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WO2018122803A1 - Procédé de détection d'anomalie de trafic routier intelligent - Google Patents

Procédé de détection d'anomalie de trafic routier intelligent Download PDF

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Publication number
WO2018122803A1
WO2018122803A1 PCT/IB2017/058533 IB2017058533W WO2018122803A1 WO 2018122803 A1 WO2018122803 A1 WO 2018122803A1 IB 2017058533 W IB2017058533 W IB 2017058533W WO 2018122803 A1 WO2018122803 A1 WO 2018122803A1
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WIPO (PCT)
Prior art keywords
time
data
traffic
sub
model
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PCT/IB2017/058533
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English (en)
Chinese (zh)
Inventor
杜豫川
邓富文
常光照
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Shanghai Intelligent Transportation Co Ltd
Tongji University
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Shanghai Intelligent Transportation Co Ltd
Tongji University
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Priority to GBGB1909407.7A priority Critical patent/GB201909407D0/en
Priority to CN201780050719.8A priority patent/CN110168520A/zh
Publication of WO2018122803A1 publication Critical patent/WO2018122803A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination

Definitions

  • the invention belongs to the technical field of traffic detection.
  • the present invention relates to a method for real-time detection of urban road traffic anomalies.
  • the on-board GNSS positioning device of the floating car Through the on-board GNSS positioning device of the floating car, the spatial position information of different time can be obtained.
  • the RN model is trained based on the historical data of the specific speed and time of the journey; the predicted value and real time according to the RNN model The difference between the actual values of the journey speed can effectively identify urban road traffic anomalies.
  • Background technique
  • Traffic anomaly detection is an important part of urban traffic management and one of the core functions of intelligent transportation systems. Traffic anomalies mainly include traffic accidents, vehicle dumping, falling objects, damage or malfunction of road traffic facilities, and other special events that cause traffic flow disturbances. Such incidents are prone to traffic congestion, reduced road capacity, and severely affect the normal operation of the entire road traffic system. Through traffic anomaly detection, traffic managers can timely understand traffic anomaly information and take appropriate inducement and control measures to reduce the adverse effects of traffic anomalies.
  • Traffic anomaly detection can be divided into manual mode and automatic mode.
  • Manual methods include patrol cars, emergency telephone reporting, and video surveillance. Due to the human and material resources and poor real-time performance, traffic management needs cannot be met.
  • the automatic method relies on the automatic event detection (AID, Automated Incidence Detection) algorithm.
  • AID Automated Incidence Detection
  • the basic principle is to identify traffic anomalies by detecting changes in road traffic at different locations.
  • AID algorithms include pattern recognition algorithms (such as Califorma algorithm, Monica algorithm), statistical prediction algorithms (such as exponential smoothing, Kalman filtering), traffic flow model algorithms (such as McMaster algorithm), and intelligent recognition algorithms. (such as artificial neural networks, fuzzy logic algorithms).
  • the invention utilizes the trajectory data returned by the taxi and the bus GNSS positioning device to establish a historical traffic state database and a real-time traffic state database, and analyzes the traffic anomaly events by analyzing the difference of the traffic flow characteristics reflected by the two.
  • the method has the characteristics of good real-time performance, parallel processing, high recognition rate and low requirements for detection facilities, and is suitable for detecting urban road traffic anomalies in a data environment with real-time floating vehicle positioning data.
  • a US patent application, US 20160148512 discloses a composition principle and implementation method of a traffic anomaly detection and reporting system.
  • the system consists of a sensor, a communication module, a mobile processing module, and a user interaction module.
  • the sensor is used to collect relevant data around the vehicle;
  • the communication module is used for transmitting the vehicle data and receiving data of the surrounding vehicle;
  • the mobile processing module is for processing and analyzing the data of the relevant vehicle in a certain area and generating a traffic event report; user interaction
  • the module is able to provide traffic incident reports like a user.
  • the scheme is a traffic anomaly detection technology based on the vehicle and vehicle communication network, which can use various types of information collected by sensors to identify abnormal events.
  • the sensor and the communication unit need to be separately installed and debugged, the implementation is difficult; the processing capacity of the mobile processing unit is limited; and the mobile and fixed message receiving end is required, and the system itself has a failure probability and the reliability is not good.
  • a Chinese patent application, CN 104809878 A discloses a method for detecting abnormal state of urban road traffic using bus GPS data.
  • the scheme obtains the link delay time index according to the GPS historical data, obtains the instantaneous speed, the cycle average speed, the weighted moving average speed and the multi-vehicle average speed according to the current GPS data, and uses the gauge variable analysis algorithm to detect the abnormality.
  • This program does not need new Increase inspection facilities and facilitate implementation.
  • the characterization of the traffic situation is too simplistic, and it is impossible to analyze the characteristics and causes of traffic anomalies. There is no basis for the division of traffic scenarios, and the influence of weather and other factors on traffic situation changes cannot be considered. Summary of the invention
  • Floating car Also known as the probe car. Refers to buses and taxis that have on-board positioning devices and are driving on city roads.
  • GNSS Global Navigation Satellite System. Including GPS, GLONASS, GALILEO and Beidou satellite navigation systems.
  • Space-time sub-zone A zone divided by two dimensions, time and space, reflected in a certain space within a certain period of time. Divide a day into several time segments, such as 0:00-0: 10, 0: 10-0:20..., called the time sub-region; divide the implementation area of urban road traffic anomaly detection into several spatial segments, for example Longitude 121.58° E-121.59 0 E, latitude 31.16° N-31.17° N, that is, the spatial sub-region; the space-time segment formed by the intersection of any one time sub-region and any one spatial sub-region, called the spatiotemporal sub-region For example, the longitude of 121.58° E-121.59 0 E, latitude 31.16° N-31.17 0 N is a time-space segment of 0:00-0:10.
  • Historical trajectory data is trajectory data accumulated over a long period of time and stored in a database. Historical trajectory data is dynamically changing data that needs to be updated in a timely manner and periodically reprocessed and analyzed to ensure the accuracy of historical traffic feature extraction. The data for each time-space sub-area can be processed in parallel to increase efficiency. In the present invention, it may be simply referred to as historical data.
  • Real-time trajectory data is a trajectory data set within a time zone that is closest to the current time. In the present invention, it may be simply referred to as real-time data.
  • Traffic situation A general term for the comprehensive situation of traffic operations within a certain period of time and within a certain space.
  • Traffic anomalies traffic flow disturbances caused by traffic accidents, vehicle dumping, truck falling, road traffic facilities damage or malfunctions.
  • Abnormal traffic severity The severity of traffic flow disorder is the difference in traffic flow characteristics after traffic flow and traffic anomalies in normal conditions.
  • Traffic Anomaly Index A measure of the severity of traffic. The range is 0 ⁇ 10. The larger the value, the more serious the traffic anomaly.
  • Traffic environment The sum of all external influences and forces acting on road traffic participants. This includes road conditions, transportation facilities, landforms, meteorological conditions, and traffic activities of other transportation participants.
  • Map Matching The process of associating geographic coordinates with a city road network.
  • Peak hourly traffic The maximum hourly traffic flow in a city's road section.
  • Response variable A variable that changes according to an independent variable, also called a dependent variable.
  • RNN Recurrent Neural Network is an artificial neural network with nodes connected in a loop. The internal state of this network can show dynamic timing behavior. It can use its internal memory to process input sequences of arbitrary timing. .
  • Elman-RNN An RNN network structure, see A RNN that learns to count) ⁇
  • Training process The process of optimizing neural network parameters through iterative calculations to reduce the model error of the neural network on the training data set.
  • the object of the present invention is to establish a scheme based on a floating vehicle trajectory recording system, using historical GNSS positioning data and real-time GNSS positioning data, combined with traffic environment information to identify road traffic anomalies.
  • the present invention provides the following technical solutions:
  • the premise of the implementation of the present invention is: a floating car (taxis, bus, etc.) equipped with a GNSS track recorder; with large-scale storage, A data center that computes, real-time task processing capabilities.
  • the scope of application of the present invention is: Urban roads (including ground roads and elevated roads) through which the above-mentioned floating vehicles pass.
  • the implementation steps of the present invention include:
  • the time range can be set to all day, that is, 0:00-24:00; it can also be set to a specific time period. For example, to detect the traffic abnormal time during the period from 17:00 to 20:00, the time will be detected.
  • the range is set from 17:00 to 20:00.
  • the spatial scope can be set as a certain city area according to the administrative division, such as Beijing, Shanghai, Huangpu District, etc. It can also be set as a certain urban functional area according to the urban spatial structure, such as a central business district and industrial area of a certain city.
  • the establishment of the spatiotemporal sub-area refers to dividing the detected time range into a number of smaller time segments, and dividing the detected spatial range, that is, the implementation area of the urban road traffic anomaly detection, into a plurality of smaller spatial segments.
  • a variety of empirical division methods can be used, including equidistant space-time division method and non-equidistant space-time division method.
  • GNSS Global Navigation Satellite System Positioning System
  • GPS Global Positioning System
  • GLONASS Global Navigation Satellite System
  • GALILEO Global Navigation Satellite System
  • Beidou satellite navigation system It also includes QZSS in Japan and IRNSS in India.
  • QZSS Global Positioning System
  • IRNSS International Radio Network Service Set
  • Such as regional navigation and positioning systems, as well as satellite positioning enhancement systems such as WASS in the United States and MSAS in Japan.
  • GNSS positioning equipment such as taxis, buses, freight cars, private cars, etc.
  • urban taxis are often used as floating vehicles as data sources for traffic anomaly detection systems.
  • the collected GNSS positioning information contains some unreasonable information.
  • anomalies include: data that falls outside the time and space of detection, and spatial position jumps that are clearly out of reasonable range.
  • space position jump beyond the reasonable range is illustrated by the following example. If the positioning point uploaded by a floating vehicle positioning device is recorded as A at 10:30:00 on a certain day, the positioning point uploaded by the floating vehicle positioning device at time 10:30:30 is recorded as B, the distance between position A and position B. It is 1500 meters, then the speed of the floating car is calculated to be at least 180km/h, which is beyond the common sense, so it is an abnormal spatial position jump, which should be eliminated in data processing.
  • GNSS positioning data After pre-processed GNSS positioning data, it is necessary to combine the urban road network data, map the GNSS positioning points to the city map through the map matching algorithm, establish the matching relationship between the positioning points and the road segments, and correct the error caused by the positioning drift.
  • the electronic maps of various geographical regions are relatively detailed.
  • Such electronic maps can be derived from the city's geographic information system, and of course can also be derived from other methods and routes.
  • These electronic maps detail the urban road information, and several sections can be obtained by dividing.
  • the anchor points to the road segments By means of distance, angle, etc., the positioning information is matched to the actual geographical environment. 4)
  • the path of the vehicle may not be unique given a set of starting and ending points.
  • the complex urban traffic network consists of several sections, which are numbered, for example, Ll, L2, etc. Roads may have two different directions of travel. In this case, two different directions of travel should be represented as two different sections, given different sections.
  • the intersection of the road segments in the urban road network can usually be used. Knowing the path of a floating car, it is now necessary to select the same path as the floating car path from the path information that has been sent by other floating cars, so as to obtain the same path group between the starting point and the ending point.
  • the positioning data of the floating car includes information such as position coordinates, instantaneous vehicle speed, and recording time.
  • data sampling refers to screening part of the data from all floating car data for subsequent analysis and processing, and the screening is based on the computing power of the data center and Pre-proposed accuracy requirements were made. Different data sampling methods can be used based on different calculation capabilities and accuracy requirements.
  • the computing power of the data center when the computing power of the data center is strong and the accuracy of the detection is high, all the floating vehicle positioning data can be treated as a processing object, and comprehensive processing analysis is performed; and when the computing power of the data center is limited, it is assumed
  • the current data center can process 500 data for each spatial sub-area within 1 minute, but the actual situation is that in 2000, each floating sub-area can generate 2000 floating-vehicle positioning data, so it can be from 2000 data. Randomly extract 500 data for analysis, so as to obtain processing results with limited accuracy within the computing power of the data center.
  • the urban road traffic anomaly detection method based on floating car data proposed in this patent uses the travel speed as the basis for urban road traffic anomaly detection. Therefore, data sampling refers to sampling the speed of the journey.
  • the so-called historical trajectory data refers to the floating vehicle trajectory data accumulated in long-term urban road traffic operations.
  • an urban road traffic feature model can be established to reflect the general characteristics of urban traffic operations.
  • the urban road traffic feature model mentioned here can refer to certain specific indicators, such as average speed, weighted average speed, etc.; it can also refer to various statistical models, such as the probability distribution of travel speed.
  • a single indicator was used to indicate the traffic characteristics of a certain section or area (such as the historical average speed).
  • this patent proposes to train the RNN model with the variation of traffic characteristic variables for each spatiotemporal sub-region.
  • the R N model can give the predicted value of the traffic characteristic variable at the next moment when the real value of the traffic characteristic variable is given for some time period.
  • the traffic characteristic variables that can be collected include the travel speed and travel time. This patent uses the travel speed to reflect the traffic characteristics. Therefore, the traffic characteristic variable refers to the travel speed.
  • the so-called real-time trajectory data refers to the trajectory data of the floating car in traffic operation in a period of time not far from the current time.
  • real-time floating car trajectory data you can grasp the dynamics of traffic characteristics and reflect the current characteristics of current traffic operations.
  • This patent uses the travel speed of the current space-time sub-zone to describe the current traffic characteristics.
  • Dennngng system state anomaly detection
  • the idea of system state anomaly detection was first proposed by Dennngng, that is, by monitoring the abnormality of the system used in the system audit record, it is possible to detect an event that violates security and may cause a system abnormality.
  • Dennrng's model is independent of any particular system, application environment, system vulnerability, and fault type, and is therefore a general anomaly detection model.
  • the model consists of five parts: subject, object, audit record, outline, exception record and activity rule.
  • a contour is a body that is represented by a metric and a statistical model relative to the object. Normal behavior.
  • Dennrng's model defines three metrics, namely event counter, interval timer, resource measurer, and proposes five statistical models, namely, operational model, mean and standard deviation models, multivariate models, Markov process models, and Time series model.
  • the model proposed by Denning establishes the statistically-based normal behavioral feature profile of the system subject through the analysis of the system audit data. When the detection, the audit data in the system is compared with the normal behavioral feature profile of the established subject. Exceeding a certain threshold is considered an abnormal event.
  • This model lays the foundation for anomaly detection, and many anomaly detection methods and systems developed in the future are developed on the basis of it.
  • user behavior data is divided into two categories according to certain statistical criteria: abnormal behavior and normal behavior.
  • the statistical-based method has certain difficulties in extracting and abstracting the audit instance, it may cause large errors, and must rely on some probability distribution hypotheses.
  • the artificial neural network is introduced. Clustering method.
  • the artificial neural network has the self-learning self-adaptive ability to train the neural network with sample points representing the normal user behavior. Through repeated learning, the neural network can extract the normal user or system activity patterns from the data and encode them into the network structure.
  • the audit data can be judged whether the system is normal by learning a good neural network. Because the anomaly evaluation criterion has certain ambiguity, the fuzzy evidence theory is introduced into the anomaly. For example, an intrusion detection framework model based on fuzzy expert system is established, which can better reduce the false alarm rate and false alarm rate.
  • This patent proposes an anomaly detection scheme based on cyclic neural network.
  • it is an anomaly detection scheme using RNN (Circular Neural Network).
  • RNN Chemical Neural Network
  • the real-time trajectory data is input into the RN model based on historical data, and the predicted values are obtained, and then compared. The difference between the predicted value and the true value.
  • the program uses a deep learning method that automatically updates the model over time and has strong adaptive capabilities.
  • the severity of traffic anomalies should be released to the public in a clear and concise manner to avoid possible congested areas and improve the efficiency of urban traffic.
  • the severity of the abnormal condition is characterized by the traffic anomaly index, ranging from 0-10, where 0 means no anomaly and 10 means a height anomaly.
  • the location of the anomaly is projected onto the electronic map and published publicly through the smart mobile device APP or the like.
  • the evaluation of system performance refers to the evaluation of the accuracy of traffic abnormal state detection, and its evaluation indicators include false positive rate and false negative rate. The lower the false positive rate and the false negative rate, the better the performance of the system.
  • the division of the spatiotemporal sub-area may specifically adopt the following method:
  • the segment size of the time dimension is determined.
  • the time segment span is a fixed value, usually 30 mm is taken as a time segment;
  • the segment size of the spatial dimension is determined, and the spatial segment span is a fixed value, and a spatial grid of 200 m ⁇ 200 m is usually taken as a spatial segment.
  • Non-equidistant space-time division method For urban central areas where the road network density is greater than 2km/km 2 or the peak hour flow is greater than 1000 vehicles/hour, take 30min time segments and 200m X 200m space segments. For road network density less than 2km/km 2 or peak hour traffic is less than 1000 sub-urban suburbs, taking 30min time segments and 400mX 400m space segments.
  • the step 3) specifically includes the following steps:
  • each grid area contains several road segments, and the set of these road segments is represented as R S , and each road segment in the set of the road segments is represented as ij, and each road segment is given Numbering;
  • the matching scheme includes:
  • the step 5 may adopt one of the following methods:
  • the total travel speed data of each sub-floating vehicle in a time and space sub-region constitutes the whole.
  • the distance between the first and second GNSS anchor points, ..., the distance between the -1 and the GNSS anchor points, ... t n is the space-time sub-region 1 , ..., the first time stamp of the GNSS anchor point; the data in each spatiotemporal sub-region is not filtered to form a set ⁇ for subsequent processing.
  • Time-smooth sampling plan for speed information Specify the length of the time segment and the upper limit of the number of segments of the same time; search for the velocity data in each time segment in a time-space sub-region. If the number of velocity data in the time segment exceeds the upper limit, the data of the upper limit is randomly added to the data to be processed. sample.
  • the distance between the GNSS positioning points is the first time in the space-time sub-region, ..., the time stamp of the first GNSS positioning point; the specified time segment length t p , the upper limit of the number of segments of the same time; ⁇ ⁇ ; speed data within a search area at the time of each temporal sub-time segment, the time segments if the data rate exceeds the upper limit number of pieces; ⁇ «, randomly; ⁇ ⁇ was added and ⁇ of data for subsequent processing.
  • the step 6) specifically includes the following steps:
  • Input layer According to the characteristics of the neural network, the input layer is each instance of the historical data to be trained, since the input data here is a one-dimensional data stream, that is, the travel speed data in the space sub-area is formed in the time dimension. Time series data, therefore, the number of input layer neurons and the number of output layer neurons are set to 1.
  • Implicit layer In the design of neural network, the number of hidden layers has not been determined. Generally, a large number of experiments are needed to determine the number of hidden neurons in the network model. The recommended value is 5 ⁇ 8.
  • Output layer The purpose of establishing a neural network is to output a predicted value, that is, to predict the value of the next moment by the values of the first two moments.
  • the output layer output value is the predicted value, so the number of output layers is set to 1.
  • Context layer In Elman-Network, the context layer is used to store the output of the hidden layer at the previous moment, so the number of context layer neurons is the same as the number of hidden layers.
  • Offset node The input layer and output layer each contain an offset node, and the initial value is set to 0.
  • the Elman network structure contains two parameters, namely the number of hidden layer neurons and the number of layers of the context layer. The number of neurons in the hidden layer The better results are selected through multiple experiments.
  • the recommended setting is 5 ⁇ 8. Since a context layer saves the hidden layer output value at the previous time, it is recommended to set it to 1.
  • the parameter learning rate needs to be set when training the model using the back propagation algorithm.
  • the size of the learning rate determines the degree of change of the weight in the iterative process of the neural network.
  • the large learning rate can make the algorithm converge quickly, but it may fall into the local solution.
  • the small learning rate makes the convergence of the algorithm slower, but it can guarantee convergence. To the global minimum, therefore, the value of the learning rate should generally be chosen to ensure the performance and stability of the model.
  • the recommended setting is 0.01 to 0.8.
  • the initial temperature, the termination temperature, and the number of iterations of each temperature value need to be set.
  • the value of the initial temperature has an important influence on the performance of the algorithm. The larger the initial temperature, the more iterations of the algorithm, the greater the possibility of convergence to the minimum, but the greater the time cost. Similarly, when the initial temperature is low, the performance of the algorithm will be affected, but the time spent will be less. In practical applications, the initial temperature setting needs to be selected by repeated trial and error.
  • the termination temperature is the lower limit temperature set by the termination algorithm during the temperature drop. The more iterations of each temperature value, the more the number of possible solutions, and the greater the likelihood of convergence to the global minimum. It is recommended to set the initial temperature to 10 5 and the termination temperature to 10 - 2 to set the number of iterations per temperature. Is 100.
  • the steps for model training are as follows:
  • Greedy Strategy If the training error rate does not improve, the recovery weight and error rate are the last values.
  • Hybrid strategy If the error rate of the model does not decrease after this training, or if the magnitude of the decline is less than the set minimum, then the simulated annealing algorithm is used for training.
  • a training of the simulated annealing algorithm the general steps are as follows: First calculate the error score of the current model, then add a random number " ⁇ « to the ownership value and offset value of the current model, get new weights and offset values, where:
  • Random is a random number
  • startTemp is the initial temperature
  • temp is the current temperature.
  • the updated model error score is calculated. If the new error score is smaller than the current error score, indicating that the new weight improves the performance of the model, save the new one. Weight, otherwise discard; multiply the current temperature by a fixed ratio ratio to lower the temperature:
  • stopTemp is the termination temperature and cycles is the number of iterations of a training session. Repeat the above process cycles.
  • step 7) Stop condition judgment: If the degree of improvement to the model is less than the minimum value is greater than the set threshold, the algorithm terminates. Repeat steps 63), 64), 65;) until the algorithm terminates, and the trained R N model is obtained. See Figure 6 for the training process.
  • the step 7) may specifically adopt one of the following methods:
  • the step 8) specifically includes the following steps:
  • the step 9) specifically includes the following steps:
  • the step 10) specifically includes the following steps:
  • the total number of missed events per unit time is the total number of false positive events per unit time.
  • the total number of abnormal times that actually occurred during the unit time is the total number of missed events per unit time.
  • GNSS trajectory data detect historical traffic state changes through historical traffic feature extraction and real-time traffic situation analysis, and realize real-time, low-cost, intelligent urban road traffic anomaly events Detection
  • the urban road traffic anomaly detection technology based on floating car data proposed by the present invention can realize the detection of abnormal events with high accuracy, the detection rate exceeds 90%, and the false negative rate is less than 15%.
  • the false alarm rate is lower than 20%, and it has achieved good detection results, and can be applied to urban traffic intelligent management and service.
  • FIG. 1 shows a schematic diagram of the components and basic principles of the present invention
  • Figure 2 is a schematic view showing the overall flow of the present invention in the implementation process
  • FIG. 3 is a schematic diagram showing an implementation manner of a fast map matching algorithm of the present invention.
  • FIG. 4 is a block diagram showing the structure of an RNN in the present invention.
  • FIG. 5 is a view showing the detailed structure of the RNN in the present invention.
  • Figure 6 shows a schematic flow chart of performing R N training
  • Fig. 7 is a flow chart showing the abnormality detection of the present invention by comparing the RNN model with real data.
  • the overall system architecture of the present invention includes: an onboard GNSS track recorder mounted on a floating vehicle, a data center, a GNSS satellite, and a communication system.
  • the GNSS here includes GPS, GLONASS, GALILEO, Beidou, IRNSS, QZSS and any similar navigation satellite positioning system.
  • GNSS track recorders equipped with floating cars, buses, etc. record the position information of the vehicle at various points in time at a certain sampling frequency / (general requirements of 0.1 Hz), and pass the GPRS mobile communication network (also can be used) Wireless network communication technologies such as WCDMA and TD-LTE, but the cost will be increased accordingly)
  • the location information will be sent to the data center in real time.
  • the data center establishes a historical road traffic characteristic database through data preprocessing, data fusion, and through a specific algorithm; establishes a real-time traffic feature database for the recently received real-time data; and determines whether the current traffic feature is abnormal through the mapping relationship between the historical database and the real-time database And visualize the display through the processing terminal and generate a traffic anomaly event report.
  • the overall process of the scheme is shown in Figure 2, including the acquisition and storage of GNSS trajectory data, the establishment of spatiotemporal sub-areas, historical traffic feature extraction, real-time traffic feature extraction, and anomaly identification.
  • Collecting and storing GNSS trajectory data is the data foundation of the whole scheme. Due to the huge amount of data, a distributed storage scheme should be adopted.
  • the basic assumption of the establishment of a spatio-temporal sub-area is that it has the same traffic characteristics in a particular area and a specific time period. This assumption is generally applicable after long-term observation.
  • Historical traffic feature extraction the principle is to use the GNSS trajectory data to calculate the travel speed, use a large number of historical travel speed data in the same space-time sub-area, train the Elman-RNN model, and use the neural network model to characterize the change of traffic characteristics.
  • Real-time traffic feature extraction the principle is to process and analyze the travel speed data in the current time period, and obtain the traffic characteristic statistics.
  • the anomaly identification is to compare the real value of the predicted range obtained by the Elman-RNN model of the historical trip speed training to determine whether a traffic anomaly event occurs.
  • Embodiment 1 According to the combination of the embodiments of the invention, the implementation is given below. Embodiment 1
  • Step 11 Determine the segment size of the time dimension by using the equidistant space-time division method.
  • the time segment span is a fixed value, usually 30 mm.
  • the spatial segment span is a fixed value, usually takes a spatial grid of 200mX200m as a spatial segment.
  • Step 12 Perform data preprocessing to perform data cleaning, data integration, data conversion, and data reduction on the GNSS positioning data to improve the structural degree of the data.
  • Step 13 Divide the space area to be processed into a grid of a certain size, and the range of each grid area can be expressed as
  • ⁇ complete the match; if it does not satisfy
  • the projection line equation is: Solve the projected coordinates P as:
  • the anchor point is matched to the spatio-temporal sub-area in combination with the timestamp data of the coordinates of the positioning point.
  • Step 14 The total vehicle speed data of each sub-floating vehicle in a time and space sub-region constitutes the whole. Calculate each time space sub-region
  • Step 15 Establish an Elman-RNN neural network consisting of an input layer, an implicit layer, an output layer, a context layer, and a bias node.
  • the number of neurons in the input layer is set to 1, and the number of neurons in the hidden layer is set to 5.
  • the number of neurons in the output layer is set to 1, the number of neurons in the context layer is the same as the number of hidden layers, and is also set to 5, and the initial value of the offset node is set to 0; the initial parameters of each component are set, where
  • the learning rate is set to 0.1
  • the initial temperature in the simulated annealing module is set to 10 5
  • the termination temperature is set to 10 - 2
  • the number of iterations per temperature is set to 100.
  • Step 17 sorting the time series data subscripts to be processed by the abnormal point detection in ascending order
  • Step 18 Normalize the difference in velocity distribution of each spatiotemporal sub-region to a normalized value of 0 to 1 ⁇ ⁇ :
  • Step 21 Using the equidistant space-time division method, determining the segment scale of the time dimension, the time segment span is a fixed value, usually taking 30 mm as a time segment; determining the segment scale of the spatial dimension, the spatial segment span is a fixed value, usually taking 200m ⁇ 200m
  • the spatial grid acts as a spatial fragment.
  • Step 22 Perform data preprocessing to perform data cleaning, data integration, data conversion, and data reduction on the GNSS positioning data to improve the structural degree of the data.
  • the anchor point is matched to the spatio-temporal sub-area in combination with the timestamp data of the coordinates of the positioning point.
  • Step 24 Calculate the travel speed of each vehicle in the space-time sub-region: where 2 ... 4-1, « is the distance between the first and second GNSS positioning points in the space-time sub-zone, . , the distance between the -1 and the nth GNSS anchor point, ⁇ ... is the first time in the space-time sub-region, ..., the time stamp of the GNSS anchor point; the specified time
  • the length of the segment length is the upper limit of the number of segments of the segment; ⁇ «; Search for the velocity data in the time segment of the time-space sub-region. If the number of velocity data in the time segment exceeds the upper limit p, randomly f data is added to ⁇ . .
  • Step 25 Establish an Elman-RNN neural network consisting of an input layer, an implicit layer, an output layer, a context layer, and a bias node.
  • the number of neurons in the input layer is set to 1, and the number of neurons in the hidden layer is set to 5.
  • the number of neurons in the output layer is set to 1, the context layer neurons The number is the same as the number of hidden layers, and is also set to 5, the initial value of the offset node is set to 0; the initial parameters of each component are set, wherein the learning rate is set to 0.1, and the initial temperature in the simulated annealing module is set to 105, the end temperature is set to 10-2, the number of iterations for each temperature is set to 100; ⁇ as the input data, the training of the neural network, trained RN model finally obtained.
  • Step 27 sorting the time series data subscripts to be processed by the abnormal point detection in ascending order
  • Step 28 Normalize the difference in velocity distribution of each space-time sub-region to a normalized value of 0 ⁇ 1:
  • Step 31 Using a non-equidistant space-time division method, for a central area of the city where the road network density is greater than 2 km/km 2 or the peak hour flow is greater than 1000 vehicles/hour, a 30 min time segment and a 200 m ⁇ 200 m spatial segment are taken, and the road network density is less than 2km/km 2 or a suburb of a city with a peak hour flow of less than 1000 vehicles/hour, take a 30-minute time segment and a 400mX400m space segment.
  • Step 32 Perform data preprocessing, perform data cleaning, data integration, data conversion, and data reduction on the GNSS positioning data to improve the structural degree of the data.
  • Step 33 Divide the space area to be processed into a grid of a certain size, and the range of each grid area can be expressed as
  • the point P(t A -t 0 ), Pfc+io) adjacent to A in time is defined as 1-adjacent point of A, P(t A -2t 0 ), ⁇ 04+23 ⁇ 4) is defined as the 2-adjacent point of A, and so on, then ⁇ 4-/3 ⁇ 4;), defined as the /- neighbor of ⁇ .
  • the anchor point is matched to the spatio-temporal sub-area in combination with the timestamp data of the coordinates of the positioning point.
  • the distance between the first and second GNSS anchor points in the zone ..., the distance between the -1 and the nth GNSS anchor point, ⁇ ... is the space-time sub-zone 1 , ..., the first time stamp of a GNSS anchor point; specify the maximum length of the clip data at the same time segment length; ⁇ «; search for the speed data in the time segment of the time and space sub-region If the number of speed data in the time segment exceeds the upper limit p, randomly f data is added to ⁇ . .
  • Step 35 Establish an Elman-RNN neural network consisting of an input layer, an implicit layer, an output layer, a context layer, and a bias node.
  • the number of neurons in the input layer is set to 1, and the number of neurons in the hidden layer is set to 5.
  • the number of neurons in the output layer is set to 1, the number of neurons in the context layer is the same as the number of hidden layers, and is also set to 5, and the initial value of the offset node is set to 0; the initial parameters of each component are set, where
  • the learning rate is set to 0.05
  • the initial temperature in the simulated annealing module is set to 10 5
  • the termination temperature is set to 10 - 2
  • the number of iterations per temperature is set to 200.
  • Step 36 Using the rolling time domain mean method, using the travel speed data obtained by data sampling, calculating the current space sub-zone travel speed
  • Step 37 The sequence V1 , v 2 , . . . , v of the points in which the time series data to be processed by the abnormal point detection is sorted in ascending order is known, and the model fitted according to the historical data is known to calculate the fitted model.
  • Predictive value (V) Calculate the difference between the predicted value and the true value of the model"
  • Step 38 Normalize the difference in velocity distribution of each spatiotemporal sub-region to a normalized value of 0 ⁇ 1:

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Abstract

L'invention concerne un procédé de détection d'anomalie de trafic routier intelligent, comprenant les étapes suivantes : établir des sous-segments de temps/espace ; prétraiter des données de suivi passées ; analyser les données de suivi passées et entraîner un modèle de réseau de neurones récurrent ; détecter les anomalies ; indiquer le degré de gravité des anomalies ; et évaluer les performances du système. Dans le procédé, des informations de suivi de véhicule sont enregistrées au moyen de dispositifs de positionnement GNSS embarqués dans des véhicules flottants circulant sur des routes, et lesdites informations de suivi sont analysées et exploitées afin d'obtenir une détection intelligente d'anomalies et d'incidents de trafic sur des routes urbaines.
PCT/IB2017/058533 2016-12-30 2017-12-30 Procédé de détection d'anomalie de trafic routier intelligent Ceased WO2018122803A1 (fr)

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PCT/IB2017/058533 Ceased WO2018122803A1 (fr) 2016-12-30 2017-12-30 Procédé de détection d'anomalie de trafic routier intelligent
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PCT/IB2017/058531 Ceased WO2018122801A1 (fr) 2016-12-30 2017-12-30 Procédé de détection d'anomalie de circulation d'une route urbaine
PCT/IB2017/058534 Ceased WO2018122804A1 (fr) 2016-12-30 2017-12-30 Procédé de détection d'anomalies de trafic routier par division temps/espace non isométrique
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