JP2011099798A - Method and device for predicting congestion range - Google Patents

Abstract

When a traffic jam occurs due to a sudden event, a route that reliably avoids the traffic jam range is presented without repeating the route search.
A traffic information acquisition step for acquiring traffic information including information on a sudden event and a traffic arrival time prediction for predicting at least one of a traffic jam start time and a traffic jam elimination time caused by a sudden event for each mesh The impact of traffic jams between the step, the arrival time prediction step for predicting the arrival time from the departure location for each mesh around the departure location based on the departure location and departure time, and until the congestion caused by the sudden event is resolved By executing the traffic jam influence range calculation step for calculating the range of the traffic jam to be received and setting the route so as to avoid the obtained traffic jam influence range, it is possible to reliably avoid the traffic jam in the future. The number of route searches can be reduced.
[Selection] Figure 1

Description

  The present invention relates to a navigation system, and more particularly to a system that calculates a traffic jam range and provides a route that avoids the range.

  In recent years, when navigating a route from the current location to the destination using a navigation system, the user selects conditions such as the shortest time, the shortest distance, and general road priority to search for the optimum route for the user. The technology to guide is widespread. When the user selects the shortest time route, the car navigation apparatus searches for a route with the shortest required time to the destination using the current traffic information and the statistical traffic information. However, sudden traffic jams caused by sudden events are difficult to predict and avoid in advance using current traffic information and statistical traffic information. Therefore, when such a traffic jam occurs, there is a problem that it takes more time than expected to get involved in the traffic jam and reach the destination. Therefore, when a traffic jam occurs, a technology has been developed to minimize the time loss due to the traffic jam by performing route search again and avoiding the traffic jam.

  For example, as shown in Japanese Patent Application Laid-Open No. 2007-285998, a traffic jam section when the vehicle reaches a traffic jam location occurring on the shortest time route is predicted, and at an intersection before the predicted traffic jam section. There is a technique for avoiding the predicted traffic jam section by starting detouring from an intersection that satisfies a user's desired condition.

  In addition, as disclosed in Japanese Patent Application Laid-Open No. 2008-268149, there is a technology for calculating the area of a region to be avoided according to the elapsed time from the occurrence time of the accident and performing a route search so as to avoid the region. .

JP 2007-285998 A JP 2008-268149 A

  However, when the technique shown in Patent Document 1 is used for sudden traffic jams, it is possible to predict and avoid sudden traffic jams on the shortest time route that is currently guiding and guiding, but after detouring It is impossible to predict whether or not a traffic jam caused by the sudden traffic jam will be encountered. Therefore, in order to reliably avoid sudden traffic jams, predict the sudden traffic jams on the route after detouring, and if the vehicle encounters sudden traffic jams on the route after detouring, You have to make a detour again.

  In addition, when the technique disclosed in Patent Document 2 is used for sudden congestion, it is possible to search for a route that avoids the current sudden traffic jam range. Whether it is included or not cannot be predicted. Therefore, in order to avoid sudden traffic jams with certainty, it is necessary to repeat the re-search process.

  That is, when these conventional techniques are used, it is necessary to repeat the re-search process in order to reliably avoid sudden traffic jams, which increases the number of route searches and the amount of calculation. In this case, the recommended route may change frequently, which is very inconvenient for the user. Furthermore, since the route is set without considering the future situation, the possibility of falling into a situation where traffic congestion cannot be avoided increases.

  Therefore, even when a traffic jam occurs due to a sudden event, the present invention predicts the traffic impact range in advance by taking into account the change in the impact range of the sudden event accompanying a change in time. An object is to present a route that reliably avoids a traffic jam range without repeating.

  In order to solve the above problems, the present invention provides a traffic information acquisition step for acquiring traffic information including information on sudden events, and arrival of traffic congestion that predicts at least one of the start time and the cancellation time of traffic jams due to sudden events for each mesh. A time prediction step, an arrival time prediction step for predicting the arrival time from the departure place for each mesh around the departure position based on the departure position and departure time, and a traffic jam influence range calculation step for calculating the range of traffic jams that will be affected in the future , Is executed.

  In addition, a display step for displaying the traffic jam influence range on the map and a route search step for performing a route search after resetting the link cost of the link included in the obtained traffic jam influence range are executed. It is characterized by that.

  According to the present invention, in order to set a traffic jam influence range as an area where a future traffic jam may be encountered, by setting a route so as to avoid the traffic jam impact range, it is possible to ensure the traffic jam progress range in the future. Traffic congestion can be avoided, and the number of route re-searches can be reduced.

It is a block diagram of the traffic information system using this invention. It is a figure which shows the structural example of map information. It is a flowchart showing the process of the traffic information system using this invention. It is a flowchart of traffic arrival time prediction processing. It is a figure explaining a traffic jam propagation model. It is a figure which shows the traffic start time and traffic cancellation time in a traffic jam propagation model. It is a flowchart of the arrival time prediction process from a departure place. It is a figure explaining the Dijkstra method of the mesh unit in an arrival time prediction process. It is a figure which shows the structural example of the data stored in a prediction information storage part. It is a flowchart of a traffic jam influence range calculation process. It is a figure which shows the example of a screen display of a traffic jam influence range. It is another block diagram of the traffic information system using this invention. It is another flowchart showing the process of the traffic information system using this invention. It is a figure which shows the structural example of the data stored in a prediction information storage part.

  Embodiments of the present invention will be described with reference to the drawings.

  FIG. 1 is a configuration diagram of a traffic information system 110 using the present invention. The traffic information system 110 includes a basic map information DB 120, a prediction unit 130, a traffic jam influence range calculation unit 140, a display map information DB 150, and a display unit 160.

  FIG. 2 shows the data structure of the basic map information DB 120. The basic map information DB 120 includes mesh data 210 representing information about meshes, and link data 220 of each link constituting a road included in the mesh area, and is stored for each mesh. The mesh represents a rectangular area (for example, an area having a latitude difference of 40 minutes and a longitude difference of 1 degree) set based on the latitude and longitude. The size of the mesh may be set as appropriate according to the resolution of the congestion range desired by the user, the configuration of the road, and the like. A link is an element constituting a road, and is represented as a vector connecting nodes designated as an intersection or a branch. At this time, the upward direction and the downward direction of the same road are managed as different links.

  The mesh data 210 includes a mesh ID 211 and an in-mesh average speed 212. The mesh ID 211 represents an identification code of each mesh, is regularly given according to the position of the mesh on the map, and the mesh ID of an adjacent mesh can be obtained from the mesh ID 211 of a certain mesh. The average mesh speed 212 stores a value obtained by averaging travel speeds 224 described later for links included in the mesh.

  The link data 220 includes a link ID 221 indicating an identification code of each link, a road type 222 classified as an expressway, a general national road, etc., a link length 223 indicating the length of the link, and an average speed when passing through the link The travel speed 224 representing the travel time 225 and the travel time 225 representing the time required for passing the link.

  The travel speed 224 stores the restriction speed of each link. The travel speed 224 may be statistical traffic information created in correspondence with the day type (day of the week, weekday / Saturday / holiday, fifty days, weather, etc.) of the prediction target day. In this case, assuming that there are multiple past days corresponding to the day type of the forecast target day, the average value and median value of the link travel speed for those same time zone correspond to the statistical travel speed of the link To do. When using statistical traffic information, traffic information for each time zone in each day type is stored for one link. The travel time 225 may be a value obtained by dividing the link length 223 by the travel speed 224.

  The prediction unit 130 includes a traffic information reception unit 131, a current position acquisition unit 132, a traffic jam determination unit 133, a traffic jam arrival time prediction unit 134, an arrival time prediction unit 135, and a prediction information storage unit 136. .

  The traffic information receiving unit 131 receives current traffic information and sudden event information from an FM multiplex broadcast receiving device, a beacon receiving device, a mobile phone, a wireless device, and the like, and sends them to the traffic jam determining unit. Current traffic information includes travel time for each link, traffic congestion level, and the like. The sudden event information includes information on the content of the restriction (blocks, one-side restriction, etc.), the cause event (accident, construction, etc.), the road type of the restricted link, the position of the restriction, the start time of the restriction, and the like. The FM multiplex broadcast receiving apparatus receives general traffic information (such as graphics) and character information transmitted as FM multiplex broadcast signals by an FM broadcast station. The general traffic information and the character information are created based on the current traffic information transmitted from the traffic information center to the FM broadcast station. The beacon receiving device receives detailed current traffic information transmitted from the traffic control center via a beacon that is a communication device on the road. The current traffic information is transmitted from the traffic information center to the traffic control center in each region. Here, the FM multiplex broadcast receiving apparatus and the beacon receiving apparatus present information relating to vehicle information (time, traveling speed, traveling position information, etc.) sent from the probe car and a link corresponding to the traveling position. It may be received as information.

  The current position acquisition unit 132 calculates the position of the departure place and acquires the current time. At this time, for example, the position is calculated based on information acquired from at least one of a vehicle speed sensor, a gyro sensor, a GPS receiver, and the like. The vehicle speed sensor measures the travel distance from the product of the circumference of the wheel and the measured rotational speed of the wheel, and further measures an angle bent from the difference in rotational speed of the paired wheels. The gyro sensor is composed of an optical fiber phylo, a vibration gyro, or the like, and detects a rotation angle of a vehicle to which the sensor is attached. Here, the departure point is the vehicle position when the traffic information system 110 is provided in an in-vehicle device. Further, when the traffic information system 110 is provided in a device different from the in-vehicle device, for example, a traffic information center, the departure point is a point designated by the user or the vehicle position sent from the in-vehicle terminal.

  Whether the traffic jam determination unit 133 considers the location information acquired from the current location acquisition unit 132, and whether there is a traffic jam that may be affected from the current traffic information and sudden event information received by the traffic information reception unit 131. Or, it is determined whether or not it will occur in the future. Specifically, an unexpected event has occurred within a predetermined distance (for example, within 50 km) from the departure place, and the occurrence time of the unexpected event is within a predetermined time range (for example, between the present and one hour before) If at least one of the conditions such as the current traffic information is rapidly changing beyond a predetermined threshold (for example, the travel speed is rapidly decreasing) is satisfied, It is determined that there is a traffic jam that may be received or that it will occur in the future. When it is determined that a traffic jam that may be affected has occurred or will occur in the future, the current traffic information and the sudden event information are sent to the traffic jam arrival time prediction unit.

  The traffic jam arrival time predicting unit 134 starts traffic jam for each mesh about the mesh around the sudden event occurrence position from the current traffic information and sudden event information acquired from the traffic jam determining unit 133 and the basic map information acquired from the basic map information DB 120. Calculate the time and the congestion elimination time. In addition, the traffic jam start time and traffic jam resolution time calculated for each mesh are sent to the prediction information storage unit 136.

  The arrival time prediction unit 135 determines the arrival time from the departure point for each mesh for the mesh around the departure point based on the position information of the departure point acquired from the current position acquisition unit 132 and the basic map information acquired from the basic map information DB 120. Is calculated. In addition, the arrival time from the departure place calculated for each mesh is sent to the prediction information storage unit 136.

  The prediction information storage unit 136 stores the arrival time, the congestion start time, and the congestion elimination time for each mesh sent from the congestion arrival time prediction unit 134 and the arrival time prediction unit 135. An example of the configuration of the prediction information stored in the prediction information storage unit 136 is shown in FIG. For each mesh ID, arrival time, traffic jam start time, and traffic jam resolution time are recorded one by one.

  The traffic jam influence range calculation unit 140 reads the arrival time, the traffic jam start time, and the traffic jam elimination time for each mesh from the prediction information storage unit 136, and determines whether each mesh is included in the traffic jam influence range. In addition, the result is transmitted to the display unit 160.

  The display map information DB 150 includes information on roads and other components that are necessary when performing map display. For example, information on location coordinates of complementary points of links, water system data such as coastline, lakes, and river shapes, administrative boundary location data, railway location data, location / shape data of facilities, etc., display location data such as place names, etc. .

  The display unit 160 includes a map display unit 161 and a display device 162. The map display unit 161 acquires map information from the display map information DB 150, and draws a road, other map components, and a mark indicating the departure place with a specified scale and drawing method (plan view, bird's-eye view, etc.). Then, a drawing command is sent to the display device 162. Further, the appearance of the mesh is displayed differently from the appearance of the normal mesh so that the user who has seen the display device 162 can recognize the mesh determined by the traffic jam influence range calculation unit 140 as the traffic jam influence range (for example, A drawing command is sent to the display device 162 so as to change the color. Here, in a certain mesh determined to be a traffic jam influence range, if the mesh is included in the sea, lake, mountainous area, etc., and there are no or very few links included in the mesh, the map display unit 161 A drawing command for displaying the color of the mesh as usual may be sent. At this time, a predetermined threshold is set, and the mesh determined as the traffic jam influence range by the traffic jam influence range calculation unit 140 is displayed as the traffic jam influence range when the number of links included in the mesh is equal to or greater than the threshold. If the number of links included in the link is less than the threshold, it is displayed as outside the traffic jam influence range.

  The display device 162 includes a CRT or a liquid crystal display, and displays a map screen based on a drawing command transmitted from the map display unit 161. FIG. 11 illustrates a screen display example in the display device 162. The display device 162 displays the traffic jam influence range on the map along with the departure location, road, facility, etc., so that the traffic jam range that will affect the departure location in the future can be shown. Can be changed.

  With the configuration of the traffic information system 110 described above, the future situation can be predicted and the affected traffic jam range can be obtained. Therefore, by avoiding this range, reliable congestion avoidance can be realized in the future.

  FIG. 3 is a flowchart showing the entire processing procedure in the traffic information system 110 to which an embodiment of the present invention is applied. In the flowchart of FIG. 3, the traffic information system 110 first acquires the current traffic information and sudden event information from the FM multiplex broadcast receiver, the beacon receiver, etc., and imports it into the traffic information receiver 131 (S40). This step is performed at regular intervals (for example, every 5 minutes).

  Next, from the position information acquired by the current position acquisition unit 132 and the current traffic information and sudden event information received by the traffic information reception unit 131, the traffic jam determination unit 133 generates traffic that may be affected. Or whether it will occur in the future (S41). Specifically, a sudden event occurs within a predetermined distance (for example, within 50 km) from the current position of the departure place, and the occurrence time of the sudden event is within a predetermined time range (for example, from the present to one hour before If at least one of the following conditions is satisfied, for example, the current traffic information is rapidly changing beyond a predetermined threshold (for example, the travel speed is rapidly decreasing) It is determined that there is a traffic jam that may be affected, or that it will occur in the future. In this case, the current traffic information and sudden event information are sent to the traffic jam arrival time prediction unit 134 (S41: Yes).

  On the other hand, when it is determined that there is no traffic jam that may affect the departure place and it will not occur in the future, the processing in the traffic information system 110 ends (S41: No).

  In the determination in the traffic jam determination unit 133, it is also determined whether or not the information on the sudden event received by the traffic information receiving unit 131 is the same as that due to the sudden event received previously. If it is determined that there is, the processing in the traffic information system 110 may be terminated. Whether or not they are the same sudden event is determined, for example, based on whether or not the conditions such as the occurrence location of the sudden event are the same and the occurrence time of the sudden event are the same are satisfied. If it does in this way, it can prevent performing prediction processing a plurality of times with respect to one sudden event, and it leads to reduction of the amount of calculation.

  When it is determined by the traffic jam determination unit 133 that an unexpected event that satisfies the condition has occurred, the traffic jam arrival time prediction unit 134 determines the mesh for each mesh based on the current traffic information and the sudden event information sent from the traffic jam determination unit 133. A traffic jam start time and a traffic jam elimination time are calculated (S42). Then, the calculated traffic jam start time and traffic jam resolution time are stored in the prediction information storage unit 136. The arrival time prediction unit 135 calculates the arrival time from the departure point for each mesh, and stores the calculated arrival time from the departure point in the prediction information storage unit 136 (S43). Next, the traffic jam influence range calculation unit 140 reads the arrival time from the departure point for each mesh, the traffic jam start time, and the traffic jam cancellation time from the prediction information storage unit 136, and determines whether each mesh is included in the traffic jam influence range. To do. Then, the map information near the departure place is acquired from the display map information DB 150, and a drawing command is sent from the map display unit 161 to the display device 162 so as to display the map screen (S44). The map display unit 161 receives the mesh ID determined to be included in the traffic jam influence range from the traffic jam influence range calculation unit 140, and sends a command to the display device 162 so as to change the display color of the mesh to a specified color. . The display device 162 receives the command and displays the traffic jam influence range on the map along with the departure place, road, facility, and the like (S45).

  FIG. 11 shows a display example of the display device 162. FIG. 11A shows a starting point displayed on a map as a triangle, a sudden event occurrence position displayed as a star, and a main road displayed as a black line. Furthermore, the mesh is indicated by a square lattice surrounded by a dotted line, and the mesh determined to be the traffic jam influence range is indicated by hatching.

  At this time, the traffic jam influence range may be displayed in consideration of an area where there is no road such as the sea or a lake, or an area where there are few roads such as a mountainous area. FIG. 11B shows a display example of the display device 162 in consideration of an area where there is no road or there are few roads. Here, by obtaining the position / shape data of the lake from the map information DB for display, as shown in FIG. 11 (b), the display color is changed for the area that is on the lake and is determined as the traffic jam influence range. Do not do. Such a determination of whether or not to change the display color may be performed based on the number of links in the mesh, the density, and the like. Further, whether or not to change the display color may be determined for each small area obtained by dividing the mesh into some parts.

  By executing the above processing, it is possible to predict the future traffic jam situation and obtain the affected traffic jam range. By avoiding this traffic jam range, reliable traffic jam avoidance can be realized in the future.

  FIG. 4 is a flowchart for explaining in detail the processing of S42 in the traffic jam arrival time prediction unit 134. In S <b> 50, current traffic information and sudden event information are acquired from the traffic jam determination unit 133. Next, in S51, various parameters for determining the traffic jam propagation model are set using the acquired current traffic information and sudden event information. By setting various parameters in the traffic jam propagation model, it is possible to predict future traffic jam conditions.

  An example of this traffic jam propagation model will be described with reference to FIGS. In this traffic jam propagation model, it is assumed that the traffic jam area expands and contracts while maintaining a donut shape centering on the point of occurrence of the sudden event, and the positions of the outer edge and inner edge of the traffic jam area change. FIG. 6A is a graph showing the time change of the distance from the sudden event occurrence position at the outer edge of the traffic jam and the inner edge of the traffic jam. In FIG. 6A, the horizontal axis represents time, and the vertical axis represents the distance from the sudden event occurrence position. FIG. 6B is a graph showing the change over time in the displacement speed of the outer edge of the traffic jam and the inner edge of the traffic jam. In FIG. 6B, the horizontal axis represents time, and the vertical axis represents the displacement speed of the outer edge of the traffic jam and the inner edge of the traffic jam. At this time, the direction outward from the sudden event occurrence position is a positive value.

  In the traffic jam propagation model shown in FIGS. 6A and 6B, the traffic jam occurs at time T1 and is eliminated at time T5. The traffic jam outer edge spreads outward at a constant speed Vj1 (≧ 0) between times T1 and T2, does not change between times T2 and T4, and is inward at a constant speed Vj3 (≦ 0) between times T4 and T5. To shrink. On the other hand, the traffic jam inner edge does not yet exist between times T1 and T3, and spreads outward at a constant speed Vj2 (≧ 0) at times T3 to T5.

  In S51, various parameters of T1, T2, T3, T4, T5, Vj1, Vj2, and Vj3 in FIG. 6A are set using the current traffic information and the sudden event information. For this reason, for example, for all situations represented by the contents of regulations (blocks, one-side regulations, etc.), cause events (accidents, construction, etc.), road types of regulated links, regulation positions, regulation start times, etc. The corresponding parameter values are determined in advance. At this time, as values of various parameters, values statistically obtained from information on past sudden traffic jams that occurred under the same situation may be used. The various parameters may be set uniformly for one sudden traffic jam, or may be set in a range for each fixed angle centered on the sudden event occurrence position.

  In S52, the traffic jam start time and the traffic jam resolution time are calculated for each mesh using the traffic jam propagation model based on the various parameters set in S51 and the basic map information acquired from the basic map information DB 120. At this time, the mesh for calculating the congestion start time and the congestion elimination time is, for example, a distance between the sudden event occurrence position and the representative point of the mesh (for example, the center point of the mesh) within a predetermined range (for example, within 100 km) ) Is selected as a mesh that satisfies the condition. The calculated traffic jam start time and traffic jam resolution time are stored in the prediction information storage unit 136.

  The calculation process of the traffic jam start time and traffic jam elimination time in a specific mesh will be described. FIG. 6C shows a mesh on the map as a square lattice. First, the distance between the sudden event occurrence position 601 and the representative point of each mesh (for example, the center point of the mesh) is calculated. In FIG. 6C, the distance between the sudden event occurrence position 601 and the mesh m1 is d1, and the distance between the mesh m2 is d2.

Next, the change in position of the outer edge of the traffic jam and the inner edge of the traffic jam in FIG.
d = f (t) (Formula 1)
d = g (t) (Formula 2)

Here, d represents the distance from the sudden event occurrence position, and t represents time. From this, it can be said that the congestion range at time t is a range that satisfies the following (Equation 3).
g (t) ≦ d ≦ f (t) (Formula 3)

Also, a positive minute time Δt is set. For example, Δt = 5 min. At this time, the traffic jam start time of the mesh located at the distance d from the sudden event occurrence position 601 is obtained as a time t satisfying the following conditions 1 and 2.
Condition 1: satisfies g (t) = d or f (t) = d Condition 2: satisfies g (t + Δt) ≦ d ≦ f (t + Δt)

Further, the mesh congestion elimination time is obtained as a time t that satisfies the following conditions 3 and 4.
Condition 3: satisfies g (t) = d or f (t) = d Condition 4: satisfies g (t−Δt) ≦ d ≦ f (t−Δt)

  In the example of FIG. 6C, for the mesh m1, by substituting d = d1 into conditions 1 to 4, the traffic jam start time in the mesh m1 is obtained as t1, and the traffic jam elimination time is obtained as t2. On the other hand, for mesh m2, even if d = d2 is substituted into conditions 1 to 4, there is no time t that satisfies the condition, and neither the traffic jam start time nor the traffic jam resolution time is calculated. As described above, when the traffic jam start time and the traffic jam resolution time are not calculated, it indicates that the traffic jam range does not propagate to the mesh, and the corresponding area of the prediction information storage unit 136 is “no data”.

  Through the above processing, the traffic jam start time and traffic jam elimination time in each mesh are calculated.

  Next, details of the process of S43 in the arrival time prediction unit 135 will be described with reference to FIGS. FIG. 7 shows a flowchart of processing in the arrival time prediction unit 135. In FIG. 7, first, the current position acquisition unit 132 acquires the current position calculated based on information received from a vehicle speed sensor, a gyro sensor, a GPS receiver, or the like (S80). Next, the moving speed between meshes is set (S81). As the moving speed, an in-mesh average speed 212 included in the mesh data of the basic map information DB 120 is used. If there is no average speed 212 in the mesh, the travel speed of the link included in the traffic jam range is changed based on the traffic jam situation predicted by the traffic jam arrival time prediction unit 134 in advance, and the average is calculated for each mesh. You may calculate the moving speed between meshes. And the arrival time from a departure place is calculated for every mesh using the set moving speed between meshes and the basic map information acquired from basic map information DB120 (S82).

  A specific process for calculating the arrival time for each mesh will be described with reference to FIG. For each mesh, one representative point (for example, the center point of the mesh) is set, and the distance between the representative points is set as the distance between the meshes. By dividing the distance between meshes by the average speed 212 in the mesh, the average moving time between meshes is obtained. In FIG. 8, each mesh is represented by a square grid, the representative points of each mesh are represented by black circles, and the movement time between each mesh is entered thereon. The Dijkstra method is applied by assuming that there is a link with the starting point as the starting node, the representative point of each mesh as a node, and the average moving time between meshes as the link cost between the representative points of the mesh. The minimum required time required to reach the representative point of each mesh can be obtained. Therefore, the arrival time to each mesh can be calculated by adding the minimum required time obtained for each mesh to the current time. At this time, the mesh for calculating the arrival time is, for example, a mesh that satisfies the condition that the distance between the departure point and the representative point of the mesh (for example, the center point of the mesh) is within a predetermined range (for example, within 100 km). To be elected.

  FIG. 8 shows the arrival time at each mesh entered on the mesh when the upper left mesh in the figure is the departure point and the departure time is 9:00.

  By the above processing, the arrival time from the departure place in each mesh is calculated.

  Next, details of the processing of S44 in the traffic jam influence range calculation unit 140 will be described with reference to FIG. This process is performed for each mesh included in a certain region. Each region is selected, for example, as a mesh that satisfies the condition that the distance between the starting point and the representative point of the mesh (for example, the center point of the mesh) is within a predetermined range (for example, within 100 km).

  First, the traffic jam influence range calculation unit 140 reads the arrival time from the departure point of a certain mesh, the traffic jam start time, and the traffic jam elimination time from the prediction information storage unit 136 (S110). Here, it is assumed that the arrival time for each mesh is Ts, the traffic jam start time is Tj1, and the traffic jam resolution time is Tj2. FIG. 9 shows an example of the format of the arrival time from the departure point for each mesh, the traffic jam start time, and the traffic jam resolution time stored in the prediction information storage unit 136. It is assumed that the prediction information storage unit 136 stores data in the format shown in FIG. 9 for each mesh within a predetermined range with respect to the mesh corresponding to the departure place.

  And it is determined whether the data of the arrival time Ts exist in the data read from the prediction information storage part 136 about the mesh made into a process target (S111). When there is no data (S111: no data), it is determined that the corresponding mesh is not included in the traffic jam influence range (S117). This is the state shown in the data example in FIG. 9 where the mesh ID is “45678901”.

  If there is Ts data (S111: data present), it is next determined whether or not there is data on the traffic jam start time Tj1 in the corresponding mesh (S112). When there is no data (S112: no data), it is determined that the corresponding mesh is not included in the traffic jam influence range (S117). This is the state shown in the data example in FIG. 9 where the mesh ID is “345567890”.

If there is data of Tj1 (S112: data is present), it is next determined whether or not there is data of traffic jam start time Tj2 in the corresponding mesh (S113). When there is no data (S113: no data), in S116, it is determined whether or not the following (Equation 4) is satisfied. When Expression (4) is satisfied (S116: Yes), it is determined that the mesh is included in the congestion influence range. When Expression (4) is not satisfied (S116: No), it is determined that the mesh is not included in the congestion influence range. (S117).
Tj1 ≦ Ts (Formula 4)

  This is the state shown in the data example in FIG. 9 where the mesh ID is “234456789”. In this example, since Ts = 9: 10 and Tj1 = 9: 05 satisfy (Expression 1), it is determined that the corresponding mesh is included in the traffic jam influence range (S115).

Finally, when there is Tj2 (S113: data present), in S115, it is determined whether or not the following (Equation 5) is satisfied. If (Expression 5) is satisfied (S115: Yes), it is determined that the mesh is included in the traffic jam influence range (S115). If (Formula 5) is not satisfied (S116: No), it is included in the traffic jam influence range. It is determined that there is not (S117).
Tj1 ≦ Ts ≦ Tj2 (Formula 5)

  This is a state shown in the example of FIG. 9 where the mesh ID is “12345678”. In this example, Ts = 9: 00, Tj1 = 9: 10, Tj2 = 9: 30, and since Expression (2) is not satisfied, it is determined that the mesh is not included in the traffic jam influence range.

  The above processing is performed for each mesh within a predetermined range. From the above processing, it is determined whether or not each mesh is included in the traffic jam influence range. As described above, the traffic jam influence range calculation unit 140 determines whether or not each mesh is included in the traffic jam influence range, thereby reducing the calculation amount and shortening the processing time.

  Further, instead of determining the traffic jam influence range in units of meshes, it may be determined whether or not each link is included in the traffic jam influence range. At this time, at least one of the traffic arrival time prediction unit 134 and the arrival time prediction unit 135 calculates the arrival time for each link. In the processing in S42, when the traffic jam arrival time prediction unit 134 calculates the traffic jam start time and the traffic jam resolution time for each link, in the processing described in FIG. 6, the distance between the sudden event occurrence position and the mesh representative point is calculated. The used distance is the distance between the sudden event occurrence position and the link representative point (for example, the midpoint of the link). On the other hand, when the arrival time is calculated for each link in the process in S43, the normal link unit Dijkstra method is used instead of the mesh unit arrival time calculation process described with reference to FIG. The required time is calculated. When determining whether each link is included in the traffic jam influence range, the prediction information storage unit 136 stores the link ID instead of the mesh ID, and arrives from the departure place for each link ID. The time, traffic start time, and traffic resolution time are recorded one by one.

  As described above, when determining whether or not each link is included in the traffic jam influence range, more accurate traffic jam influence range calculation can be realized as compared with the case where prediction is performed in units of meshes.

  Moreover, you may perform a prediction process in multiple times with respect to one sudden event. In this way, the accuracy of prediction can be further increased, and an appropriate traffic jam influence range can be calculated. In this case, in the processing of S42 and S43, the previously calculated own vehicle arrival time, traffic jam start time, and traffic jam resolution time are stored. In S42 and S43, when the arrival time from the departure point in each mesh, the traffic jam start time, and the traffic jam resolution time are calculated, if the difference exceeds a predetermined threshold, the processing of S44 is performed. It is good to do. In this way, the calculation of the traffic jam impact range is repeated only when the difference between the previous prediction result and the current traffic situation for the same sudden event increases, so compared to the case where all steps are repeated each time traffic information is received. The amount of calculation can be reduced.

  FIG. 12 is a configuration diagram of a traffic information system 1410 in which a route search unit 1420 having a route search function for searching for a route connecting a departure place and a destination is added to the traffic information system 110 shown in FIG.

  FIG. 13 is a flowchart showing a processing procedure in the traffic information system 1410. Steps that perform the same processing as each step in the flowchart shown in FIG. 3 are indicated using the same abbreviations. Compared with the processing procedure of the first embodiment shown in FIG. 3, in this embodiment, a route search process (S150) in the route search unit 1420 is added. In S150, the shortest time path from the departure point to the destination is searched using the Dijkstra method or the like. At this time, the link cost is set using the travel time 225 for each link included in the basic map information DB 120. Moreover, you may set link cost using the statistical traffic information contained in basic map information DB120. Further, the link cost may be set using the current traffic information acquired from the traffic information receiving unit 131.

  The route search unit 1420 acquires the mesh ID of the mesh determined to be included in the traffic jam influence range in S44 from the traffic jam influence range calculation unit 140, and the link included in the mesh determined to be included in the traffic jam influence range. , Reset the link cost. In resetting the link cost, for example, the average speed within the traffic jam influence range is uniformly set (for example, 5 km / h), and for each link, the link length 223 is divided by the average speed within the traffic jam influence range, Link cost. Alternatively, the link cost may be set smaller as it approaches the end of the traffic jam and higher as it approaches the center of the traffic jam. This is because the reliability of the traffic jam prediction result is low at the edge of the traffic jam and high at the center of the traffic jam.

As a specific link cost calculation method, for example, there are the following methods. If the distance from the sudden event occurrence position is represented by d, the distance from the sudden event occurrence position to the inner edge of the traffic jam is di, and the distance from the sudden event occurrence position to the outer edge of the traffic jam is do, the central part of the traffic jam is the location of the sudden event occurrence. It can be said that the distance is dm = (di + do) / 2. Further, the link cost obtained by setting the link cost at the normal time and the average speed within the stagnation influence range uniformly are expressed as functions C1 (d) and C2 (d) using d, respectively. At this time, the link cost C (d) according to the distance d from the sudden event occurrence position is obtained from the following (formula 6). Here, N (μ, σ ² ) represents a normal distribution with an average μ and a variance σ ² .
C (d) = {1 + N (C2 (dm) / C1 (dm), (do-di) / 2)} × C1 (d)
(when di ≦ d ≦ do) (Formula 6)
C (d) = C1 (d) (when d ≦ di or d ≧ do)

  As a result, the central part of traffic jams with high prediction reliability can be avoided reliably, but at the traffic jam edges with low prediction reliability, it can pass through the traffic jams depending on the traffic conditions and the positional relationship between the departure point and destination. A route to be selected may be selected. That is, it is possible to perform a route search that is more suitable for traffic conditions.

  In the subsequent S45, as in the first embodiment, the map display unit 161 sends a command to the display device 162 so as to change and display the color of the mesh determined to be within the traffic jam influence range. At this time, the display device 162 may display the recommended route after the re-search based on the route information acquired from the route search unit 1420.

  In S52 of FIG. 4, when determining the mesh for calculating the traffic arrival time, the mesh may include a representative point in a predetermined region including the destination and the departure point. The predetermined area may be, for example, an area included in an elliptical shape having two points of origin and destination. Further, the mesh for calculating the arrival time from the departure place in S82 of FIG. 7 and the mesh for determining the traffic jam influence range in S44 of FIG. 3 are also represented in a predetermined area including the destination and the departure place. The same process may be performed when a point is included.

  As described above, when processing is performed only for meshes included in a predetermined area including the starting point and the destination, each processing is performed only for meshes that are likely to pass thereafter, which is efficient processing. Is possible.

  In the first and second embodiments, the traffic jam influence range is calculated without considering the road type. In the present embodiment, the traffic jam influence range calculation in consideration of the road type will be described. In this case, the differences from the configurations of the first embodiment and the second embodiment are the processes in the basic map information DB 120, the traffic arrival time prediction unit 134, the arrival time prediction unit 135, the prediction information storage unit 136, and the map display unit 161. is there.

  Hereinafter, each of these processes, which are different from the first embodiment and the second embodiment, will be described. First, when calculating the traffic jam influence range for each mesh, the basic map information DB 120 stores the average speed 212 in the mesh calculated for each road type.

  In S172, various parameters (see FIG. 5) of the traffic jam propagation model necessary for the traffic jam arrival time calculation process are obtained for each road type.

  After that, in S42, the calculation of the traffic jam start time and the traffic jam resolution time is performed for each road type. Similarly, S43 calculates arrival time for each road type. The arrival time, traffic jam start time, and traffic jam resolution time calculated in S42 and S43 are stored in the prediction information storage unit 136. FIG. 14 shows the format of the arrival time from the departure point, the traffic jam start time, and the traffic jam resolution time for each mesh stored in the prediction information storage unit 136 in the present embodiment. The vehicle arrival time, traffic jam start time, and traffic jam resolution time are stored.

  Here, when a certain sudden event occurs, the traffic jam start time and the traffic jam elimination time are calculated on the assumption that the traffic jam propagates along the link of the same road type as the road type of the link at the occurrence point. For example, it can be said that a traffic jam caused by a sudden event on an expressway does not propagate to a general road with a different road type, although the distance on the map is short. And if the sudden traffic jam is caused by a sudden event that occurred on the expressway, the traffic jam area does not propagate directly on the general road, so in S45, the traffic jam area on the expressway is colored on the map, and the sudden traffic jam Will be displayed.

  Alternatively, traffic congestion may be propagated between different road types. At this time, it is considered that a new sudden event has occurred at a connection point of different types of roads, and traffic jam propagation is predicted. For example, a situation in which a traffic jam due to a sudden event occurring on an expressway propagates from an interchange to a general road can be considered. At this time, traffic propagation on a general road is predicted using the interchange as a new sudden event occurrence point. In this case, the sudden traffic jam is caused by a sudden event that occurred on the expressway. However, in S45, the state of propagation of the sudden traffic jam on the general road is the same as the case where the interchange is set as the sudden event occurrence location on the map. indicate.

  In S44, as the processing of the traffic jam influence range calculation unit 140, the arrival time from the departure point, the traffic jam start time, and the traffic jam elimination time for each road type of each mesh are read from the prediction information storage unit 136, and the traffic jam for each road type is read. The range of influence is calculated. In the example of FIG. 14, in the mesh with the mesh ID “12345678”, (Expression 1) is satisfied on the expressway, and therefore, it is determined that it is included in the traffic jam influence range. On the other hand, since there is no data on the traffic jam start time and the traffic jam elimination time on a general road, it is determined that the traffic is not included in the traffic jam influence range.

  In the map display unit 161, a drawing command is sent to the display device 162 so that the user can display a traffic jam influence range for each road type. For example, if a highway link and a general road link are in the same mesh and congestion occurs only on the highway, only the color of the highway is displayed as described above, and the color of the general road is normal. Send drawing commands to display on the street.

110,1410 Traffic information system 120 Basic map information DB
130 Prediction unit 131 Traffic information reception unit 132 Current position acquisition unit 133 Congestion determination unit 134 Congestion arrival time prediction unit 135 Arrival time prediction unit 136 Prediction information storage unit 140 Congestion influence range calculation unit 150 Display map information DB
160 Display Unit 161 Map Display Unit 162 Display Device 1420 Route Search Unit

Claims (6)

A traffic information acquisition step for acquiring traffic information including information on sudden events;
A traffic jam arrival time prediction step for predicting at least one of the start time and the cancellation time of the traffic jam generated by the sudden event for each mesh that is a rectangular area divided based on the latitude and longitude;
A location information acquisition step for acquiring a departure location and a departure time;
An arrival time prediction step for predicting the arrival time from the departure place for each mesh around the departure position based on the departure position and the departure time;
Using at least one of the start time and the cancellation time of the traffic jam and the arrival time from the departure place, the range affected by the traffic jam from the occurrence of the traffic jam due to the sudden event to the resolution A traffic jam influence range calculation step for each mesh,
A method for predicting a traffic jam range, characterized in that   2. The traffic jam area prediction method according to claim 1, wherein the traffic jam influence range calculation step calculates a traffic jam influence range for each road type.   The route search step of performing a route search from a departure point to a destination after resetting a link cost different from that outside the congestion influence range for a link included in the congestion influence range. The traffic jam area prediction method according to 1. In a traffic jam area prediction device comprising a traffic information acquisition unit that acquires traffic information including information on sudden events, and a current position acquisition unit that acquires the current position of the host vehicle,
A traffic jam arrival time prediction unit that predicts at least one of the start time and the cancellation time of the traffic jam generated by the sudden event for each mesh that is a rectangular area divided based on latitude and longitude;
A vehicle arrival time prediction unit that predicts the vehicle arrival time from the current position for each mesh around the current position based on the current position and the current time;
A traffic jam influence range for calculating, for each mesh, a range in which the traffic jam caused by a sudden event affects the vehicle using at least one of the start time and the cancellation time of the traffic jam and the arrival time of the vehicle from the current position. A calculation unit;
A traffic congestion range prediction apparatus comprising:   5. The traffic jam area prediction apparatus according to claim 4, wherein the traffic jam influence range calculation unit calculates a traffic jam influence range for each road type.   The route search unit further includes a route search unit that performs a route search from the current position to the destination after resetting a link cost different from that outside the congestion influence range for the links included in the congestion influence range. The congestion range prediction apparatus according to claim 4, wherein:

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