figure 905373DEST_PATH_IMAGE066
的中心点的垂直距离，
Figure 336354DEST_PATH_IMAGE067
表示任一轨迹点
Figure 105727DEST_PATH_IMAGE068
与所述近邻路段几何中其他路段k的中心点的垂直距离，
Figure 821046DEST_PATH_IMAGE047
表示第
Figure 712778DEST_PATH_IMAGE069
个轨迹点，
Figure 303160DEST_PATH_IMAGE070
表示第
Figure 328753DEST_PATH_IMAGE071
个路段，
Figure 484928DEST_PATH_IMAGE072
表示第
Figure 688507DEST_PATH_IMAGE073
个路段。in,
Figure 435339DEST_PATH_IMAGE061
represents the probability of a node event occurring,
Figure 922952DEST_PATH_IMAGE062
Represents a track point
Figure 601058DEST_PATH_IMAGE063
The vertical distance of the farthest adjacent road segment among all the adjacent road segments of ,
Figure 81587DEST_PATH_IMAGE064
represents any track point
Figure 325487DEST_PATH_IMAGE065
with any road segment in the set of adjacent road segments
Figure 905373DEST_PATH_IMAGE066
The vertical distance from the center point of ,
Figure 336354DEST_PATH_IMAGE067
represents any track point
Figure 105727DEST_PATH_IMAGE068
the vertical distance from the center point of other road segments k in the neighbor road segment geometry,
Figure 821046DEST_PATH_IMAGE047
means the first
Figure 712778DEST_PATH_IMAGE069
track points,
Figure 303160DEST_PATH_IMAGE070
means the first
Figure 328753DEST_PATH_IMAGE071
road section,
Figure 484928DEST_PATH_IMAGE072
means the first
Figure 688507DEST_PATH_IMAGE073
road section.

根据本发明的一个方面，计算轨迹线与路网之间的匹配概率网络T-R-N中的节点事件之间的转移概率

Figure 749873DEST_PATH_IMAGE074
包括：According to one aspect of the present invention, the matching probability between the trajectory line and the road network is calculated as the transition probability between the node events in the network TRN
Figure 749873DEST_PATH_IMAGE074
include:

通过利用路网数据中各个路段间的空间关联关系，定义得到所述近邻路段集合中任意两条路段

Figure 454524DEST_PATH_IMAGE075
与
Figure 606151DEST_PATH_IMAGE076
之间的转移阻抗
Figure 105265DEST_PATH_IMAGE077
，其取值区间为
Figure 653927DEST_PATH_IMAGE078
，设定路网上路段
Figure 772056DEST_PATH_IMAGE079
与路段
Figure 902823DEST_PATH_IMAGE073
之间的最短距离长度为
Figure 697472DEST_PATH_IMAGE080
，
Figure 484163DEST_PATH_IMAGE081
的赋值公式如下：By using the spatial relationship between each road segment in the road network data, any two road segments in the set of adjacent road segments are defined and obtained
Figure 454524DEST_PATH_IMAGE075
and
Figure 606151DEST_PATH_IMAGE076
transfer impedance between
Figure 105265DEST_PATH_IMAGE077
, whose value range is
Figure 653927DEST_PATH_IMAGE078
, set the road network segment
Figure 772056DEST_PATH_IMAGE079
with road sections
Figure 902823DEST_PATH_IMAGE073
The shortest distance between
Figure 697472DEST_PATH_IMAGE080
,
Figure 484163DEST_PATH_IMAGE081
The assignment formula is as follows:

Figure 265037DEST_PATH_IMAGE082
；
Figure 265037DEST_PATH_IMAGE082
;

其中，

Figure 640524DEST_PATH_IMAGE083
表示任意两条路段
Figure 153545DEST_PATH_IMAGE084
与
Figure 755427DEST_PATH_IMAGE085
之间的转移阻抗，
Figure 464626DEST_PATH_IMAGE086
表示第
Figure 569985DEST_PATH_IMAGE086
个路段，
Figure 253908DEST_PATH_IMAGE073
表示第
Figure 202141DEST_PATH_IMAGE073
个路段，
Figure 324818DEST_PATH_IMAGE087
表示路网上路段
Figure 222367DEST_PATH_IMAGE086
与路段
Figure 529720DEST_PATH_IMAGE073
之间的最短距离；in,
Figure 640524DEST_PATH_IMAGE083
represents any two road segments
Figure 153545DEST_PATH_IMAGE084
and
Figure 755427DEST_PATH_IMAGE085
transfer impedance between,
Figure 464626DEST_PATH_IMAGE086
means the first
Figure 569985DEST_PATH_IMAGE086
road section,
Figure 253908DEST_PATH_IMAGE073
means the first
Figure 202141DEST_PATH_IMAGE073
road section,
Figure 324818DEST_PATH_IMAGE087
Represents a road network segment
Figure 222367DEST_PATH_IMAGE086
with road sections
Figure 529720DEST_PATH_IMAGE073
the shortest distance between;

当轨迹点

Figure 840616DEST_PATH_IMAGE088
在路段
Figure 642349DEST_PATH_IMAGE089
上时，后一个轨迹点
Figure 643672DEST_PATH_IMAGE090
在路段
Figure 262873DEST_PATH_IMAGE091
上的概率为所述节点事件之间的转移概率
Figure 670851DEST_PATH_IMAGE092
，得到所述节点事件之间的转移概率与路段间的转移阻抗
Figure 400910DEST_PATH_IMAGE093
相关，所述转移阻抗越小则所述节点事件之间的转移概率越大，用下述公式进行计算：when the track point
Figure 840616DEST_PATH_IMAGE088
on the road
Figure 642349DEST_PATH_IMAGE089
up, the next track point
Figure 643672DEST_PATH_IMAGE090
on the road
Figure 262873DEST_PATH_IMAGE091
The probability on is the transition probability between the node events
Figure 670851DEST_PATH_IMAGE092
, obtain the transition probability between the node events and the transition impedance between the road segments
Figure 400910DEST_PATH_IMAGE093
Correlation, the smaller the transition impedance, the greater the transition probability between the node events, which is calculated by the following formula:

Figure 256739DEST_PATH_IMAGE094
；
Figure 256739DEST_PATH_IMAGE094
;

其中，

Figure 656628DEST_PATH_IMAGE095
表示节点事件之间的转移概率，
Figure 863487DEST_PATH_IMAGE096
表示路段间的转移阻抗，
Figure 866078DEST_PATH_IMAGE097
表示求和时第
Figure 795988DEST_PATH_IMAGE098
个转移阻抗数据。in,
Figure 656628DEST_PATH_IMAGE095
represents the transition probability between node events,
Figure 863487DEST_PATH_IMAGE096
represents the transfer impedance between road segments,
Figure 866078DEST_PATH_IMAGE097
Indicates the time of summation
Figure 795988DEST_PATH_IMAGE098
transfer impedance data.

根据本发明的一个方面，构建自行车轨迹与路段之间的马尔可夫链包括：According to one aspect of the present invention, constructing a Markov chain between bicycle trajectories and road segments includes:

通过设置轨迹线与路网之间的匹配概率网络T-R-N中的网络节点集合

Figure 678362DEST_PATH_IMAGE099
中的每一个节点为初始节点，网络节点集合
Figure 185567DEST_PATH_IMAGE100
中的任一节点为终到节点，利用所述轨迹线与路网之间的匹配概率网络T-R-N中的有向连接弧，遍历整个网络，所述起点节点到所述终到节点间的任意一条节点序列对应一项自行车轨迹线匹配方案，构建自行车轨迹与路段之间的马尔可夫链。The set of network nodes in the network TRN by setting the matching probability between the trajectory line and the road network
Figure 678362DEST_PATH_IMAGE099
Each node in is an initial node, a collection of network nodes
Figure 185567DEST_PATH_IMAGE100
Any node is the final node, and the directed connection arc in the matching probability network TRN between the trajectory line and the road network is used to traverse the entire network, and any one between the starting node and the final node is used. The node sequence corresponds to a bicycle trajectory line matching scheme, and a Markov chain between bicycle trajectory and road segment is constructed.

根据本发明的一个方面，在轨迹线与路网之间的匹配概率网络T-R-N上搜索最大组合概率的自行车轨迹与路段之间的马尔可夫链并实现轨迹线的最优匹配结果包括：According to one aspect of the present invention, searching the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability on the matching probability network T-R-N between the trajectory line and the road network and realizing the optimal matching result of the trajectory line includes:

所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件发生概率

Figure 867215DEST_PATH_IMAGE101
和所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件之间的转移概率
Figure 963216DEST_PATH_IMAGE102
通过公式计算得到自行车轨迹与路段之间的马尔可夫链的组合概率
Figure 829541DEST_PATH_IMAGE103
，得到最大组合概率的自行车轨迹与路段之间的马尔可夫链，The matching probability between the trajectory line and the road network The node event occurrence probability in the network TRN
Figure 867215DEST_PATH_IMAGE101
and the matching probability between the trajectory line and the road network and the transition probability between node events in the network TRN
Figure 963216DEST_PATH_IMAGE102
The combined probability of the Markov chain between the bicycle track and the road segment is calculated by the formula
Figure 829541DEST_PATH_IMAGE103
, the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability is obtained,

公式：

Figure 699408DEST_PATH_IMAGE104
；formula:
Figure 699408DEST_PATH_IMAGE104
;

其中，

Figure 309381DEST_PATH_IMAGE105
表示节点事件发生概率，
Figure 259888DEST_PATH_IMAGE106
表示节点事件之间的转移概率，
Figure 172480DEST_PATH_IMAGE107
表示自行车轨迹的路段马尔可夫连的组合概率；in,
Figure 309381DEST_PATH_IMAGE105
represents the probability of node event occurrence,
Figure 259888DEST_PATH_IMAGE106
represents the transition probability between node events,
Figure 172480DEST_PATH_IMAGE107
the combined probability of the Markov connection representing the segment of the bicycle trajectory;

根据所述轨迹线的路段匹配概率网络T-R-N，通过网络路径搜索算法搜索出所述最大组合概率的自行车轨迹与路段之间的马尔可夫链，其所对应的路段序列

Figure 575649DEST_PATH_IMAGE108
为轨迹线的最优匹配结果。According to the road segment matching probability network TRN of the trajectory line, the Markov chain between the bicycle trajectory with the maximum combined probability and the road segment is searched through the network path search algorithm, and the corresponding road segment sequence
Figure 575649DEST_PATH_IMAGE108
is the optimal matching result of the trajectory line.

为实现上述目的，本发明提供一种针对较大噪声且参数不明的自行车轨迹数据的路网匹配系统，包括：In order to achieve the above object, the present invention provides a road network matching system for bicycle trajectory data with relatively large noise and unknown parameters, including:

路网网络构建模块，采集自行车的骑行轨迹线的轨迹点序列

Figure 989313DEST_PATH_IMAGE109
和路网中由所述骑行轨迹线的距离阈值S范围内的所有路段形成的近邻路段集合
Figure 482742DEST_PATH_IMAGE110
，N为所述骑行轨迹线上的轨迹点的总数，M为所述近邻路段集合中的路段总数，构建轨迹线与路网之间的匹配概率网络T-R-N；The road network network building module collects the trajectory point sequence of the bicycle's riding trajectory
Figure 989313DEST_PATH_IMAGE109
and the set of nearby road segments formed by all road segments within the range of the distance threshold S of the cycling trajectory line in the road network
Figure 482742DEST_PATH_IMAGE110
, N is the total number of trajectory points on the riding trajectory line, M is the total number of road sections in the set of adjacent road sections, and a matching probability network TRN between the trajectory line and the road network is constructed;

单轨迹点模型构建模块，根据所述轨迹点序列和所述近邻路段集合，构建从单轨迹点映射到路段的随机事件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件发生概率

Figure 956449DEST_PATH_IMAGE111
；A single trajectory point model building module, according to the trajectory point sequence and the set of adjacent road segments, constructs a random event probability model mapped from a single trajectory point to a road segment, and calculates the matching probability between the trajectory line and the road network in the network TRN The probability of node event occurrence
Figure 956449DEST_PATH_IMAGE111
;

前后轨迹点模型构建模块，根据所述轨迹点序列中的相邻轨迹点对，构建自行车前后轨迹点对映射到路段的条件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件之间的转移概率

Figure 846913DEST_PATH_IMAGE112
；The front and rear trajectory point model building module, according to the adjacent trajectory point pairs in the trajectory point sequence, constructs the conditional probability model of the bicycle front and rear trajectory point pairs mapped to the road section, and calculates the matching probability network TRN between the trajectory line and the road network The transition probabilities between node events in
Figure 846913DEST_PATH_IMAGE112
;

轨迹线匹配模块，在所述轨迹线与路网之间的匹配概率网络T-R-N上构建自行车轨迹与路段之间的马尔可夫链，根据所述节点事件发生概率

Figure 408476DEST_PATH_IMAGE113
和所述节点事件之间的转移概率
Figure 412204DEST_PATH_IMAGE114
，得到最大组合概率的自行车轨迹与路段之间的马尔可夫链，其所对应的路段序列
Figure 712604DEST_PATH_IMAGE115
为轨迹线的最优匹配结果。The trajectory line matching module constructs a Markov chain between the bicycle trajectory and the road segment on the matching probability network TRN between the trajectory line and the road network, according to the probability of occurrence of the node event
Figure 408476DEST_PATH_IMAGE113
and the transition probability between the node events
Figure 412204DEST_PATH_IMAGE114
, the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability is obtained, and the corresponding road segment sequence
Figure 712604DEST_PATH_IMAGE115
is the optimal matching result of the trajectory line.

为实现上述目的，本发明提供一种电子设备，包括处理器、存储器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述计算机程序被所述处理器执行时实现上述针对有噪声且参数不明的自行车轨迹数据的路网匹配方法。In order to achieve the above object, the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored on the memory and running on the processor, the computer program being implemented when executed by the processor The above road network matching method for the bicycle trajectory data with noise and unknown parameters.

为实现上述目的，本发明提供一种计算机可读存储介质，所述计算机可读存储介质上存储计算机程序，所述计算机程序被处理器执行时实现上述针对有噪声且参数不明的自行车轨迹数据的路网匹配方法。In order to achieve the above object, the present invention provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned data for bicycle track data with noise and unknown parameters is realized. Road network matching method.

基于此，本发明的有益效果在于：Based on this, the beneficial effects of the present invention are:

1.利用了共享单车运营平台积累的带轨迹点定位坐标的微观骑行数据进行路网匹配处理，能够满足慢性系统交通管理应用对骑行数据处理与微观骑行行为分析的要求，并充分挖掘共享经济背景下的大数据对交通的应用价值。1. Using the micro-cycling data with track point positioning coordinates accumulated by the shared bicycle operation platform for road network matching processing, it can meet the requirements of chronic system traffic management applications for cycling data processing and micro-cycling behavior analysis, and fully excavate The application value of big data to transportation in the context of sharing economy.

2.与一般的基于轨迹修正后的数据实现路网匹配方式不同，本发明考虑了自行车运动有别于汽车车辆行驶的特殊性，以及共享单车实际轨迹数据具有较大噪声且参数不明确的现实条件，更好地适应自行车骑行行为特点与骑行轨迹的实际数据特点，提高了既有轨迹大数据的可用性。2. Different from the general way of realizing road network matching based on the data after trajectory correction, the present invention takes into account the particularity of bicycle movement that is different from the driving of automobiles, and the reality that the actual trajectory data of shared bicycles has relatively large noise and unclear parameters. conditions, better adapt to the characteristics of bicycle riding behavior and the actual data characteristics of the riding trajectory, and improve the availability of existing trajectory big data.

3.构建了基于概率网络与不确定性匹配模型的轨迹数据路网匹配方法，适应自行车骑行随机性强的特点与骑行轨迹数据难以修正的制约条件，模型简洁，求解方便，便于实施。3. A road network matching method of trajectory data based on probability network and uncertainty matching model is constructed, which adapts to the characteristics of strong randomness of bicycle riding and the constraints that cycling trajectory data is difficult to modify. The model is simple, easy to solve and easy to implement.

附图说明Description of drawings

图1示意性表示根据本发明的针对有噪声且参数不明的自行车轨迹数据的路网匹配方法的流程图；FIG. 1 schematically shows a flow chart of a road network matching method for noisy bicycle track data with unknown parameters according to the present invention;

图2示意性表示自行车轨迹线836430的空间位置图；FIG. 2 schematically represents the spatial position diagram of the bicycle track line 836430;

图3示意性表示自行车轨迹线与路网之间的匹配概率网络T-R-N图；Fig. 3 schematically represents the matching probability network T-R-N diagram between the bicycle trajectory line and the road network;

图4示意性表示自行车轨迹线的最优匹配路段示意图；FIG. 4 schematically shows the schematic diagram of the optimal matching road section of the bicycle trajectory line;

图5示意性表示根据本发明的针对较大噪声且参数不明的自行车轨迹数据的路网匹配系统图。FIG. 5 schematically shows a diagram of a road network matching system according to the present invention for bicycle trajectory data with relatively large noise and unknown parameters.

具体实施方式Detailed ways

现在将参照示例性实施例来论述本发明的内容。应当理解，论述的实施例仅是为了使得本领域普通技术人员能够更好地理解且因此实现本发明的内容，而不是暗示对本发明的范围的任何限制。The present invention will now be discussed with reference to exemplary embodiments. It should be understood that the discussed embodiments are only provided to enable those of ordinary skill in the art to better understand and thus implement the content of the present invention, and are not intended to imply any limitation on the scope of the present invention.

如本文中所使用的，术语“包括”及其变体要被解读为意味着“包括但不限于”的开放式术语。术语“基于”要被解读为“至少部分地基于”。术语“一个实施例”和“一种实施例”要被解读为“至少一个实施例”。As used herein, the term "including" and variations thereof are to be read as open-ended terms meaning "including, but not limited to." The term "based on" is to be read as "based at least in part on". The terms "one embodiment" and "one embodiment" are to be read as "at least one embodiment."

图1示意性表示根据本发明的针对有噪声且参数不明的自行车轨迹数据的路网匹配方法的流程图。如图1所示，根据本发明的针对有噪声且参数不明的自行车轨迹数据的路网匹配方法，包括以下步骤：FIG. 1 schematically shows a flowchart of a road network matching method for noisy bicycle trajectory data with unknown parameters according to the present invention. As shown in FIG. 1 , the road network matching method for bicycle trajectory data with noise and unknown parameters according to the present invention includes the following steps:

101：采集自行车的骑行轨迹线的轨迹点序列

Figure 44359DEST_PATH_IMAGE116
和路网中由所述骑行轨迹线的距离阈值S范围内的所有路段形成的近邻路段集合
Figure 534246DEST_PATH_IMAGE117
，N为所述骑行轨迹线上的轨迹点的总数，M为所述近邻路段集合中的路段总数，构建轨迹线与路网之间的匹配概率网络T-R-N；101: Collect the track point sequence of the cycling track line of the bicycle
Figure 44359DEST_PATH_IMAGE116
and the set of nearby road segments formed by all road segments within the range of the distance threshold S of the cycling trajectory line in the road network
Figure 534246DEST_PATH_IMAGE117
, N is the total number of trajectory points on the riding trajectory line, M is the total number of road sections in the set of adjacent road sections, and a matching probability network TRN between the trajectory line and the road network is constructed;

102：根据所述轨迹点序列和所述近邻路段集合，构建从单轨迹点映射到路段的随机事件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件发生概率

Figure 517115DEST_PATH_IMAGE118
；102: Construct a random event probability model mapped from a single trajectory point to a road segment according to the trajectory point sequence and the set of adjacent road sections, and calculate the probability of occurrence of node events in the matching probability network TRN between the trajectory line and the road network
Figure 517115DEST_PATH_IMAGE118
;

103：根据所述轨迹点序列中的相邻轨迹点对，构建自行车前后轨迹点对映射到路段的条件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件之间的转移概率

Figure 4728DEST_PATH_IMAGE119
；103: According to the adjacent trajectory point pairs in the trajectory point sequence, construct a conditional probability model in which the front and rear trajectory point pairs of the bicycle are mapped to the road sections, and calculate the matching probability between the trajectory line and the road network. transition probability between
Figure 4728DEST_PATH_IMAGE119
;

104：在所述轨迹线与路网之间的匹配概率网络T-R-N上构建自行车轨迹与路段之间的马尔可夫链，根据所述节点事件发生概率

Figure 682834DEST_PATH_IMAGE120
和所述节点事件之间的转移概率
Figure 101046DEST_PATH_IMAGE121
，得到最大组合概率的自行车轨迹与路段之间的马尔可夫链，其所对应的路段序列
Figure 485891DEST_PATH_IMAGE122
为轨迹线的最优匹配结果。104: Construct a Markov chain between the bicycle trajectory and the road segment on the matching probability network TRN between the trajectory line and the road network, according to the occurrence probability of the node event
Figure 682834DEST_PATH_IMAGE120
and the transition probability between the node events
Figure 101046DEST_PATH_IMAGE121
, the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability is obtained, and the corresponding road segment sequence
Figure 485891DEST_PATH_IMAGE122
is the optimal matching result of the trajectory line.

本发明提供的上述针对有噪声且参数不明的自行车轨迹数据的路网匹配方法，充分利用共享单车运营平台的轨迹大数据，为城市慢行系统的交通管理应用提供数据基础。The above road network matching method for the bicycle trajectory data with noise and unknown parameters provided by the present invention makes full use of the trajectory big data of the shared bicycle operation platform, and provides a data basis for the traffic management application of the urban slow-moving system.

根据本发明的一个实施例，所述构建轨迹线与路网之间的匹配概率网络T-R-N为：采集自行车的骑行轨迹线的轨迹点序列

Figure 472301DEST_PATH_IMAGE123
和路网中由所述骑行轨迹线的距离阈值S范围内的所有路段形成的近邻路段集合
Figure 762337DEST_PATH_IMAGE124
。考虑到轨迹定位噪声较大且误差参数不明，通过单点角度提取轨迹点与路段空间映射的不确定性关系和前后点间关联角度提取骑行过程在路段上空间关联的不确定性关系，构建轨迹线与路网之间的匹配概率网络T-R-N。According to an embodiment of the present invention, the matching probability network TRN between the constructed trajectory line and the road network is: collecting the trajectory point sequence of the cycling trajectory line of the bicycle
Figure 472301DEST_PATH_IMAGE123
and the set of nearby road segments formed by all road segments within the range of the distance threshold S of the cycling trajectory line in the road network
Figure 762337DEST_PATH_IMAGE124
. Considering that the trajectory positioning noise is relatively large and the error parameters are unknown, the uncertainty relationship between the spatial mapping of the trajectory point and the road segment is extracted by the single point angle, and the uncertainty relationship of the spatial correlation of the riding process on the road segment is extracted by the correlation angle between the front and rear points. The matching probability network TRN between the trajectory line and the road network.

具体的，本实施例中将共享单车骑行轨迹匹配到路段上，任选数据中一条轨迹，如图2所示为编号836430的轨迹线及相应的空间位置，表示轨迹线匹配到路网上的实施过程：Specifically, in this embodiment, the shared bicycle riding track is matched to the road section, and one track in the data is selected. As shown in Figure 2, the track line numbered 836430 and the corresponding spatial position, indicating that the track line matches the track line on the road network. Implementation process:

距离阈值S取值为30m,检索轨迹内各轨迹点的邻近路段集合，其中部分轨迹点的近邻路段集合如表1所示：The distance threshold S is set to 30m, and the set of adjacent road segments of each track point in the track is retrieved, and the set of adjacent road segments of some track points is shown in Table 1:

Figure 531710DEST_PATH_IMAGE125
Figure 531710DEST_PATH_IMAGE125

表1Table 1

图3示意性表示自行车轨迹线与路网之间的匹配概率网络T-R-N图。FIG. 3 schematically shows a T-R-N diagram of the matching probability network between the bicycle trajectory and the road network.

根据本发明的一个实施例，所述构建轨迹线与路网之间的匹配概率网络T-R-N的具体步骤包括：根据所述轨迹点序列

Figure 98958DEST_PATH_IMAGE126
中的任一轨迹点
Figure 380903DEST_PATH_IMAGE127
，得到轨迹点i在所述近邻路段集合
Figure 33602DEST_PATH_IMAGE128
中路段
Figure 544348DEST_PATH_IMAGE016
的不确定性事件节点
Figure 153053DEST_PATH_IMAGE129
，同一轨迹点i的所有事件节点构成集合
Figure 481266DEST_PATH_IMAGE130
，得到所有的事件节点构成所述路网匹配概率网络T-R-N中的节点集合
Figure 231048DEST_PATH_IMAGE019
；According to an embodiment of the present invention, the specific step of constructing the matching probability network TRN between the trajectory line and the road network includes: according to the trajectory point sequence
Figure 98958DEST_PATH_IMAGE126
any track point in
Figure 380903DEST_PATH_IMAGE127
, get the trajectory point i in the set of adjacent road sections
Figure 33602DEST_PATH_IMAGE128
middle section
Figure 544348DEST_PATH_IMAGE016
The uncertainty event node of
Figure 153053DEST_PATH_IMAGE129
, all event nodes of the same trajectory point i constitute a set
Figure 481266DEST_PATH_IMAGE130
, get all the event nodes to form the node set in the road network matching probability network TRN
Figure 231048DEST_PATH_IMAGE019
;

根据所述轨迹点序列

Figure 857070DEST_PATH_IMAGE020
中的前后相邻轨迹点对
Figure 398910DEST_PATH_IMAGE131
，得到在任一轨迹点
Figure 507811DEST_PATH_IMAGE132
所属的任意路段
Figure 135102DEST_PATH_IMAGE023
的不确定性事件节点
Figure 33656DEST_PATH_IMAGE133
和轨迹点
Figure 570948DEST_PATH_IMAGE134
所属的任意路段
Figure 975385DEST_PATH_IMAGE026
的不确定性事件节点
Figure 214605DEST_PATH_IMAGE135
之间设置的有向连接弧
Figure 667583DEST_PATH_IMAGE136
，其代表事件之间的转移关系，所有的事件转移关系构成了所述轨迹线与路网之间的匹配概率网络T-R-N中的有向连接弧集合
Figure 918436DEST_PATH_IMAGE029
，构建所述轨迹线与路网之间的匹配概率网络T-R-N。According to the trajectory point sequence
Figure 857070DEST_PATH_IMAGE020
pair of adjacent trajectory points in
Figure 398910DEST_PATH_IMAGE131
, get at any trajectory point
Figure 507811DEST_PATH_IMAGE132
any road segment
Figure 135102DEST_PATH_IMAGE023
The uncertainty event node of
Figure 33656DEST_PATH_IMAGE133
and track points
Figure 570948DEST_PATH_IMAGE134
any road segment
Figure 975385DEST_PATH_IMAGE026
The uncertainty event node of
Figure 214605DEST_PATH_IMAGE135
Directed connection arcs set between
Figure 667583DEST_PATH_IMAGE136
, which represents the transition relationship between events, and all event transition relationships constitute the set of directed connection arcs in the matching probability network TRN between the trajectory line and the road network
Figure 918436DEST_PATH_IMAGE029
, and construct the matching probability network TRN between the trajectory line and the road network.

根据本发明的一个实施例，所述构建从单轨迹点映射到路段的随机事件概率模型为：对于所述轨迹线与路网之间的匹配概率网络T-R-N任一节点

Figure 883987DEST_PATH_IMAGE137
，在整体定位误差较大且参数未知条件下，通过利用轨迹点与近邻路段集合R中各个路段的垂直距离关系，构建基于横向距离分布的从单轨迹点映射到路段的随机事件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件发生的概率
Figure 157973DEST_PATH_IMAGE138
。According to an embodiment of the present invention, the construction of a random event probability model mapped from a single trajectory point to a road segment is: for any node of the matching probability network TRN between the trajectory line and the road network
Figure 883987DEST_PATH_IMAGE137
, under the condition that the overall positioning error is large and the parameters are unknown, by using the vertical distance relationship between the trajectory point and each road segment in the adjacent road segment set R, a random event probability model from a single trajectory point to a road segment based on the horizontal distance distribution is constructed to calculate The matching probability between the trajectory line and the road network is the probability of occurrence of node events in the network TRN
Figure 157973DEST_PATH_IMAGE138
.

根据各个轨迹点和近邻路段集中各个路段的坐标数据，得到任一轨迹点

Figure 742538DEST_PATH_IMAGE139
与所述近邻路段集合中任一路段
Figure 972531DEST_PATH_IMAGE140
的中心点的垂直距离
Figure 984350DEST_PATH_IMAGE141
，得到所述轨迹点
Figure 480053DEST_PATH_IMAGE142
的所有近邻路段中最远的近邻路段的垂直距离
Figure 992943DEST_PATH_IMAGE143
，计算得到任一近邻路段距离
Figure 952809DEST_PATH_IMAGE144
与所述最远的近邻路段的垂直距离
Figure 10894DEST_PATH_IMAGE145
的差值
Figure 508741DEST_PATH_IMAGE146
：According to each trajectory point and the coordinate data of each road section in the set of adjacent road sections, any trajectory point is obtained
Figure 742538DEST_PATH_IMAGE139
with any road segment in the set of adjacent road segments
Figure 972531DEST_PATH_IMAGE140
The vertical distance of the center point of
Figure 984350DEST_PATH_IMAGE141
, get the trajectory point
Figure 480053DEST_PATH_IMAGE142
The vertical distance of the farthest neighbor of all neighbors
Figure 992943DEST_PATH_IMAGE143
, calculate the distance of any nearby road segment
Figure 952809DEST_PATH_IMAGE144
the vertical distance from the farthest neighbor
Figure 10894DEST_PATH_IMAGE145
difference
Figure 508741DEST_PATH_IMAGE146
:

Figure 435108DEST_PATH_IMAGE147
；
Figure 435108DEST_PATH_IMAGE147
;

其中，

Figure 124847DEST_PATH_IMAGE148
表示表示任一轨迹点
Figure 665419DEST_PATH_IMAGE149
与所述近邻路段集合中路段
Figure 463610DEST_PATH_IMAGE150
的中心点的垂直距离，
Figure 537877DEST_PATH_IMAGE151
表示轨迹点
Figure 659285DEST_PATH_IMAGE152
的所有近邻路段中最远的近邻路段的垂直距离，
Figure 183807DEST_PATH_IMAGE153
表示
Figure 79082DEST_PATH_IMAGE154
与所述最远的近邻路段的垂直距离
Figure 557641DEST_PATH_IMAGE155
的差值，
Figure 18709DEST_PATH_IMAGE047
表示第
Figure 448553DEST_PATH_IMAGE048
个轨迹点，
Figure 345971DEST_PATH_IMAGE049
表示第
Figure 89936DEST_PATH_IMAGE049
个路段；in,
Figure 124847DEST_PATH_IMAGE148
Represents any trajectory point
Figure 665419DEST_PATH_IMAGE149
The road segment in the set with the neighbor road segment
Figure 463610DEST_PATH_IMAGE150
The vertical distance from the center point of ,
Figure 537877DEST_PATH_IMAGE151
Represents a track point
Figure 659285DEST_PATH_IMAGE152
The vertical distance of the farthest adjacent road segment among all the adjacent road segments of ,
Figure 183807DEST_PATH_IMAGE153
express
Figure 79082DEST_PATH_IMAGE154
the vertical distance from the farthest neighbor
Figure 557641DEST_PATH_IMAGE155
difference of ,
Figure 18709DEST_PATH_IMAGE047
means the first
Figure 448553DEST_PATH_IMAGE048
track points,
Figure 345971DEST_PATH_IMAGE049
means the first
Figure 89936DEST_PATH_IMAGE049
a road segment;

根据所述轨迹点

Figure 998986DEST_PATH_IMAGE156
与各路段垂直距离的定位偏差是独立同分布随机变量，得到轨迹点
Figure 989945DEST_PATH_IMAGE157
在所述近邻路段集合中的路段
Figure 984446DEST_PATH_IMAGE158
的所述节点事件发生概率
Figure 532102DEST_PATH_IMAGE159
与该轨迹点距所述路段
Figure 420292DEST_PATH_IMAGE160
的垂直距离相关，
Figure 457518DEST_PATH_IMAGE161
越小，则所述节点事件发生概率
Figure 876998DEST_PATH_IMAGE162
越大，得到轨迹点
Figure 415296DEST_PATH_IMAGE163
在所述近邻路段集合中的其他各个路段k的所述
Figure 33359DEST_PATH_IMAGE164
相关，采用公式计算
Figure 116853DEST_PATH_IMAGE165
：According to the trajectory point
Figure 998986DEST_PATH_IMAGE156
The positioning deviation of the vertical distance from each road section is an independent and identically distributed random variable, and the trajectory point is obtained.
Figure 989945DEST_PATH_IMAGE157
road segments in the set of neighbor road segments
Figure 984446DEST_PATH_IMAGE158
The node event occurrence probability of
Figure 532102DEST_PATH_IMAGE159
Distance from the track point to the road segment
Figure 420292DEST_PATH_IMAGE160
is related to the vertical distance,
Figure 457518DEST_PATH_IMAGE161
The smaller the probability of occurrence of the node event
Figure 876998DEST_PATH_IMAGE162
bigger, get track points
Figure 415296DEST_PATH_IMAGE163
In the set of neighboring road segments, the other respective road segments k
Figure 33359DEST_PATH_IMAGE164
Correlation, calculated by formula
Figure 116853DEST_PATH_IMAGE165
:

Figure 7317DEST_PATH_IMAGE166
；
Figure 7317DEST_PATH_IMAGE166
;

其中，

Figure 224672DEST_PATH_IMAGE167
表示节点事件发生的概率，
Figure 572608DEST_PATH_IMAGE168
表示轨迹点
Figure 138587DEST_PATH_IMAGE169
的所有近邻路段中最远的近邻路段的垂直距离，
Figure 594976DEST_PATH_IMAGE170
表示任一轨迹点
Figure 960230DEST_PATH_IMAGE171
与所述近邻路段集合中任一路段
Figure 818464DEST_PATH_IMAGE172
的中心点的垂直距离，
Figure 555345DEST_PATH_IMAGE173
表示任一轨迹点
Figure 108817DEST_PATH_IMAGE174
与所述近邻路段几何中其他路段k的中心点的垂直距离，
Figure 402395DEST_PATH_IMAGE047
表示第
Figure 36508DEST_PATH_IMAGE069
个轨迹点，
Figure 898285DEST_PATH_IMAGE175
表示第
Figure 63687DEST_PATH_IMAGE176
个路段，
Figure 285590DEST_PATH_IMAGE072
表示第
Figure 524941DEST_PATH_IMAGE177
个路段。in,
Figure 224672DEST_PATH_IMAGE167
represents the probability of a node event occurring,
Figure 572608DEST_PATH_IMAGE168
Represents a track point
Figure 138587DEST_PATH_IMAGE169
The vertical distance of the farthest adjacent road segment among all the adjacent road segments of ,
Figure 594976DEST_PATH_IMAGE170
represents any track point
Figure 960230DEST_PATH_IMAGE171
with any road segment in the set of adjacent road segments
Figure 818464DEST_PATH_IMAGE172
The vertical distance from the center point of ,
Figure 555345DEST_PATH_IMAGE173
represents any track point
Figure 108817DEST_PATH_IMAGE174
the vertical distance from the center point of other road segments k in the neighbor road segment geometry,
Figure 402395DEST_PATH_IMAGE047
means the first
Figure 36508DEST_PATH_IMAGE069
track points,
Figure 898285DEST_PATH_IMAGE175
means the first
Figure 63687DEST_PATH_IMAGE176
road section,
Figure 285590DEST_PATH_IMAGE072
means the first
Figure 524941DEST_PATH_IMAGE177
road section.

具体的，如表2所示统计各轨迹点与近邻路段集合R中各个路段的垂直距离：Specifically, as shown in Table 2, the vertical distance between each track point and each road segment in the set R of adjacent road segments is counted:

Figure 416674DEST_PATH_IMAGE178
Figure 416674DEST_PATH_IMAGE178

表2Table 2

根据该距离计算轨迹点属于各路段的概率

Figure 194006DEST_PATH_IMAGE179
，计算结果如表3所示：Calculate the probability that the track point belongs to each road segment according to the distance
Figure 194006DEST_PATH_IMAGE179
, the calculation results are shown in Table 3:

Figure 767070DEST_PATH_IMAGE180
Figure 767070DEST_PATH_IMAGE180

表3table 3

根据本发明的一个实施例，所述构建前后轨迹点对映射到路段的条件概率模型为：对所述轨迹线与路网之间的匹配概率网络T-R-N上的任一连弧

Figure 188824DEST_PATH_IMAGE181
，在整体误差较大且参数未知的条件下，通过利用所述相邻轨迹点对所在路段间的空间关联关系，构建所述前后轨迹点对的路段映射条件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件之间的转移概率
Figure 641671DEST_PATH_IMAGE182
。According to an embodiment of the present invention, the conditional probability model for mapping the front and rear track point pairs to road segments is: for any connecting arc on the matching probability network TRN between the trajectory line and the road network
Figure 188824DEST_PATH_IMAGE181
, under the condition that the overall error is large and the parameters are unknown, by using the spatial correlation between the road segments where the adjacent trajectory point pairs are located, the road segment mapping conditional probability model of the front and rear trajectory point pairs is constructed, and the trajectory line and the road segment are calculated. Matching probability between road networks Transition probability between node events in network TRN
Figure 641671DEST_PATH_IMAGE182
.

通过利用路网数据中各个路段间的空间关联关系，定义得到所述近邻路段集合中任意两条路段

Figure 453769DEST_PATH_IMAGE183
与
Figure 892840DEST_PATH_IMAGE184
之间的转移阻抗
Figure 293735DEST_PATH_IMAGE185
，其取值区间为
Figure 792849DEST_PATH_IMAGE078
，设定路网上路段
Figure 92243DEST_PATH_IMAGE079
与路段
Figure 459640DEST_PATH_IMAGE073
之间的最短距离长度为
Figure 855986DEST_PATH_IMAGE186
，
Figure 401368DEST_PATH_IMAGE187
的赋值公式如下：By using the spatial relationship between each road segment in the road network data, any two road segments in the set of adjacent road segments are defined and obtained
Figure 453769DEST_PATH_IMAGE183
and
Figure 892840DEST_PATH_IMAGE184
transfer impedance between
Figure 293735DEST_PATH_IMAGE185
, whose value range is
Figure 792849DEST_PATH_IMAGE078
, set the road network segment
Figure 92243DEST_PATH_IMAGE079
with road sections
Figure 459640DEST_PATH_IMAGE073
The shortest distance between
Figure 855986DEST_PATH_IMAGE186
,
Figure 401368DEST_PATH_IMAGE187
The assignment formula is as follows:

Figure 171747DEST_PATH_IMAGE188
；
Figure 171747DEST_PATH_IMAGE188
;

其中，

Figure 218200DEST_PATH_IMAGE189
表示任意两条路段
Figure 344419DEST_PATH_IMAGE190
与
Figure 106708DEST_PATH_IMAGE191
之间的转移阻抗，
Figure 443011DEST_PATH_IMAGE086
表示第
Figure 637363DEST_PATH_IMAGE086
个路段，
Figure 8302DEST_PATH_IMAGE073
表示第
Figure 941491DEST_PATH_IMAGE073
个路段，
Figure 640457DEST_PATH_IMAGE087
表示路网上路段
Figure 763134DEST_PATH_IMAGE086
与路段
Figure 175530DEST_PATH_IMAGE073
之间的最短距离；in,
Figure 218200DEST_PATH_IMAGE189
represents any two road segments
Figure 344419DEST_PATH_IMAGE190
and
Figure 106708DEST_PATH_IMAGE191
transfer impedance between,
Figure 443011DEST_PATH_IMAGE086
means the first
Figure 637363DEST_PATH_IMAGE086
road section,
Figure 8302DEST_PATH_IMAGE073
means the first
Figure 941491DEST_PATH_IMAGE073
road section,
Figure 640457DEST_PATH_IMAGE087
Represents a road network segment
Figure 763134DEST_PATH_IMAGE086
with road sections
Figure 175530DEST_PATH_IMAGE073
the shortest distance between;

当轨迹点

Figure 233616DEST_PATH_IMAGE192
在路段
Figure 278932DEST_PATH_IMAGE193
上时，后一个轨迹点
Figure 329933DEST_PATH_IMAGE194
在路段
Figure 347568DEST_PATH_IMAGE195
上的概率为所述节点事件之间的转移概率
Figure 435610DEST_PATH_IMAGE196
，得到所述节点事件之间的转移概率与路段间的转移阻抗
Figure 358435DEST_PATH_IMAGE197
相关，所述转移阻抗越小则所述节点事件之间的转移概率越大，用下述公式进行计算：when the track point
Figure 233616DEST_PATH_IMAGE192
on the road
Figure 278932DEST_PATH_IMAGE193
up, the next track point
Figure 329933DEST_PATH_IMAGE194
on the road
Figure 347568DEST_PATH_IMAGE195
The probability on is the transition probability between the node events
Figure 435610DEST_PATH_IMAGE196
, obtain the transition probability between the node events and the transition impedance between the road segments
Figure 358435DEST_PATH_IMAGE197
Correlation, the smaller the transition impedance, the greater the transition probability between the node events, which is calculated by the following formula:

Figure 760598DEST_PATH_IMAGE198
；
Figure 760598DEST_PATH_IMAGE198
;

其中，

Figure 695056DEST_PATH_IMAGE199
表示节点事件之间的转移概率，
Figure 344212DEST_PATH_IMAGE200
表示路段间的转移阻抗，
Figure 301803DEST_PATH_IMAGE201
表示求和时第
Figure 569974DEST_PATH_IMAGE202
个转移阻抗数据。in,
Figure 695056DEST_PATH_IMAGE199
represents the transition probability between node events,
Figure 344212DEST_PATH_IMAGE200
represents the transfer impedance between road segments,
Figure 301803DEST_PATH_IMAGE201
Indicates the time of summation
Figure 569974DEST_PATH_IMAGE202
transfer impedance data.

具体的，利用路段之间的空间关联关系定义路段之间的转移阻抗，阻抗矩阵如表4所示：Specifically, the transfer impedance between the road segments is defined by the spatial relationship between the road segments, and the impedance matrix is shown in Table 4:

Figure 749151DEST_PATH_IMAGE203
Figure 749151DEST_PATH_IMAGE203

表4Table 4

根据阻抗计算得到相邻前后轨迹点之间的转移概率

Figure 116679DEST_PATH_IMAGE204
。Calculate the transition probability between adjacent track points before and after the impedance
Figure 116679DEST_PATH_IMAGE204
.

图4示意性表示自行车轨迹线的最优匹配路段示意图。FIG. 4 schematically shows a schematic diagram of an optimal matching road segment for a bicycle trajectory line.

根据本发明的一个实施例，所述构建前后轨迹点对映射到路段的条件概率模型为：在所述轨迹线与路网之间的匹配概率网络T-R-N上，对每种匹配方案构建所述自行车轨迹与路段之间的马尔可夫链，根据所述节点事件发生概率

Figure 889463DEST_PATH_IMAGE205
和所述节点事件之间的转移概率
Figure 820378DEST_PATH_IMAGE206
，通过网络路径搜索算法搜索出所述最大组合概率的自行车轨迹与路段之间的马尔可夫链，其所对应的路段序列
Figure 729428DEST_PATH_IMAGE207
为轨迹线的最优匹配结果。According to an embodiment of the present invention, the conditional probability model for mapping the front and rear track point pairs to road sections is: constructing the bicycle for each matching scheme on the matching probability network TRN between the trajectory line and the road network Markov chain between trajectories and road segments, according to the probability of occurrence of the node event
Figure 889463DEST_PATH_IMAGE205
and the transition probability between the node events
Figure 820378DEST_PATH_IMAGE206
, the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability is searched through the network path search algorithm, and the corresponding road segment sequence
Figure 729428DEST_PATH_IMAGE207
is the optimal matching result of the trajectory line.

通过设置轨迹线与路网之间的匹配概率网络T-R-N中的网络节点集合

Figure 533436DEST_PATH_IMAGE208
中的每一个节点为初始节点，网络节点集合
Figure 386992DEST_PATH_IMAGE209
中的任一节点为终到节点，利用所述轨迹线与路网之间的匹配概率网络T-R-N中的有向连接弧，遍历整个网络，所述起点节点到所述终到节点间的任意一条节点序列对应一项自行车轨迹线匹配方案，构建自行车轨迹与路段之间的马尔可夫链。The set of network nodes in the network TRN by setting the matching probability between the trajectory line and the road network
Figure 533436DEST_PATH_IMAGE208
Each node in is an initial node, a collection of network nodes
Figure 386992DEST_PATH_IMAGE209
Any node is the final node, and the directed connection arc in the matching probability network TRN between the trajectory line and the road network is used to traverse the entire network, and any one between the starting node and the final node is used. The node sequence corresponds to a bicycle trajectory line matching scheme, and a Markov chain between bicycle trajectory and road segment is constructed.

所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件发生概率

Figure 262544DEST_PATH_IMAGE210
和所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件之间的转移概率
Figure 698204DEST_PATH_IMAGE211
通过公式计算得到自行车轨迹与路段之间的马尔可夫链的组合概率
Figure 125644DEST_PATH_IMAGE212
，得到最大组合概率的自行车轨迹与路段之间的马尔可夫链，The matching probability between the trajectory line and the road network The node event occurrence probability in the network TRN
Figure 262544DEST_PATH_IMAGE210
and the matching probability between the trajectory line and the road network and the transition probability between node events in the network TRN
Figure 698204DEST_PATH_IMAGE211
The combined probability of the Markov chain between the bicycle track and the road segment is calculated by the formula
Figure 125644DEST_PATH_IMAGE212
, the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability is obtained,

公式：

Figure 341861DEST_PATH_IMAGE213
；formula:
Figure 341861DEST_PATH_IMAGE213
;

其中，

Figure 630891DEST_PATH_IMAGE214
表示节点事件发生概率，
Figure 435905DEST_PATH_IMAGE215
表示节点事件之间的转移概率，
Figure 644033DEST_PATH_IMAGE216
表示自行车轨迹的路段马尔可夫连的组合概率；in,
Figure 630891DEST_PATH_IMAGE214
represents the probability of node event occurrence,
Figure 435905DEST_PATH_IMAGE215
represents the transition probability between node events,
Figure 644033DEST_PATH_IMAGE216
the combined probability of the Markov connection representing the segment of the bicycle trajectory;

根据所述轨迹线的路段匹配概率网络T-R-N，通过网络路径搜索算法搜索出所述最大组合概率的自行车轨迹与路段之间的马尔可夫链，其所对应的路段序列

Figure 488492DEST_PATH_IMAGE217
为轨迹线的最优匹配结果。According to the road segment matching probability network TRN of the trajectory line, the Markov chain between the bicycle trajectory with the maximum combined probability and the road segment is searched through the network path search algorithm, and the corresponding road segment sequence
Figure 488492DEST_PATH_IMAGE217
is the optimal matching result of the trajectory line.

具体的，如表5所示最优匹配路段结果，图4示意性表示自行车轨迹线的最优匹配路段示意图：Specifically, as shown in Table 5, the results of the optimal matching road section are shown in Table 5, and FIG. 4 schematically shows the schematic diagram of the optimal matching road section of the bicycle trajectory:

Figure 627218DEST_PATH_IMAGE218
Figure 627218DEST_PATH_IMAGE218

表5table 5

本实施例的有益效果在于：The beneficial effects of this embodiment are:

该针对有噪声且参数不明的自行车轨迹数据的路网匹配方法既考虑了较大噪声条件下轨迹点与路段在空间上的映射概率，又考虑了骑行过程中多轨迹点在路段上的空间关联概率，并采用基于概率网络的搜索算法识别出最大可能性的路段序列，能够在骑行者手机定位数据噪声较大、定位误差参数不明确且无法进行轨迹数据纠偏的现实条件下，实现自行车轨迹与路网的最优匹配。The road network matching method for noisy bicycle trajectory data with unknown parameters not only considers the spatial mapping probability of trajectory points and road segments under the condition of large noise, but also considers the spatial mapping of multiple trajectory points on road segments during cycling. Correlation probability, and use the search algorithm based on probability network to identify the most likely road segment sequence, which can realize the bicycle trajectory under the realistic conditions that the cyclist’s mobile phone positioning data is noisy, the positioning error parameters are unclear, and the trajectory data cannot be corrected. The best match with the road network.

图5示意性表示根据本发明的针对较大噪声且参数不明的自行车轨迹数据的路网匹配系统图。FIG. 5 schematically shows a diagram of a road network matching system according to the present invention for bicycle trajectory data with relatively large noise and unknown parameters.

不仅如此，为实现上述发明目的，本发明还提供一种针对较大噪声且参数不明的自行车轨迹数据的路网匹配系统，该系统包括：Not only that, in order to achieve the above purpose of the invention, the present invention also provides a road network matching system for bicycle trajectory data with relatively large noise and unknown parameters, the system comprising:

路网网络构建模块，采集自行车的骑行轨迹线的轨迹点序列

Figure 99788DEST_PATH_IMAGE219
和路网中由所述骑行轨迹线的距离阈值S范围内的所有路段形成的近邻路段集合
Figure 619762DEST_PATH_IMAGE220
，N为所述骑行轨迹线上的轨迹点的总数，M为所述近邻路段集合中的路段总数，构建轨迹线与路网之间的匹配概率网络T-R-N；The road network network building module collects the trajectory point sequence of the bicycle's riding trajectory
Figure 99788DEST_PATH_IMAGE219
and the set of nearby road segments formed by all road segments within the range of the distance threshold S of the cycling trajectory line in the road network
Figure 619762DEST_PATH_IMAGE220
, N is the total number of trajectory points on the riding trajectory line, M is the total number of road sections in the set of adjacent road sections, and a matching probability network TRN between the trajectory line and the road network is constructed;

单轨迹点模型构建模块，根据所述轨迹点序列和所述近邻路段集合，构建从单轨迹点映射到路段的随机事件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件发生概率

Figure 997522DEST_PATH_IMAGE221
；A single trajectory point model building module, according to the trajectory point sequence and the set of adjacent road segments, constructs a random event probability model mapped from a single trajectory point to a road segment, and calculates the matching probability between the trajectory line and the road network in the network TRN The probability of node event occurrence
Figure 997522DEST_PATH_IMAGE221
;

前后轨迹点模型构建模块，根据所述轨迹点序列中的相邻轨迹点对，构建自行车前后轨迹点对映射到路段的条件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件之间的转移概率

Figure 487410DEST_PATH_IMAGE222
；The front and rear trajectory point model building module, according to the adjacent trajectory point pairs in the trajectory point sequence, constructs the conditional probability model of the bicycle front and rear trajectory point pairs mapped to the road section, and calculates the matching probability network TRN between the trajectory line and the road network The transition probabilities between node events in
Figure 487410DEST_PATH_IMAGE222
;

轨迹线匹配模块，在所述轨迹线与路网之间的匹配概率网络T-R-N上构建自行车轨迹与路段之间的马尔可夫链，根据所述节点事件发生概率

Figure 17748DEST_PATH_IMAGE223
和所述节点事件之间的转移概率
Figure 426733DEST_PATH_IMAGE224
，得到最大组合概率的自行车轨迹与路段之间的马尔可夫链，其所对应的路段序列
Figure 42522DEST_PATH_IMAGE225
为轨迹线的最优匹配结果。The trajectory line matching module constructs a Markov chain between the bicycle trajectory and the road segment on the matching probability network TRN between the trajectory line and the road network, according to the probability of occurrence of the node event
Figure 17748DEST_PATH_IMAGE223
and the transition probability between the node events
Figure 426733DEST_PATH_IMAGE224
, the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability is obtained, and the corresponding road segment sequence
Figure 42522DEST_PATH_IMAGE225
is the optimal matching result of the trajectory line.

根据本发明的一个实施例，路网网络构建模块是构建轨迹线与路网之间的匹配概率网络T-R-N,采集自行车的骑行轨迹线的轨迹点序列

Figure 601679DEST_PATH_IMAGE226
和路网中由所述骑行轨迹线的距离阈值S范围内的所有路段形成的近邻路段集合
Figure 173475DEST_PATH_IMAGE227
。考虑到轨迹定位噪声较大且误差参数不明，通过单点角度提取轨迹点与路段空间映射的不确定性关系和前后点间关联角度提取骑行过程在路段上空间关联的不确定性关系，构建轨迹线与路网之间的匹配概率网络T-R-N。According to an embodiment of the present invention, the road network network building module is to construct a matching probability network TRN between the trajectory line and the road network, and collect the trajectory point sequence of the cycling trajectory line of the bicycle.
Figure 601679DEST_PATH_IMAGE226
and the set of nearby road segments formed by all road segments within the range of the distance threshold S of the cycling trajectory line in the road network
Figure 173475DEST_PATH_IMAGE227
. Considering that the trajectory positioning noise is relatively large and the error parameters are unknown, the uncertainty relationship between the spatial mapping of the trajectory point and the road segment is extracted by the single point angle, and the uncertainty relationship of the spatial correlation of the riding process on the road segment is extracted by the correlation angle between the front and rear points. The matching probability network TRN between the trajectory line and the road network.

具体的，本实施例中将共享单车骑行轨迹匹配到路段上，任选数据中一条轨迹，如图2所示为编号836430的轨迹线及相应的空间位置，表示轨迹线匹配到路网上的实施过程：Specifically, in this embodiment, the shared bicycle riding track is matched to the road section, and one track in the data is selected. As shown in Figure 2, the track line numbered 836430 and the corresponding spatial position, indicating that the track line matches the track line on the road network. Implementation process:

距离阈值S取值为30m,检索轨迹内各轨迹点的邻近路段集合，其中部分轨迹点的近邻路段集合如表6所示：The distance threshold S is set to 30m, and the set of adjacent road segments of each track point in the track is retrieved, and the set of adjacent road segments of some track points is shown in Table 6:

Figure 831989DEST_PATH_IMAGE228
Figure 831989DEST_PATH_IMAGE228

表6Table 6

图3示意性表示自行车轨迹线与路网之间的匹配概率网络T-R-N图。FIG. 3 schematically shows a T-R-N diagram of the matching probability network between the bicycle trajectory and the road network.

根据本发明的一个实施例，单轨迹点模型构建模块是构建从单轨迹点映射到路段的随机事件概率模型，并计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件发生概率

Figure 731812DEST_PATH_IMAGE229
。对于所述轨迹线与路网之间的匹配概率网络T-R-N任一节点
Figure 750453DEST_PATH_IMAGE230
，在整体定位误差较大且参数未知条件下，通过利用轨迹点与近邻路段集合R中各个路段的垂直距离关系，构建基于横向距离分布的从单轨迹点映射到路段的随机事件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件发生的概率
Figure 193066DEST_PATH_IMAGE231
。According to an embodiment of the present invention, the single trajectory point model building module is to construct a random event probability model mapped from a single trajectory point to a road segment, and calculate the matching probability between the trajectory line and the road network. The node event occurrence in the network TRN probability
Figure 731812DEST_PATH_IMAGE229
. For any node of the matching probability network TRN between the trajectory line and the road network
Figure 750453DEST_PATH_IMAGE230
, under the condition that the overall positioning error is large and the parameters are unknown, by using the vertical distance relationship between the trajectory point and each road segment in the adjacent road segment set R, a random event probability model from a single trajectory point to a road segment based on the horizontal distance distribution is constructed to calculate The matching probability between the trajectory line and the road network is the probability of occurrence of node events in the network TRN
Figure 193066DEST_PATH_IMAGE231
.

根据各个轨迹点和近邻路段集中各个路段的坐标数据，得到任一轨迹点

Figure 350378DEST_PATH_IMAGE232
与所述近邻路段集合中任一路段
Figure 924448DEST_PATH_IMAGE233
的中心点的垂直距离
Figure 435195DEST_PATH_IMAGE234
，得到所述轨迹点
Figure 122528DEST_PATH_IMAGE235
的所有近邻路段中最远的近邻路段的垂直距离
Figure 395550DEST_PATH_IMAGE236
，计算得到任一近邻路段距离
Figure 145331DEST_PATH_IMAGE237
与所述最远的近邻路段的垂直距离
Figure 584403DEST_PATH_IMAGE238
的差值
Figure 47614DEST_PATH_IMAGE239
：According to each trajectory point and the coordinate data of each road section in the set of adjacent road sections, any trajectory point is obtained
Figure 350378DEST_PATH_IMAGE232
with any road segment in the set of adjacent road segments
Figure 924448DEST_PATH_IMAGE233
The vertical distance of the center point of
Figure 435195DEST_PATH_IMAGE234
, get the trajectory point
Figure 122528DEST_PATH_IMAGE235
The vertical distance of the farthest neighbor of all neighbors
Figure 395550DEST_PATH_IMAGE236
, calculate the distance of any nearby road segment
Figure 145331DEST_PATH_IMAGE237
the vertical distance from the farthest neighbor
Figure 584403DEST_PATH_IMAGE238
difference
Figure 47614DEST_PATH_IMAGE239
:

Figure 156516DEST_PATH_IMAGE240
；
Figure 156516DEST_PATH_IMAGE240
;

其中，

Figure 705178DEST_PATH_IMAGE241
表示表示任一轨迹点
Figure 682361DEST_PATH_IMAGE242
与所述近邻路段集合中路段
Figure 750811DEST_PATH_IMAGE243
的中心点的垂直距离，
Figure 279881DEST_PATH_IMAGE244
表示轨迹点
Figure 66572DEST_PATH_IMAGE245
的所有近邻路段中最远的近邻路段的垂直距离，
Figure 113025DEST_PATH_IMAGE246
表示
Figure 222933DEST_PATH_IMAGE247
与所述最远的近邻路段的垂直距离
Figure 735953DEST_PATH_IMAGE248
的差值，
Figure 337836DEST_PATH_IMAGE249
表示第
Figure 781456DEST_PATH_IMAGE250
个轨迹点，
Figure 824498DEST_PATH_IMAGE251
表示第
Figure 836316DEST_PATH_IMAGE251
个路段；in,
Figure 705178DEST_PATH_IMAGE241
Represents any trajectory point
Figure 682361DEST_PATH_IMAGE242
The road segment in the set with the neighbor road segment
Figure 750811DEST_PATH_IMAGE243
The vertical distance from the center point of ,
Figure 279881DEST_PATH_IMAGE244
Represents a track point
Figure 66572DEST_PATH_IMAGE245
The vertical distance of the farthest adjacent road segment among all the adjacent road segments of ,
Figure 113025DEST_PATH_IMAGE246
express
Figure 222933DEST_PATH_IMAGE247
the vertical distance from the farthest neighbor
Figure 735953DEST_PATH_IMAGE248
difference of ,
Figure 337836DEST_PATH_IMAGE249
means the first
Figure 781456DEST_PATH_IMAGE250
track points,
Figure 824498DEST_PATH_IMAGE251
means the first
Figure 836316DEST_PATH_IMAGE251
a road segment;

根据所述轨迹点

Figure 784550DEST_PATH_IMAGE252
与各路段垂直距离的定位偏差是独立同分布随机变量，得到轨迹点
Figure 907227DEST_PATH_IMAGE253
在所述近邻路段集合中的路段
Figure 804775DEST_PATH_IMAGE254
的所述节点事件发生概率
Figure 377708DEST_PATH_IMAGE255
与该轨迹点距所述路段
Figure 423024DEST_PATH_IMAGE256
的垂直距离相关，
Figure 21496DEST_PATH_IMAGE257
越小，则所述节点事件发生概率
Figure 491661DEST_PATH_IMAGE258
越大，得到轨迹点
Figure 579702DEST_PATH_IMAGE259
在所述近邻路段集合中的其他各个路段k的所述
Figure 253260DEST_PATH_IMAGE260
相关，采用公式计算
Figure 904690DEST_PATH_IMAGE261
：According to the trajectory point
Figure 784550DEST_PATH_IMAGE252
The positioning deviation of the vertical distance from each road section is an independent and identically distributed random variable, and the trajectory point is obtained.
Figure 907227DEST_PATH_IMAGE253
road segments in the set of neighbor road segments
Figure 804775DEST_PATH_IMAGE254
The node event occurrence probability of
Figure 377708DEST_PATH_IMAGE255
Distance from the track point to the road segment
Figure 423024DEST_PATH_IMAGE256
is related to the vertical distance,
Figure 21496DEST_PATH_IMAGE257
The smaller the probability of occurrence of the node event
Figure 491661DEST_PATH_IMAGE258
bigger, get track points
Figure 579702DEST_PATH_IMAGE259
In the set of neighboring road segments, the other respective road segments k
Figure 253260DEST_PATH_IMAGE260
Correlation, calculated by formula
Figure 904690DEST_PATH_IMAGE261
:

Figure 104727DEST_PATH_IMAGE262
；
Figure 104727DEST_PATH_IMAGE262
;

其中，

Figure 239037DEST_PATH_IMAGE263
表示节点事件发生的概率，
Figure 524525DEST_PATH_IMAGE264
表示轨迹点
Figure 714066DEST_PATH_IMAGE265
的所有近邻路段中最远的近邻路段的垂直距离，
Figure 378397DEST_PATH_IMAGE266
表示任一轨迹点
Figure 260771DEST_PATH_IMAGE267
与所述近邻路段集合中任一路段
Figure 33555DEST_PATH_IMAGE268
的中心点的垂直距离，
Figure 449624DEST_PATH_IMAGE173
表示任一轨迹点
Figure 545625DEST_PATH_IMAGE269
与所述近邻路段几何中其他路段k的中心点的垂直距离，
Figure 411950DEST_PATH_IMAGE249
表示第
Figure 547396DEST_PATH_IMAGE270
个轨迹点，
Figure 891790DEST_PATH_IMAGE271
表示第
Figure 45559DEST_PATH_IMAGE272
个路段，
Figure 20469DEST_PATH_IMAGE273
表示第
Figure 236686DEST_PATH_IMAGE274
个路段。in,
Figure 239037DEST_PATH_IMAGE263
represents the probability of a node event occurring,
Figure 524525DEST_PATH_IMAGE264
Represents a track point
Figure 714066DEST_PATH_IMAGE265
The vertical distance of the farthest adjacent road segment among all the adjacent road segments of ,
Figure 378397DEST_PATH_IMAGE266
represents any track point
Figure 260771DEST_PATH_IMAGE267
with any road segment in the set of adjacent road segments
Figure 33555DEST_PATH_IMAGE268
The vertical distance from the center point of ,
Figure 449624DEST_PATH_IMAGE173
represents any track point
Figure 545625DEST_PATH_IMAGE269
the vertical distance from the center point of other road segments k in the neighbor road segment geometry,
Figure 411950DEST_PATH_IMAGE249
means the first
Figure 547396DEST_PATH_IMAGE270
track points,
Figure 891790DEST_PATH_IMAGE271
means the first
Figure 45559DEST_PATH_IMAGE272
road section,
Figure 20469DEST_PATH_IMAGE273
means the first
Figure 236686DEST_PATH_IMAGE274
road section.

具体的，如表7所示统计各轨迹点与近邻路段集合R中各个路段的垂直距离：Specifically, as shown in Table 7, the vertical distance between each track point and each road segment in the set R of adjacent road segments is counted:

Figure 774984DEST_PATH_IMAGE275
Figure 774984DEST_PATH_IMAGE275

表7Table 7

根据该距离计算轨迹点属于各路段的概率

Figure 330730DEST_PATH_IMAGE276
，计算结果如表8所示：Calculate the probability that the track point belongs to each road segment according to the distance
Figure 330730DEST_PATH_IMAGE276
, the calculation results are shown in Table 8:

Figure 804437DEST_PATH_IMAGE277
Figure 804437DEST_PATH_IMAGE277

表8Table 8

根据本发明的一个实施例，前后轨迹点模型构建模块是构建自行车前后轨迹点对映射到路段的条件概率模型，并计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件之间的转移概率

Figure 632584DEST_PATH_IMAGE278
。According to an embodiment of the present invention, the front and rear trajectory point model building module is to construct a conditional probability model in which the front and rear trajectory point pairs of the bicycle are mapped to the road sections, and calculate the matching probability between the trajectory line and the road network between the node events in the network TRN transition probability between
Figure 632584DEST_PATH_IMAGE278
.

所述构建前后轨迹点对映射到路段的条件概率模型为：对所述轨迹线与路网之间的匹配概率网络T-R-N上的任一连弧

Figure 584360DEST_PATH_IMAGE279
，在整体误差较大且参数未知的条件下，通过利用所述相邻轨迹点对所在路段间的空间关联关系，构建所述前后轨迹点对的路段映射条件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件之间的转移概率
Figure 260192DEST_PATH_IMAGE280
。The conditional probability model in which the trajectory point pairs before and after construction are mapped to the road segment is: for any connecting arc on the matching probability network TRN between the trajectory line and the road network
Figure 584360DEST_PATH_IMAGE279
, under the condition that the overall error is large and the parameters are unknown, by using the spatial correlation between the road segments where the adjacent trajectory point pairs are located, the road segment mapping conditional probability model of the front and rear trajectory point pairs is constructed, and the trajectory line and the road segment are calculated. Matching probability between road networks Transition probability between node events in network TRN
Figure 260192DEST_PATH_IMAGE280
.

通过利用路网数据中各个路段间的空间关联关系，定义得到所述近邻路段集合中任意两条路段

Figure 560592DEST_PATH_IMAGE281
与
Figure 626768DEST_PATH_IMAGE282
之间的转移阻抗
Figure 382235DEST_PATH_IMAGE283
，其取值区间为
Figure 896261DEST_PATH_IMAGE284
，设定路网上路段
Figure 321558DEST_PATH_IMAGE285
与路段
Figure 265243DEST_PATH_IMAGE273
之间的最短距离长度为
Figure 745772DEST_PATH_IMAGE286
，
Figure 333879DEST_PATH_IMAGE287
的赋值公式如下：By using the spatial relationship between each road segment in the road network data, any two road segments in the set of adjacent road segments are defined and obtained
Figure 560592DEST_PATH_IMAGE281
and
Figure 626768DEST_PATH_IMAGE282
transfer impedance between
Figure 382235DEST_PATH_IMAGE283
, whose value range is
Figure 896261DEST_PATH_IMAGE284
, set the road network segment
Figure 321558DEST_PATH_IMAGE285
with road sections
Figure 265243DEST_PATH_IMAGE273
The shortest distance between
Figure 745772DEST_PATH_IMAGE286
,
Figure 333879DEST_PATH_IMAGE287
The assignment formula is as follows:

Figure 789131DEST_PATH_IMAGE288
；
Figure 789131DEST_PATH_IMAGE288
;

其中，

Figure 141484DEST_PATH_IMAGE289
表示任意两条路段
Figure 114119DEST_PATH_IMAGE290
与
Figure 681367DEST_PATH_IMAGE291
之间的转移阻抗，
Figure 494471DEST_PATH_IMAGE292
表示第
Figure 22535DEST_PATH_IMAGE292
个路段，
Figure 923495DEST_PATH_IMAGE293
表示第
Figure 266621DEST_PATH_IMAGE293
个路段，
Figure 204621DEST_PATH_IMAGE294
表示路网上路段
Figure 344615DEST_PATH_IMAGE292
与路段
Figure 173900DEST_PATH_IMAGE293
之间的最短距离；in,
Figure 141484DEST_PATH_IMAGE289
represents any two road segments
Figure 114119DEST_PATH_IMAGE290
and
Figure 681367DEST_PATH_IMAGE291
transfer impedance between,
Figure 494471DEST_PATH_IMAGE292
means the first
Figure 22535DEST_PATH_IMAGE292
road section,
Figure 923495DEST_PATH_IMAGE293
means the first
Figure 266621DEST_PATH_IMAGE293
road section,
Figure 204621DEST_PATH_IMAGE294
Represents a road network segment
Figure 344615DEST_PATH_IMAGE292
with road sections
Figure 173900DEST_PATH_IMAGE293
the shortest distance between;

当轨迹点

Figure 387843DEST_PATH_IMAGE295
在路段
Figure 621379DEST_PATH_IMAGE296
上时，后一个轨迹点
Figure 170040DEST_PATH_IMAGE297
在路段
Figure 288169DEST_PATH_IMAGE298
上的概率为所述节点事件之间的转移概率
Figure 605887DEST_PATH_IMAGE299
，得到所述节点事件之间的转移概率与路段间的转移阻抗
Figure 682427DEST_PATH_IMAGE300
相关，所述转移阻抗越小则所述节点事件之间的转移概率越大，用下述公式进行计算：when the track point
Figure 387843DEST_PATH_IMAGE295
on the road
Figure 621379DEST_PATH_IMAGE296
up, the next track point
Figure 170040DEST_PATH_IMAGE297
on the road
Figure 288169DEST_PATH_IMAGE298
The probability on is the transition probability between the node events
Figure 605887DEST_PATH_IMAGE299
, obtain the transition probability between the node events and the transition impedance between the road segments
Figure 682427DEST_PATH_IMAGE300
Correlation, the smaller the transition impedance, the greater the transition probability between the node events, which is calculated by the following formula:

Figure 718385DEST_PATH_IMAGE301
；
Figure 718385DEST_PATH_IMAGE301
;

其中，

Figure 109046DEST_PATH_IMAGE299
表示节点事件之间的转移概率，
Figure 625478DEST_PATH_IMAGE302
表示路段间的转移阻抗，
Figure 122188DEST_PATH_IMAGE303
表示求和时第
Figure 51966DEST_PATH_IMAGE304
个转移阻抗数据。in,
Figure 109046DEST_PATH_IMAGE299
represents the transition probability between node events,
Figure 625478DEST_PATH_IMAGE302
represents the transfer impedance between road segments,
Figure 122188DEST_PATH_IMAGE303
Indicates the time of summation
Figure 51966DEST_PATH_IMAGE304
transfer impedance data.

具体的，利用路段之间的空间关联关系定义路段之间的转移阻抗，阻抗矩阵如表9所示：Specifically, the transfer impedance between the road segments is defined by the spatial relationship between the road segments, and the impedance matrix is shown in Table 9:

Figure 761165DEST_PATH_IMAGE305
Figure 761165DEST_PATH_IMAGE305

表9Table 9

根据阻抗计算得到相邻前后轨迹点之间的转移概率

Figure 522317DEST_PATH_IMAGE306
。Calculate the transition probability between adjacent track points before and after the impedance
Figure 522317DEST_PATH_IMAGE306
.

图4示意性表示自行车轨迹线的最优匹配路段示意图。FIG. 4 schematically shows a schematic diagram of an optimal matching road segment for a bicycle trajectory line.

根据本发明的一个实施例，轨迹线匹配模块是构建自行车轨迹与路段之间的马尔可夫链并查找轨迹线的最优匹配结果。所述构建前后轨迹点对映射到路段的条件概率模型为：在所述轨迹线与路网之间的匹配概率网络T-R-N上，对每种匹配方案构建所述自行车轨迹与路段之间的马尔可夫链，根据所述节点事件发生概率

Figure 393190DEST_PATH_IMAGE307
和所述节点事件之间的转移概率
Figure 896019DEST_PATH_IMAGE308
，通过网络路径搜索算法搜索出所述最大组合概率的自行车轨迹与路段之间的马尔可夫链，其所对应的路段序列
Figure 877750DEST_PATH_IMAGE309
为轨迹线的最优匹配结果。According to an embodiment of the present invention, the trajectory line matching module is to construct a Markov chain between the bicycle trajectory and the road segment and find the optimal matching result of the trajectory line. The conditional probability model in which the front and rear track point pairs are mapped to the road section is: on the matching probability network TRN between the trajectory line and the road network, for each matching scheme, the Marko between the bicycle trajectory and the road section is constructed. Fu chain, according to the probability of occurrence of the node event
Figure 393190DEST_PATH_IMAGE307
and the transition probability between the node events
Figure 896019DEST_PATH_IMAGE308
, the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability is searched through the network path search algorithm, and the corresponding road segment sequence
Figure 877750DEST_PATH_IMAGE309
is the optimal matching result of the trajectory line.

通过设置轨迹线与路网之间的匹配概率网络T-R-N中的网络节点集合

Figure 712982DEST_PATH_IMAGE208
中的每一个节点为初始节点，网络节点集合
Figure 82653DEST_PATH_IMAGE209
中的任一节点为终到节点，利用所述轨迹线与路网之间的匹配概率网络T-R-N中的有向连接弧，遍历整个网络，所述起点节点到所述终到节点间的任意一条节点序列对应一项自行车轨迹线匹配方案，构建自行车轨迹与路段之间的马尔可夫链。The set of network nodes in the network TRN by setting the matching probability between the trajectory line and the road network
Figure 712982DEST_PATH_IMAGE208
Each node in is an initial node, a collection of network nodes
Figure 82653DEST_PATH_IMAGE209
Any node is the final node, and the directed connection arc in the matching probability network TRN between the trajectory line and the road network is used to traverse the entire network, and any one between the starting node and the final node is used. The node sequence corresponds to a bicycle trajectory line matching scheme, and a Markov chain between bicycle trajectory and road segment is constructed.

所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件发生概率

Figure 65652DEST_PATH_IMAGE310
和所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件之间的转移概率
Figure 116654DEST_PATH_IMAGE311
通过公式计算得到自行车轨迹与路段之间的马尔可夫链的组合概率
Figure 868709DEST_PATH_IMAGE212
，得到最大组合概率的自行车轨迹与路段之间的马尔可夫链，The matching probability between the trajectory line and the road network The node event occurrence probability in the network TRN
Figure 65652DEST_PATH_IMAGE310
and the matching probability between the trajectory line and the road network and the transition probability between node events in the network TRN
Figure 116654DEST_PATH_IMAGE311
The combined probability of the Markov chain between the bicycle track and the road segment is calculated by the formula
Figure 868709DEST_PATH_IMAGE212
, the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability is obtained,

公式：

Figure 392969DEST_PATH_IMAGE312
；formula:
Figure 392969DEST_PATH_IMAGE312
;

其中，

Figure 174849DEST_PATH_IMAGE313
表示节点事件发生概率，
Figure 295121DEST_PATH_IMAGE314
表示节点事件之间的转移概率，
Figure 88633DEST_PATH_IMAGE315
表示自行车轨迹的路段马尔可夫链的组合概率；in,
Figure 174849DEST_PATH_IMAGE313
represents the probability of node event occurrence,
Figure 295121DEST_PATH_IMAGE314
represents the transition probability between node events,
Figure 88633DEST_PATH_IMAGE315
the combined probability of the segment Markov chain representing the bicycle trajectory;

根据所述轨迹线的路段匹配概率网络T-R-N，通过网络路径搜索算法搜索出所述最大组合概率的自行车轨迹与路段之间的马尔可夫链，其所对应的路段序列

Figure 252636DEST_PATH_IMAGE217
为轨迹线的最优匹配结果。According to the road segment matching probability network TRN of the trajectory line, the Markov chain between the bicycle trajectory with the maximum combined probability and the road segment is searched through the network path search algorithm, and the corresponding road segment sequence
Figure 252636DEST_PATH_IMAGE217
is the optimal matching result of the trajectory line.

具体的，如表10所示最优匹配路段结果，图4示意性表示自行车轨迹线的最优匹配路段示意图：Specifically, as shown in Table 10, the results of the optimal matching road section, and FIG. 4 schematically shows the schematic diagram of the optimal matching road section of the bicycle trajectory:

Figure 69282DEST_PATH_IMAGE316
Figure 69282DEST_PATH_IMAGE316

表10Table 10

本实施例的有益效果在于：The beneficial effects of this embodiment are:

该针对有噪声且参数不明的自行车轨迹数据的路网匹配方法既考虑了较大噪声条件下轨迹点与路段在空间上的映射概率，又考虑了骑行过程中多轨迹点在路段上的空间关联概率，并采用基于概率网络的搜索算法识别出最大可能性的路段序列，能够在骑行者手机定位数据噪声较大、定位误差参数不明确且无法进行轨迹数据纠偏的现实条件下，实现自行车轨迹与路网的最优匹配。The road network matching method for noisy bicycle trajectory data with unknown parameters not only considers the spatial mapping probability of trajectory points and road segments under the condition of large noise, but also considers the spatial mapping of multiple trajectory points on road segments during cycling. Correlation probability, and use the search algorithm based on probability network to identify the most likely road segment sequence, which can realize the bicycle trajectory under the realistic conditions that the cyclist’s mobile phone positioning data is noisy, the positioning error parameters are unclear, and the trajectory data cannot be corrected. The best match with the road network.

为实现上述发明目的，本发明还提供一种电子设备，该电子设备包括：处理器、存储器及存储在所述存储器上并可在所述处理器上运行的计算机程序，计算机程序被处理器执行时实现上述针对有噪声且参数不明的自行车轨迹数据的路网匹配方法。In order to achieve the above purpose of the invention, the present invention also provides an electronic device, the electronic device includes: a processor, a memory and a computer program stored on the memory and running on the processor, the computer program being executed by the processor The above-mentioned road network matching method for the bicycle trajectory data with noise and unknown parameters is realized at the same time.

为实现上述发明目的，本发明还提供一种计算机可读存储介质，计算机可读存储介质上存储计算机程序，计算机程序被处理器执行时实现上述针对有噪声且参数不明的自行车轨迹数据的路网匹配方法。In order to achieve the above object of the invention, the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by the processor, the above-mentioned road network for the bicycle track data with noise and unknown parameters is realized. matching method.

由此可知，本发明所提供的针对有噪声且参数不明的自行车轨迹数据的路网匹配方法有效地解决了上述现有技术中的多个技术问题。并且充分利用共享单车运营平台的轨迹大数据，为城市慢行系统的交通管理应用提供数据基础。It can be seen from this that the road network matching method for the bicycle trajectory data with noise and unknown parameters provided by the present invention effectively solves the above-mentioned many technical problems in the prior art. And make full use of the trajectory big data of the shared bicycle operation platform to provide a data foundation for the traffic management application of the urban slow-moving system.

本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的模块及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。Those of ordinary skill in the art can realize that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of the present invention.

所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的装置和设备的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。Those skilled in the art can clearly understand that, for the convenience and brevity of description, for the specific working process of the above-described apparatuses and devices, reference may be made to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

在本申请所提供的实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述模块的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个模块或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或模块的间接耦合或通信连接，可以是电性，机械或其它的形式。In the embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or modules, and may be in electrical, mechanical or other forms.

所述作为分离部件说明的模块可以是或者也可以不是物理上分开的，作为模块显示的部件可以是或者也可以不是物理模块，即可以位于一个地方，或者也可以分布到多个网络模块上。可以根据实际的需要选择其中的部分或者全部模块来实现本发明实施例方案的目的。The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, may be located in one place, or may be distributed to multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solutions of the embodiments of the present invention.

另外，在本发明实施例中的各功能模块可以集成在一个处理模块中，也可以是各个模块单独物理存在，也可以两个或两个以上模块集成在一个模块中。In addition, each functional module in this embodiment of the present invention may be integrated into one processing module, or each module may exist physically alone, or two or more modules may be integrated into one module.

所述功能如果以软件功能模块的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本发明各个实施例节能信号发送/接收的方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method for transmitting/receiving an energy-saving signal according to various embodiments of the present invention. The aforementioned storage medium includes: a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk and other mediums that can store program codes.

以上描述仅为本申请的较佳实施例以及对所运用技术原理的说明。本领域技术人员应当理解，本申请中所涉及的发明范围，并不限于上述技术特征的特定组合而成的技术方案，同时也应涵盖在不脱离所述发明构思的情况下，由上述技术特征或其等同特征进行任意组合而形成的其它技术方案。例如上述特征与本申请中公开的(但不限于)具有类似功能的技术特征进行互相替换而形成的技术方案。The above description is only a preferred embodiment of the present application and an illustration of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solution formed by the specific combination of the above-mentioned technical features, and should also cover the above-mentioned technical features without departing from the inventive concept. Other technical solutions formed by any combination of its equivalent features. For example, a technical solution is formed by replacing the above-mentioned features with the technical features disclosed in this application (but not limited to) with similar functions.

应理解，本发明的发明内容及实施例中各步骤的序号的大小并不绝对意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本发明实施例的实施过程构成任何限定。It should be understood that the size of the sequence numbers of the steps in the content of the present invention and the embodiments does not absolutely mean the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, and should not Implementation constitutes any limitation.

Claims (4)

1.一种针对有噪声且参数不明的自行车轨迹数据的路网匹配方法，其特征在于，包括：1. a road network matching method for the bicycle track data with noise and unknown parameters, is characterized in that, comprises: 步骤1：采集自行车的骑行轨迹线的轨迹点序列
Figure DEST_PATH_IMAGE002
和路网中由所述骑行轨迹 线的距离阈值S范围内的所有路段形成的近邻路段集合
Figure DEST_PATH_IMAGE004
，N为所述骑行轨迹线 上的轨迹点的总数，M为所述近邻路段集合中的路段总数，构建轨迹线与路网之间的匹配概 率网络T-R-N； Step 1: Collect the track point sequence of the cycling track line of the bicycle
Figure DEST_PATH_IMAGE002
and the set of nearby road segments formed by all road segments within the range of the distance threshold S of the cycling trajectory line in the road network
Figure DEST_PATH_IMAGE004
, N is the total number of trajectory points on the riding trajectory line, M is the total number of road sections in the set of adjacent road sections, and a matching probability network TRN between the trajectory line and the road network is constructed; 步骤2：根据所述轨迹点序列和所述近邻路段集合，构建从单轨迹点映射到路段的随机 事件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件发生概率
Figure DEST_PATH_IMAGE006
； Step 2: According to the trajectory point sequence and the set of adjacent road sections, construct a random event probability model mapped from a single trajectory point to a road section, and calculate the matching probability between the trajectory line and the road network. The occurrence of node events in the network TRN probability
Figure DEST_PATH_IMAGE006
; 步骤3：根据所述轨迹点序列中的相邻轨迹点对，构建自行车前后轨迹点对映射到路段 的条件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件之间的 转移概率
Figure DEST_PATH_IMAGE008
； Step 3: According to the adjacent trajectory point pairs in the trajectory point sequence, construct a conditional probability model in which the front and rear trajectory point pairs of the bicycle are mapped to the road sections, and calculate the matching probability network between the trajectory line and the road network. Node events in the network TRN transition probability between
Figure DEST_PATH_IMAGE008
; 步骤4：在所述轨迹线与路网之间的匹配概率网络T-R-N上构建自行车轨迹与路段之间的马 尔可夫链，根据所述节点事件发生概率
Figure DEST_PATH_IMAGE010
和所述节点事件之间的转移概率
Figure DEST_PATH_IMAGE012
，得到最大 组合概率的自行车轨迹与路段之间的马尔可夫链，其所对应的路段序列
Figure DEST_PATH_IMAGE014
为轨迹线的最优匹配结果； Step 4: Build a Markov chain between the bicycle trajectory and the road segment on the matching probability network TRN between the trajectory line and the road network, according to the probability of occurrence of the node event
Figure DEST_PATH_IMAGE010
and the transition probability between the node events
Figure DEST_PATH_IMAGE012
, the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability is obtained, and the corresponding road segment sequence
Figure DEST_PATH_IMAGE014
is the optimal matching result of the trajectory line; 所述构建轨迹线对路网的匹配概率网络T-R-N为：The matching probability network T-R-N of the constructed trajectory line-to-road network is: 通过单点角度提取轨迹点与路段空间映射的不确定性关系和前后点间关联角度提取骑行过程在路段上空间关联的不确定性关系，构建轨迹线与路网之间的匹配概率网络T-R-N；Extract the uncertainty relationship between the trajectory point and the road segment spatial mapping through the single-point angle and the correlation angle between the front and rear points to extract the uncertainty relationship of the spatial association of the riding process on the road segment, and construct a matching probability network T-R-N between the trajectory line and the road network. ; 所述构建从单轨迹点映射到路段的随机事件概率模型为：The random event probability model that is constructed from a single trajectory point to a road segment is: 通过利用轨迹点与近邻路段集合R中各个路段的垂直距离关系，构建基于横向距离分 布的从单轨迹点映射到路段的随机事件概率模型，计算所述轨迹线与路网之间的匹配概率 网络T-R-N中的节点事件发生概率
Figure DEST_PATH_IMAGE016
； By using the vertical distance relationship between the trajectory point and each road segment in the set R of adjacent road segments, a random event probability model from a single trajectory point to a road segment is constructed based on the horizontal distance distribution, and the matching probability network between the trajectory line and the road network is calculated. Node Event Occurrence Probability in TRN
Figure DEST_PATH_IMAGE016
; 所述构建前后轨迹点对映射到路段的条件概率模型为：The conditional probability model in which the trajectory point pairs before and after the construction are mapped to the road section is: 通过利用所述相邻轨迹点对所在路段间的空间关联关系，构建所述前后轨迹点对的路 段映射条件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件之 间的转移概率
Figure DEST_PATH_IMAGE018
； By using the spatial correlation between the road segments where the adjacent trajectory point pairs are located, a road segment mapping conditional probability model of the front and rear trajectory point pairs is constructed, and the matching probability between the trajectory line and the road network is calculated. Node events in the network TRN transition probability between
Figure DEST_PATH_IMAGE018
; 所述轨迹线的最优匹配结果为：The optimal matching result of the trajectory line is: 在所述轨迹线与路网之间的匹配概率网络T-R-N上，对每种匹配方案构建所述自行车 轨迹与路段之间的马尔可夫链，根据所述节点事件发生概率
Figure DEST_PATH_IMAGE020
和所述节点事件之间的 转移概率
Figure DEST_PATH_IMAGE022
，通过网络路径搜索算法搜索出所述最大组合概率的自行车轨迹与路段之 间的马尔可夫链，其所对应的路段序列
Figure DEST_PATH_IMAGE023
为轨迹线的最优匹配结果； On the matching probability network TRN between the trajectory line and the road network, a Markov chain between the bicycle trajectory and the road segment is constructed for each matching scheme, according to the probability of occurrence of the node event
Figure DEST_PATH_IMAGE020
and the transition probability between the node events
Figure DEST_PATH_IMAGE022
, the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability is searched through the network path search algorithm, and the corresponding road segment sequence
Figure DEST_PATH_IMAGE023
is the optimal matching result of the trajectory line; 所述构建轨迹线与路网之间的匹配概率网络T-R-N包括：The matching probability network T-R-N between the constructed trajectory line and the road network includes: 根据所述轨迹点序列
Figure DEST_PATH_IMAGE025
中的任一轨迹点
Figure DEST_PATH_IMAGE027
，得到轨迹点i在所述近邻路段集合
Figure DEST_PATH_IMAGE029
中路段
Figure DEST_PATH_IMAGE031
的不确定性事件节点
Figure DEST_PATH_IMAGE033
，同一轨迹点i的所有事件节点构成集合
Figure DEST_PATH_IMAGE035
，得到所有的事件节点 构成所述路网匹配概率网络T-R-N中的节点集合
Figure DEST_PATH_IMAGE037
； According to the trajectory point sequence
Figure DEST_PATH_IMAGE025
any track point in
Figure DEST_PATH_IMAGE027
, get the trajectory point i in the set of adjacent road sections
Figure DEST_PATH_IMAGE029
middle section
Figure DEST_PATH_IMAGE031
The uncertainty event node of
Figure DEST_PATH_IMAGE033
, all event nodes of the same trajectory point i constitute a set
Figure DEST_PATH_IMAGE035
, get all the event nodes to form the node set in the road network matching probability network TRN
Figure DEST_PATH_IMAGE037
; 根据所述轨迹点序列
Figure DEST_PATH_IMAGE039
中的前后相邻轨迹点对
Figure DEST_PATH_IMAGE041
，得到在任一轨迹点
Figure DEST_PATH_IMAGE043
所属的任 意路段
Figure DEST_PATH_IMAGE045
的不确定性事件节点
Figure DEST_PATH_IMAGE047
和轨迹点
Figure DEST_PATH_IMAGE049
所属的任意路段
Figure DEST_PATH_IMAGE051
的不确定性事件节点
Figure DEST_PATH_IMAGE053
之间设置的有向连接弧
Figure DEST_PATH_IMAGE055
，其代表事件之间的转移关系，所有的事件转移关系构成了所 述轨迹线与路网之间的匹配概率网络T-R-N中的有向连接弧集合
Figure DEST_PATH_IMAGE057
，构建所述轨迹线与路网 之间的匹配概率网络T-R-N； According to the trajectory point sequence
Figure DEST_PATH_IMAGE039
pair of adjacent trajectory points in
Figure DEST_PATH_IMAGE041
, get at any trajectory point
Figure DEST_PATH_IMAGE043
any road segment
Figure DEST_PATH_IMAGE045
The uncertainty event node of
Figure DEST_PATH_IMAGE047
and track points
Figure DEST_PATH_IMAGE049
any road segment
Figure DEST_PATH_IMAGE051
The uncertainty event node of
Figure DEST_PATH_IMAGE053
Directed connection arcs set between
Figure DEST_PATH_IMAGE055
, which represents the transition relationship between events, and all event transition relationships constitute the set of directed connection arcs in the matching probability network TRN between the trajectory line and the road network
Figure DEST_PATH_IMAGE057
, construct the matching probability network TRN between the trajectory line and the road network; 计算所述节点事件发生概率包括：Calculating the occurrence probability of the node event includes: 根据各个轨迹点和近邻路段集中各个路段的坐标数据，得到任一轨迹点
Figure DEST_PATH_IMAGE059
与所述近邻 路段集合中任一路段
Figure DEST_PATH_IMAGE061
的中心点的垂直距离
Figure DEST_PATH_IMAGE063
，得到所述轨迹点
Figure DEST_PATH_IMAGE065
的所有近邻路段中最远 的近邻路段的垂直距离
Figure DEST_PATH_IMAGE067
，计算得到任一近邻路段距离
Figure DEST_PATH_IMAGE069
与所述最远的近邻路段的垂直距 离
Figure DEST_PATH_IMAGE071
的差值
Figure DEST_PATH_IMAGE073
： According to each trajectory point and the coordinate data of each road section in the set of adjacent road sections, any trajectory point is obtained
Figure DEST_PATH_IMAGE059
with any road segment in the set of adjacent road segments
Figure DEST_PATH_IMAGE061
The vertical distance of the center point of
Figure DEST_PATH_IMAGE063
, get the trajectory point
Figure DEST_PATH_IMAGE065
The vertical distance of the farthest neighbor of all neighbors
Figure DEST_PATH_IMAGE067
, calculate the distance of any nearby road segment
Figure DEST_PATH_IMAGE069
the vertical distance from the farthest neighbor
Figure DEST_PATH_IMAGE071
difference
Figure DEST_PATH_IMAGE073
:
Figure DEST_PATH_IMAGE075
；
Figure DEST_PATH_IMAGE075
; 其中，
Figure DEST_PATH_IMAGE076
表示轨迹点
Figure 221737DEST_PATH_IMAGE065
与所述近邻路段集合中路段
Figure DEST_PATH_IMAGE077
的中心点的垂直距离，
Figure DEST_PATH_IMAGE078
表示轨 迹点
Figure 14244DEST_PATH_IMAGE065
的所有近邻路段中最远的近邻路段的垂直距离，
Figure DEST_PATH_IMAGE079
表示
Figure DEST_PATH_IMAGE080
与所述最远的近邻路 段的垂直距离
Figure 400838DEST_PATH_IMAGE071
的差值，
Figure DEST_PATH_IMAGE082
表示第
Figure DEST_PATH_IMAGE084
个轨迹点，
Figure DEST_PATH_IMAGE086
表示第
Figure 609097DEST_PATH_IMAGE086
个路段； in,
Figure DEST_PATH_IMAGE076
Represents a track point
Figure 221737DEST_PATH_IMAGE065
The road segment in the set with the neighbor road segment
Figure DEST_PATH_IMAGE077
The vertical distance from the center point of ,
Figure DEST_PATH_IMAGE078
Represents a track point
Figure 14244DEST_PATH_IMAGE065
The vertical distance of the farthest adjacent road segment among all the adjacent road segments of ,
Figure DEST_PATH_IMAGE079
express
Figure DEST_PATH_IMAGE080
the vertical distance from the farthest neighbor
Figure 400838DEST_PATH_IMAGE071
difference of ,
Figure DEST_PATH_IMAGE082
means the first
Figure DEST_PATH_IMAGE084
track points,
Figure DEST_PATH_IMAGE086
means the first
Figure 609097DEST_PATH_IMAGE086
a road segment; 根据所述轨迹点
Figure DEST_PATH_IMAGE088
与各路段垂直距离的定位偏差是独立同分布随机变量，得到轨迹点
Figure DEST_PATH_IMAGE090
在所述近邻路段集合中的路段
Figure DEST_PATH_IMAGE092
的所述节点事件发生概率
Figure DEST_PATH_IMAGE094
与该轨迹点距所述路段
Figure DEST_PATH_IMAGE096
的垂直距离相关，
Figure DEST_PATH_IMAGE098
越小，则所述节点事件发生概率
Figure DEST_PATH_IMAGE100
越大，得到轨迹点
Figure DEST_PATH_IMAGE102
在所述近邻 路段集合中的其他各个路段k的所述
Figure DEST_PATH_IMAGE104
相关，采用公式计算
Figure DEST_PATH_IMAGE106
： According to the trajectory point
Figure DEST_PATH_IMAGE088
The positioning deviation of the vertical distance from each road section is an independent and identically distributed random variable, and the trajectory point is obtained.
Figure DEST_PATH_IMAGE090
road segments in the set of neighbor road segments
Figure DEST_PATH_IMAGE092
The node event occurrence probability of
Figure DEST_PATH_IMAGE094
Distance from the track point to the road segment
Figure DEST_PATH_IMAGE096
is related to the vertical distance,
Figure DEST_PATH_IMAGE098
The smaller the probability of occurrence of the node event
Figure DEST_PATH_IMAGE100
bigger, get track points
Figure DEST_PATH_IMAGE102
In the set of neighboring road segments, the other respective road segments k
Figure DEST_PATH_IMAGE104
Correlation, calculated by formula
Figure DEST_PATH_IMAGE106
:
Figure DEST_PATH_IMAGE108
；
Figure DEST_PATH_IMAGE108
; 其中，
Figure DEST_PATH_IMAGE109
表示节点事件发生概率，
Figure 62950DEST_PATH_IMAGE071
表示轨迹点
Figure 416571DEST_PATH_IMAGE065
的所有近邻路段中最远的近邻路 段的垂直距离，
Figure 824549DEST_PATH_IMAGE063
表示任一轨迹点
Figure 223782DEST_PATH_IMAGE065
与所述近邻路段集合中任一路段
Figure 158240DEST_PATH_IMAGE077
的中心点的垂直距 离，
Figure DEST_PATH_IMAGE111
表示任一轨迹点
Figure 495812DEST_PATH_IMAGE065
与所述近邻路段集合中其他路段
Figure DEST_PATH_IMAGE113
的中心点的垂直距离，
Figure DEST_PATH_IMAGE114
表示第
Figure DEST_PATH_IMAGE116
个轨迹点，
Figure DEST_PATH_IMAGE117
表示第
Figure DEST_PATH_IMAGE118
个路段，
Figure DEST_PATH_IMAGE120
表示第
Figure DEST_PATH_IMAGE121
个路段； in,
Figure DEST_PATH_IMAGE109
represents the probability of node event occurrence,
Figure 62950DEST_PATH_IMAGE071
Represents a track point
Figure 416571DEST_PATH_IMAGE065
The vertical distance of the farthest adjacent road segment among all the adjacent road segments of ,
Figure 824549DEST_PATH_IMAGE063
represents any track point
Figure 223782DEST_PATH_IMAGE065
with any road segment in the set of adjacent road segments
Figure 158240DEST_PATH_IMAGE077
The vertical distance from the center point of ,
Figure DEST_PATH_IMAGE111
represents any track point
Figure 495812DEST_PATH_IMAGE065
and other road segments in the set of adjacent road segments
Figure DEST_PATH_IMAGE113
The vertical distance from the center point of ,
Figure DEST_PATH_IMAGE114
means the first
Figure DEST_PATH_IMAGE116
track points,
Figure DEST_PATH_IMAGE117
means the first
Figure DEST_PATH_IMAGE118
road section,
Figure DEST_PATH_IMAGE120
means the first
Figure DEST_PATH_IMAGE121
a road segment; 所述计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件之间的转移概率
Figure DEST_PATH_IMAGE123
包括： The calculation of the matching probability between the trajectory line and the road network is the transition probability between node events in the network TRN
Figure DEST_PATH_IMAGE123
include: 通过利用路网数据中各个路段间的空间关联关系，定义得到所述近邻路段集合中任意 两条路段
Figure DEST_PATH_IMAGE125
与
Figure DEST_PATH_IMAGE126
之间的转移阻抗
Figure DEST_PATH_IMAGE128
，其取值区间为
Figure DEST_PATH_IMAGE130
，设定路网上路段
Figure DEST_PATH_IMAGE131
与路段
Figure DEST_PATH_IMAGE132
之间 的最短距离长度为
Figure DEST_PATH_IMAGE134
，
Figure DEST_PATH_IMAGE136
的赋值公式如下： By using the spatial relationship between each road segment in the road network data, any two road segments in the set of adjacent road segments are defined and obtained
Figure DEST_PATH_IMAGE125
and
Figure DEST_PATH_IMAGE126
transfer impedance between
Figure DEST_PATH_IMAGE128
, whose value range is
Figure DEST_PATH_IMAGE130
, set the road network segment
Figure DEST_PATH_IMAGE131
with road sections
Figure DEST_PATH_IMAGE132
The shortest distance between
Figure DEST_PATH_IMAGE134
,
Figure DEST_PATH_IMAGE136
The assignment formula is as follows:
Figure DEST_PATH_IMAGE138
；
Figure DEST_PATH_IMAGE138
; 其中，
Figure 371845DEST_PATH_IMAGE136
表示任意两条路段
Figure DEST_PATH_IMAGE140
与
Figure DEST_PATH_IMAGE142
之间的转移阻抗，
Figure DEST_PATH_IMAGE144
表示第
Figure 921906DEST_PATH_IMAGE144
个路段，
Figure DEST_PATH_IMAGE145
表示第
Figure 179712DEST_PATH_IMAGE145
个路 段，
Figure DEST_PATH_IMAGE147
表示路网上路段
Figure 953764DEST_PATH_IMAGE144
与路段
Figure 460969DEST_PATH_IMAGE145
之间的最短距离； in,
Figure 371845DEST_PATH_IMAGE136
represents any two road segments
Figure DEST_PATH_IMAGE140
and
Figure DEST_PATH_IMAGE142
transfer impedance between,
Figure DEST_PATH_IMAGE144
means the first
Figure 921906DEST_PATH_IMAGE144
road section,
Figure DEST_PATH_IMAGE145
means the first
Figure 179712DEST_PATH_IMAGE145
road section,
Figure DEST_PATH_IMAGE147
Represents a road network segment
Figure 953764DEST_PATH_IMAGE144
with road sections
Figure 460969DEST_PATH_IMAGE145
the shortest distance between; 当轨迹点
Figure DEST_PATH_IMAGE149
在路段
Figure DEST_PATH_IMAGE151
上时，后一个轨迹点
Figure DEST_PATH_IMAGE153
在路段
Figure DEST_PATH_IMAGE155
上的概率为所述节点事件之间的 转移概率
Figure DEST_PATH_IMAGE157
，得到所述节点事件之间的转移概率与路段间的转移阻抗
Figure DEST_PATH_IMAGE159
相关，所述 转移阻抗越小则所述节点事件之间的转移概率越大，用下述公式进行计算： when the track point
Figure DEST_PATH_IMAGE149
on the road
Figure DEST_PATH_IMAGE151
up, the next track point
Figure DEST_PATH_IMAGE153
on the road
Figure DEST_PATH_IMAGE155
The probability on is the transition probability between the node events
Figure DEST_PATH_IMAGE157
, obtain the transition probability between the node events and the transition impedance between the road segments
Figure DEST_PATH_IMAGE159
Correlation, the smaller the transition impedance, the greater the transition probability between the node events, which is calculated by the following formula:
Figure DEST_PATH_IMAGE161
；
Figure DEST_PATH_IMAGE161
; 其中，
Figure DEST_PATH_IMAGE163
表示节点事件之间的转移概率，
Figure DEST_PATH_IMAGE164
表示路段间的转移阻抗,
Figure DEST_PATH_IMAGE166
表示求和 时第
Figure DEST_PATH_IMAGE168
个转移阻抗数据； in,
Figure DEST_PATH_IMAGE163
represents the transition probability between node events,
Figure DEST_PATH_IMAGE164
represents the transfer impedance between road segments,
Figure DEST_PATH_IMAGE166
Indicates the time of summation
Figure DEST_PATH_IMAGE168
transfer impedance data; 所述构建自行车轨迹与路段之间的马尔可夫链包括：The construction of a Markov chain between bicycle trajectories and road segments includes: 通过设置轨迹线与路网之间的匹配概率网络T-R-N中的网络节点集合
Figure DEST_PATH_IMAGE170
中的每一个节 点为初始节点，网络节点集合
Figure DEST_PATH_IMAGE172
中的任一节点为终到节点，利用所述轨迹线与路网之间的 匹配概率网络T-R-N中的有向连接弧，遍历整个网络，所述初始节点到所述终到节点间的任 意一条节点序列对应一项自行车轨迹线匹配方案，构建自行车轨迹与路段之间的马尔可夫 链； The set of network nodes in the network TRN by setting the matching probability between the trajectory line and the road network
Figure DEST_PATH_IMAGE170
Each node in is an initial node, a collection of network nodes
Figure DEST_PATH_IMAGE172
Any node is the final node, and the directed connection arc in the matching probability network TRN between the trajectory line and the road network is used to traverse the entire network, and any one between the initial node and the final node is used. The node sequence corresponds to a bicycle trajectory line matching scheme, and a Markov chain between bicycle trajectory and road segment is constructed; 在所述轨迹线与路网之间的匹配概率网络T-R-N上搜索最大组合概率的自行车轨迹与路段之间的马尔可夫链并实现轨迹线的最优匹配结果包括：Searching the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability on the matching probability network T-R-N between the trajectory line and the road network and realizing the optimal matching result of the trajectory line includes: 所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件发生概率
Figure DEST_PATH_IMAGE174
和所述轨迹 线与路网之间的匹配概率网络T-R-N中的节点事件之间的转移概率
Figure DEST_PATH_IMAGE176
通过公式计算得 到自行车轨迹与路段之间的马尔可夫链的组合概率
Figure DEST_PATH_IMAGE178
，得到最大组合概率的自行车轨迹 与路段之间的马尔可夫链： The matching probability between the trajectory line and the road network The node event occurrence probability in the network TRN
Figure DEST_PATH_IMAGE174
and the matching probability between the trajectory line and the road network and the transition probability between node events in the network TRN
Figure DEST_PATH_IMAGE176
The combined probability of the Markov chain between the bicycle track and the road segment is calculated by the formula
Figure DEST_PATH_IMAGE178
, get the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability: 公式：
Figure DEST_PATH_IMAGE180
； formula:
Figure DEST_PATH_IMAGE180
; 其中，
Figure DEST_PATH_IMAGE182
表示节点事件发生概率，
Figure DEST_PATH_IMAGE183
表示节点事件之间的转移概率，
Figure DEST_PATH_IMAGE185
表 示自行车轨迹的路段马尔可夫连的组合概率； in,
Figure DEST_PATH_IMAGE182
represents the probability of node event occurrence,
Figure DEST_PATH_IMAGE183
represents the transition probability between node events,
Figure DEST_PATH_IMAGE185
the combined probability of the Markov connection representing the segment of the bicycle trajectory; 根据所述轨迹线的路段匹配概率网络T-R-N，通过网络路径搜索算法搜索出所述最大组合概率的自行车轨迹与路段之间的马尔可夫链，其所对应的路段序列
Figure DEST_PATH_IMAGE187
为轨迹线的最优匹配结果。According to the road segment matching probability network TRN of the trajectory line, the Markov chain between the bicycle trajectory with the maximum combined probability and the road segment is searched through the network path search algorithm, and the corresponding road segment sequence
Figure DEST_PATH_IMAGE187
is the optimal matching result of the trajectory line. 2.一种针对较大噪声且参数不明的自行车轨迹数据的路网匹配系统，其特征在于，包括：2. A road network matching system for bicycle trajectory data with relatively large noise and unknown parameters, characterized in that, comprising: 路网网络构建模块，采集自行车的骑行轨迹线的轨迹点序列
Figure DEST_PATH_IMAGE188
和路网中由所 述骑行轨迹线的距离阈值S范围内的所有路段形成的近邻路段集合
Figure DEST_PATH_IMAGE189
，N为所述 骑行轨迹线上的轨迹点的总数，M为所述近邻路段集合中的路段总数，构建轨迹线与路网之 间的匹配概率网络T-R-N； The road network network building module collects the trajectory point sequence of the bicycle's riding trajectory
Figure DEST_PATH_IMAGE188
and the set of nearby road segments formed by all road segments within the range of the distance threshold S of the cycling trajectory line in the road network
Figure DEST_PATH_IMAGE189
, N is the total number of trajectory points on the riding trajectory line, M is the total number of road sections in the set of adjacent road sections, and a matching probability network TRN between the trajectory line and the road network is constructed; 单轨迹点模型构建模块，根据所述轨迹点序列和所述近邻路段集合，构建从单轨迹点 映射到路段的随机事件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的 节点事件发生概率
Figure DEST_PATH_IMAGE190
； A single trajectory point model building module, according to the trajectory point sequence and the set of adjacent road segments, constructs a random event probability model mapped from a single trajectory point to a road segment, and calculates the matching probability between the trajectory line and the road network in the network TRN The probability of node event occurrence
Figure DEST_PATH_IMAGE190
; 前后轨迹点模型构建模块，根据所述轨迹点序列中的相邻轨迹点对，构建自行车前后 轨迹点对映射到路段的条件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N 中的节点事件之间的转移概率
Figure DEST_PATH_IMAGE191
； The front and rear trajectory point model building module, according to the adjacent trajectory point pairs in the trajectory point sequence, constructs the conditional probability model of the bicycle front and rear trajectory point pairs mapped to the road section, and calculates the matching probability network TRN between the trajectory line and the road network The transition probabilities between node events in
Figure DEST_PATH_IMAGE191
; 轨迹线匹配模块，在所述轨迹线与路网之间的匹配概率网络T-R-N上构建自行车轨迹 与路段之间的马尔可夫链，根据所述节点事件发生概率
Figure DEST_PATH_IMAGE192
和所述节点事件之间的转移 概率
Figure DEST_PATH_IMAGE193
，得到最大组合概率的自行车轨迹与路段之间的马尔可夫链，其所对应的路 段序列
Figure DEST_PATH_IMAGE194
为轨迹线的最优匹配结果； The trajectory line matching module constructs the Markov chain between the bicycle trajectory and the road segment on the matching probability network TRN between the trajectory line and the road network, according to the occurrence probability of the node event
Figure DEST_PATH_IMAGE192
and the transition probability between the node events
Figure DEST_PATH_IMAGE193
, the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability is obtained, and the corresponding road segment sequence
Figure DEST_PATH_IMAGE194
is the optimal matching result of the trajectory line; 所述构建轨迹线对路网的匹配概率网络T-R-N为：The matching probability network T-R-N of the constructed trajectory line-to-road network is: 通过单点角度提取轨迹点与路段空间映射的不确定性关系和前后点间关联角度提取骑行过程在路段上空间关联的不确定性关系，构建轨迹线与路网之间的匹配概率网络T-R-N；Extract the uncertainty relationship between the trajectory point and the road segment spatial mapping through the single-point angle and the correlation angle between the front and rear points to extract the uncertainty relationship of the spatial association of the riding process on the road segment, and construct a matching probability network T-R-N between the trajectory line and the road network. ; 所述构建从单轨迹点映射到路段的随机事件概率模型为：The random event probability model that is constructed from a single trajectory point to a road segment is: 通过利用轨迹点与近邻路段集合R中各个路段的垂直距离关系，构建基于横向距离分 布的从单轨迹点映射到路段的随机事件概率模型，计算所述轨迹线与路网之间的匹配概率 网络T-R-N中的节点事件发生概率
Figure DEST_PATH_IMAGE195
； By using the vertical distance relationship between the trajectory point and each road segment in the set R of adjacent road segments, a random event probability model from a single trajectory point to a road segment is constructed based on the horizontal distance distribution, and the matching probability network between the trajectory line and the road network is calculated. Node Event Occurrence Probability in TRN
Figure DEST_PATH_IMAGE195
; 所述构建前后轨迹点对映射到路段的条件概率模型为：The conditional probability model in which the trajectory point pairs before and after the construction are mapped to the road section is: 通过利用所述相邻轨迹点对所在路段间的空间关联关系，构建所述前后轨迹点对的路 段映射条件概率模型，计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件之 间的转移概率
Figure DEST_PATH_IMAGE196
； By using the spatial correlation between the road segments where the adjacent trajectory point pairs are located, a road segment mapping conditional probability model of the front and rear trajectory point pairs is constructed, and the matching probability between the trajectory line and the road network is calculated. Node events in the network TRN transition probability between
Figure DEST_PATH_IMAGE196
; 所述轨迹线的最优匹配结果为：The optimal matching result of the trajectory line is: 在所述轨迹线与路网之间的匹配概率网络T-R-N上，对每种匹配方案构建所述自行车 轨迹与路段之间的马尔可夫链，根据所述节点事件发生概率
Figure DEST_PATH_IMAGE197
和所述节点事件之间的转 移概率
Figure DEST_PATH_IMAGE198
，通过网络路径搜索算法搜索出所述最大组合概率的自行车轨迹与路段之 间的马尔可夫链，其所对应的路段序列
Figure DEST_PATH_IMAGE199
为轨迹线的最优匹配结果； On the matching probability network TRN between the trajectory line and the road network, a Markov chain between the bicycle trajectory and the road segment is constructed for each matching scheme, according to the occurrence probability of the node event
Figure DEST_PATH_IMAGE197
and the transition probability between the node events
Figure DEST_PATH_IMAGE198
, the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability is searched through the network path search algorithm, and the corresponding road segment sequence
Figure DEST_PATH_IMAGE199
is the optimal matching result of the trajectory line; 所述构建轨迹线与路网之间的匹配概率网络T-R-N包括：The matching probability network T-R-N between the constructed trajectory line and the road network includes: 根据所述轨迹点序列
Figure DEST_PATH_IMAGE200
中的任一轨迹点
Figure DEST_PATH_IMAGE201
，得到轨迹点i在所述近邻路段集合
Figure DEST_PATH_IMAGE202
中路段
Figure DEST_PATH_IMAGE203
的不确定性事件节点
Figure DEST_PATH_IMAGE204
，同一轨迹点i的所有事件节点构成集合
Figure DEST_PATH_IMAGE205
，得到所有的事件节点 构成所述路网匹配概率网络T-R-N中的节点集合
Figure DEST_PATH_IMAGE206
； According to the trajectory point sequence
Figure DEST_PATH_IMAGE200
any track point in
Figure DEST_PATH_IMAGE201
, get the trajectory point i in the set of adjacent road sections
Figure DEST_PATH_IMAGE202
middle section
Figure DEST_PATH_IMAGE203
The uncertainty event node of
Figure DEST_PATH_IMAGE204
, all event nodes of the same trajectory point i constitute a set
Figure DEST_PATH_IMAGE205
, get all the event nodes to form the node set in the road network matching probability network TRN
Figure DEST_PATH_IMAGE206
; 根据所述轨迹点序列
Figure DEST_PATH_IMAGE207
中的前后相邻轨迹点对
Figure DEST_PATH_IMAGE208
，得到在任一轨迹点
Figure DEST_PATH_IMAGE209
所属的任 意路段
Figure DEST_PATH_IMAGE210
的不确定性事件节点
Figure DEST_PATH_IMAGE211
和轨迹点
Figure DEST_PATH_IMAGE212
所属的任意路段
Figure DEST_PATH_IMAGE213
的不确定性事件节点
Figure DEST_PATH_IMAGE214
之间设置的有向连接弧
Figure DEST_PATH_IMAGE215
，其代表事件之间的转移关系，所有的事件转移关系构成 了所述轨迹线与路网之间的匹配概率网络T-R-N中的有向连接弧集合
Figure DEST_PATH_IMAGE216
，构建所述轨迹线与 路网之间的匹配概率网络T-R-N； According to the trajectory point sequence
Figure DEST_PATH_IMAGE207
pair of adjacent trajectory points in
Figure DEST_PATH_IMAGE208
, get at any trajectory point
Figure DEST_PATH_IMAGE209
any road segment
Figure DEST_PATH_IMAGE210
The uncertainty event node of
Figure DEST_PATH_IMAGE211
and track points
Figure DEST_PATH_IMAGE212
any road segment
Figure DEST_PATH_IMAGE213
The uncertainty event node of
Figure DEST_PATH_IMAGE214
Directed connection arcs set between
Figure DEST_PATH_IMAGE215
, which represents the transition relationship between events, and all event transition relationships constitute the set of directed connection arcs in the matching probability network TRN between the trajectory line and the road network
Figure DEST_PATH_IMAGE216
, construct the matching probability network TRN between the trajectory line and the road network; 计算所述节点事件发生概率包括：Calculating the occurrence probability of the node event includes: 根据各个轨迹点和近邻路段集中各个路段的坐标数据，得到任一轨迹点
Figure DEST_PATH_IMAGE217
与所述近邻 路段集合中任一路段
Figure DEST_PATH_IMAGE218
的中心点的垂直距离
Figure DEST_PATH_IMAGE219
，得到所述轨迹点
Figure DEST_PATH_IMAGE220
的所有近邻路段中最远 的近邻路段的垂直距离
Figure DEST_PATH_IMAGE221
，计算得到任一近邻路段距离
Figure DEST_PATH_IMAGE222
与所述最远的近邻路段的垂直 距离
Figure DEST_PATH_IMAGE223
的差值
Figure DEST_PATH_IMAGE224
： According to each trajectory point and the coordinate data of each road section in the set of adjacent road sections, any trajectory point is obtained
Figure DEST_PATH_IMAGE217
with any road segment in the set of adjacent road segments
Figure DEST_PATH_IMAGE218
The vertical distance of the center point of
Figure DEST_PATH_IMAGE219
, get the trajectory point
Figure DEST_PATH_IMAGE220
The vertical distance of the farthest neighbor of all neighbors
Figure DEST_PATH_IMAGE221
, calculate the distance of any nearby road segment
Figure DEST_PATH_IMAGE222
the vertical distance from the farthest neighbor
Figure DEST_PATH_IMAGE223
difference
Figure DEST_PATH_IMAGE224
:
Figure DEST_PATH_IMAGE225
；
Figure DEST_PATH_IMAGE225
; 其中，
Figure 734429DEST_PATH_IMAGE219
表示轨迹点
Figure 643480DEST_PATH_IMAGE220
与所述近邻路段集合中路段
Figure DEST_PATH_IMAGE226
的中心点的垂直距离，
Figure 322854DEST_PATH_IMAGE223
表示轨 迹点
Figure 255038DEST_PATH_IMAGE220
的所有近邻路段中最远的近邻路段的垂直距离，
Figure 599431DEST_PATH_IMAGE224
表示
Figure 503933DEST_PATH_IMAGE219
与所述最远的近邻路段 的垂直距离
Figure 478843DEST_PATH_IMAGE223
的差值，
Figure DEST_PATH_IMAGE227
表示第
Figure DEST_PATH_IMAGE228
个轨迹点，
Figure DEST_PATH_IMAGE229
表示第
Figure 239601DEST_PATH_IMAGE229
个路段； in,
Figure 734429DEST_PATH_IMAGE219
Represents a track point
Figure 643480DEST_PATH_IMAGE220
The road segment in the set with the neighbor road segment
Figure DEST_PATH_IMAGE226
The vertical distance from the center point of ,
Figure 322854DEST_PATH_IMAGE223
Represents a track point
Figure 255038DEST_PATH_IMAGE220
The vertical distance of the farthest adjacent road segment among all the adjacent road segments of ,
Figure 599431DEST_PATH_IMAGE224
express
Figure 503933DEST_PATH_IMAGE219
the vertical distance from the farthest neighbor
Figure 478843DEST_PATH_IMAGE223
difference of ,
Figure DEST_PATH_IMAGE227
means the first
Figure DEST_PATH_IMAGE228
track points,
Figure DEST_PATH_IMAGE229
means the first
Figure 239601DEST_PATH_IMAGE229
a road segment; 根据所述轨迹点
Figure DEST_PATH_IMAGE230
与各路段垂直距离的定位偏差是独立同分布随机变量，得到轨迹点
Figure DEST_PATH_IMAGE231
在所述近邻路段集合中的路段
Figure DEST_PATH_IMAGE232
的所述节点事件发生概率
Figure DEST_PATH_IMAGE233
与该轨迹点距所述路段
Figure DEST_PATH_IMAGE234
的垂直距离相关，
Figure DEST_PATH_IMAGE235
越小，则所述节点事件发生概率
Figure DEST_PATH_IMAGE236
越大，得到轨迹点
Figure DEST_PATH_IMAGE237
在所述近邻 路段集合中的其他各个路段k的所述
Figure DEST_PATH_IMAGE238
相关，采用公式计算
Figure DEST_PATH_IMAGE239
： According to the trajectory point
Figure DEST_PATH_IMAGE230
The positioning deviation of the vertical distance from each road section is an independent and identically distributed random variable, and the trajectory point is obtained.
Figure DEST_PATH_IMAGE231
road segments in the set of neighbor road segments
Figure DEST_PATH_IMAGE232
The node event occurrence probability of
Figure DEST_PATH_IMAGE233
Distance from the track point to the road segment
Figure DEST_PATH_IMAGE234
is related to the vertical distance,
Figure DEST_PATH_IMAGE235
The smaller the probability of occurrence of the node event
Figure DEST_PATH_IMAGE236
bigger, get track points
Figure DEST_PATH_IMAGE237
In the set of neighboring road segments, the other respective road segments k
Figure DEST_PATH_IMAGE238
Correlation, calculated by formula
Figure DEST_PATH_IMAGE239
:
Figure DEST_PATH_IMAGE240
；
Figure DEST_PATH_IMAGE240
; 其中，
Figure DEST_PATH_IMAGE241
表示节点事件发生概率，
Figure 496007DEST_PATH_IMAGE223
表示轨迹点
Figure 379650DEST_PATH_IMAGE220
的所有近邻路段中最远的近邻路 段的垂直距离，
Figure 463143DEST_PATH_IMAGE219
表示任一轨迹点
Figure 104340DEST_PATH_IMAGE220
与所述近邻路段集合中任一路段
Figure 790537DEST_PATH_IMAGE226
的中心点的垂直距 离，
Figure DEST_PATH_IMAGE242
表示任一轨迹点
Figure 627822DEST_PATH_IMAGE220
与所述近邻路段集合中其他路段
Figure 272430DEST_PATH_IMAGE126
的中心点的垂直距离，
Figure DEST_PATH_IMAGE243
表示第
Figure DEST_PATH_IMAGE116A
个轨迹点，
Figure 276289DEST_PATH_IMAGE117
表示第
Figure 641542DEST_PATH_IMAGE118
个路段，
Figure 906302DEST_PATH_IMAGE120
表示第
Figure 456232DEST_PATH_IMAGE121
个路段； in,
Figure DEST_PATH_IMAGE241
represents the probability of node event occurrence,
Figure 496007DEST_PATH_IMAGE223
Represents a track point
Figure 379650DEST_PATH_IMAGE220
The vertical distance of the farthest adjacent road segment among all the adjacent road segments of ,
Figure 463143DEST_PATH_IMAGE219
represents any track point
Figure 104340DEST_PATH_IMAGE220
with any road segment in the set of adjacent road segments
Figure 790537DEST_PATH_IMAGE226
The vertical distance from the center point of ,
Figure DEST_PATH_IMAGE242
represents any track point
Figure 627822DEST_PATH_IMAGE220
and other road segments in the set of adjacent road segments
Figure 272430DEST_PATH_IMAGE126
The vertical distance from the center point of ,
Figure DEST_PATH_IMAGE243
means the first
Figure DEST_PATH_IMAGE116A
track points,
Figure 276289DEST_PATH_IMAGE117
means the first
Figure 641542DEST_PATH_IMAGE118
road section,
Figure 906302DEST_PATH_IMAGE120
means the first
Figure 456232DEST_PATH_IMAGE121
a road segment; 所述计算所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件之间的转移概率
Figure DEST_PATH_IMAGE244
包括： The calculation of the matching probability between the trajectory line and the road network is the transition probability between the node events in the network TRN
Figure DEST_PATH_IMAGE244
include: 通过利用路网数据中各个路段间的空间关联关系，定义得到所述近邻路段集合中任意 两条路段
Figure 212966DEST_PATH_IMAGE125
与
Figure 772124DEST_PATH_IMAGE126
之间的转移阻抗
Figure DEST_PATH_IMAGE245
，其取值区间为
Figure 294984DEST_PATH_IMAGE130
，设定路网上路段
Figure 687919DEST_PATH_IMAGE131
与路段
Figure 853322DEST_PATH_IMAGE132
之 间的最短距离长度为
Figure 622694DEST_PATH_IMAGE134
，
Figure DEST_PATH_IMAGE246
的赋值公式如下： By using the spatial relationship between each road segment in the road network data, any two road segments in the set of adjacent road segments are defined and obtained
Figure 212966DEST_PATH_IMAGE125
and
Figure 772124DEST_PATH_IMAGE126
transfer impedance between
Figure DEST_PATH_IMAGE245
, whose value range is
Figure 294984DEST_PATH_IMAGE130
, set the road network segment
Figure 687919DEST_PATH_IMAGE131
with road sections
Figure 853322DEST_PATH_IMAGE132
The shortest distance between
Figure 622694DEST_PATH_IMAGE134
,
Figure DEST_PATH_IMAGE246
The assignment formula is as follows:
Figure DEST_PATH_IMAGE247
；
Figure DEST_PATH_IMAGE247
; 其中，
Figure DEST_PATH_IMAGE248
表示任意两条路段
Figure 737412DEST_PATH_IMAGE140
与
Figure 238932DEST_PATH_IMAGE142
之间的转移阻抗，
Figure 157209DEST_PATH_IMAGE144
表示第
Figure 727343DEST_PATH_IMAGE144
个路段，
Figure 758884DEST_PATH_IMAGE145
表示第
Figure 759201DEST_PATH_IMAGE145
个 路段，
Figure 633617DEST_PATH_IMAGE147
表示路网上路段
Figure 10371DEST_PATH_IMAGE144
与路段
Figure 161998DEST_PATH_IMAGE145
之间的最短距离； in,
Figure DEST_PATH_IMAGE248
represents any two road segments
Figure 737412DEST_PATH_IMAGE140
and
Figure 238932DEST_PATH_IMAGE142
transfer impedance between,
Figure 157209DEST_PATH_IMAGE144
means the first
Figure 727343DEST_PATH_IMAGE144
road section,
Figure 758884DEST_PATH_IMAGE145
means the first
Figure 759201DEST_PATH_IMAGE145
road section,
Figure 633617DEST_PATH_IMAGE147
Represents a road network segment
Figure 10371DEST_PATH_IMAGE144
with road sections
Figure 161998DEST_PATH_IMAGE145
the shortest distance between; 当轨迹点
Figure DEST_PATH_IMAGE249
在路段
Figure DEST_PATH_IMAGE250
上时，后一个轨迹点
Figure DEST_PATH_IMAGE251
在路段
Figure DEST_PATH_IMAGE252
上的概率为所述节点事件之间 的转移概率
Figure DEST_PATH_IMAGE253
，得到所述节点事件之间的转移概率与路段间的转移阻抗
Figure DEST_PATH_IMAGE254
相关，所 述转移阻抗越小则所述节点事件之间的转移概率越大，用下述公式进行计算： when the track point
Figure DEST_PATH_IMAGE249
on the road
Figure DEST_PATH_IMAGE250
up, the next track point
Figure DEST_PATH_IMAGE251
on the road
Figure DEST_PATH_IMAGE252
The probability on is the transition probability between the node events
Figure DEST_PATH_IMAGE253
, obtain the transition probability between the node events and the transition impedance between the road segments
Figure DEST_PATH_IMAGE254
Correlation, the smaller the transition impedance, the greater the transition probability between the node events, which is calculated by the following formula:
Figure DEST_PATH_IMAGE255
；
Figure DEST_PATH_IMAGE255
; 其中，
Figure DEST_PATH_IMAGE256
表示节点事件之间的转移概率，
Figure DEST_PATH_IMAGE257
表示路段间的转移阻抗,
Figure 628489DEST_PATH_IMAGE166
表示求和 时第
Figure DEST_PATH_IMAGE168A
个转移阻抗数据； in,
Figure DEST_PATH_IMAGE256
represents the transition probability between node events,
Figure DEST_PATH_IMAGE257
represents the transfer impedance between road segments,
Figure 628489DEST_PATH_IMAGE166
Indicates the time of summation
Figure DEST_PATH_IMAGE168A
transfer impedance data; 所述构建自行车轨迹与路段之间的马尔可夫链包括：The construction of a Markov chain between bicycle trajectories and road segments includes: 通过设置轨迹线与路网之间的匹配概率网络T-R-N中的网络节点集合
Figure 599987DEST_PATH_IMAGE170
中的每一个节 点为初始节点，网络节点集合
Figure 514854DEST_PATH_IMAGE172
中的任一节点为终到节点，利用所述轨迹线与路网之间的 匹配概率网络T-R-N中的有向连接弧，遍历整个网络，所述初始节点到所述终到节点间的任 意一条节点序列对应一项自行车轨迹线匹配方案，构建自行车轨迹与路段之间的马尔可夫 链； The set of network nodes in the network TRN by setting the matching probability between the trajectory line and the road network
Figure 599987DEST_PATH_IMAGE170
Each node in is an initial node, a collection of network nodes
Figure 514854DEST_PATH_IMAGE172
Any node is the final node, and the directed connection arc in the matching probability network TRN between the trajectory line and the road network is used to traverse the entire network, and any one between the initial node and the final node is used. The node sequence corresponds to a bicycle trajectory line matching scheme, and a Markov chain between bicycle trajectory and road segment is constructed; 在所述轨迹线与路网之间的匹配概率网络T-R-N上搜索最大组合概率的自行车轨迹与路段之间的马尔可夫链并实现轨迹线的最优匹配结果包括：Searching the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability on the matching probability network T-R-N between the trajectory line and the road network and realizing the optimal matching result of the trajectory line includes: 所述轨迹线与路网之间的匹配概率网络T-R-N中的节点事件发生概率
Figure DEST_PATH_IMAGE258
和所述轨迹线 与路网之间的匹配概率网络T-R-N中的节点事件之间的转移概率
Figure 845953DEST_PATH_IMAGE183
通过公式计算 得到自行车轨迹与路段之间的马尔可夫链的组合概率
Figure DEST_PATH_IMAGE259
，得到最大组合概率的自行车轨 迹与路段之间的马尔可夫链： The matching probability between the trajectory line and the road network The node event occurrence probability in the network TRN
Figure DEST_PATH_IMAGE258
and the matching probability between the trajectory line and the road network and the transition probability between node events in the network TRN
Figure 845953DEST_PATH_IMAGE183
The combined probability of the Markov chain between the bicycle track and the road segment is calculated by the formula
Figure DEST_PATH_IMAGE259
, get the Markov chain between the bicycle trajectory and the road segment with the maximum combined probability: 公式：
Figure DEST_PATH_IMAGE260
； formula:
Figure DEST_PATH_IMAGE260
; 其中，
Figure DEST_PATH_IMAGE261
表示节点事件发生概率，
Figure DEST_PATH_IMAGE262
表示节点事件之间的转移概率，
Figure 204385DEST_PATH_IMAGE185
表 示自行车轨迹的路段马尔可夫连的组合概率； in,
Figure DEST_PATH_IMAGE261
represents the probability of node event occurrence,
Figure DEST_PATH_IMAGE262
represents the transition probability between node events,
Figure 204385DEST_PATH_IMAGE185
the combined probability of the Markov connection representing the segment of the bicycle trajectory; 根据所述轨迹线的路段匹配概率网络T-R-N，通过网络路径搜索算法搜索出所述最大组合概率的自行车轨迹与路段之间的马尔可夫链，其所对应的路段序列
Figure 53392DEST_PATH_IMAGE187
为轨迹线的最优匹配结果。According to the road segment matching probability network TRN of the trajectory line, the Markov chain between the bicycle trajectory with the maximum combined probability and the road segment is searched through the network path search algorithm, and the corresponding road segment sequence
Figure 53392DEST_PATH_IMAGE187
is the optimal matching result of the trajectory line. 3.一种电子设备，其特征在于，包括处理器、存储器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述计算机程序被所述处理器执行时实现如权利要求1所述的针对有噪声且参数不明的自行车轨迹数据的路网匹配方法。3. An electronic device, characterized in that it comprises a processor, a memory and a computer program stored on the memory and can be run on the processor, the computer program being executed by the processor to achieve as claimed in the right The road network matching method for the bicycle trajectory data with noise and unknown parameters according to claim 1. 4.一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储计算机程序，所述计算机程序被处理器执行时实现如权利要求1所述的针对有噪声且参数不明的自行车轨迹数据的路网匹配方法。4. A computer-readable storage medium, characterized in that, a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method according to claim 1 for noise and unknown parameters is realized. A road network matching method for bicycle trajectory data.
CN202110827926.8A 2021-07-22 2021-07-22 Road network matching method for noisy and unidentified parameter bicycle track data Active CN113282699B (en)

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