CN112133105B - Traffic flow prediction support device and traffic flow prediction support method - Google Patents
Traffic flow prediction support device and traffic flow prediction support method Download PDFInfo
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
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Abstract
The invention provides a traffic flow prediction support device and a traffic flow prediction support method. Traffic flow can be predicted with high accuracy. The traffic flow prediction support device (10) is provided with: and a vehicle matcher (13) which acquires movement information of a plurality of vehicle models based on past OD data, selects a matching vehicle model which is a vehicle model matching an actual vehicle according to movement information of a communication terminal estimated to exist in a predetermined actual vehicle, and generates OD data according to movement information of the actual vehicle matched with the matching vehicle model instead of movement information of the matching vehicle model. The vehicle matcher (13) may select a vehicle model satisfying a predetermined condition as the matching vehicle model that matches the actual vehicle. In addition, the vehicle matcher (13) may generate a vehicle model corresponding to the actual vehicle when there is no matching vehicle model satisfying the predetermined condition, and generate OD data including movement information of the generated vehicle model.
Description
Technical Field
The present invention relates to a technique for assisting in prediction of traffic flow.
Background
Conventionally, a technique for predicting traffic flow by performing traffic flow simulation based on past OD data has been known. In recent years, the accuracy of traffic flow prediction has been improved by correcting the traffic volume at an observation point based on real-time information such as vehicle sensors disposed on a road.
For example, a technique for predicting traffic flow using observation information of a vehicle sensor is known from patent document 1. In the technique of patent document 1, a traffic volume and a route selection parameter for minimizing a difference between a traffic volume based on observation information and a traffic volume obtained by traffic flow simulation are adjusted, and the adjustment and the traffic flow simulation are repeatedly performed until a result of performing the traffic flow simulation with the traffic volume and the route selection parameter as inputs satisfies a predetermined end condition.
Patent document 1: japanese patent application laid-open No. 2013-235326
Disclosure of Invention
The technique disclosed in patent document 1 uses observation information of a vehicle sensor, taking into account the traffic volume at the observation point. Therefore, the movement information of each vehicle cannot be reflected without considering the movement of each actual vehicle, and there is a problem in terms of accuracy. In addition, in the technique disclosed in patent document 1, adjustment and traffic flow simulation must be repeatedly performed until the end condition is satisfied, and there are problems in that the processing load is high and the responsiveness is poor.
The present invention has been made in view of the above circumstances, and has an object to: provided is a technique capable of predicting traffic flow with high accuracy.
In order to achieve the above object, a traffic flow prediction support device according to one aspect includes: an acquisition unit that acquires movement information of a plurality of vehicle models based on past OD data; a vehicle selection unit that selects a matching vehicle model that is a vehicle model matching an actual vehicle, based on movement information of a communication terminal estimated to be present in a predetermined actual vehicle; and an OD data generation unit that generates OD data based on the movement information of the actual vehicle to which the matching vehicle model matches, instead of the movement information of the matching vehicle model.
According to the present invention, traffic flow can be predicted with high accuracy.
Drawings
Fig. 1 is an overall configuration diagram of a traffic flow prediction system according to an embodiment.
Fig. 2 is a block diagram of an example of OD data of one embodiment.
Fig. 3 is a hardware configuration diagram of the traffic flow prediction support device according to one embodiment.
Fig. 4 is a flowchart of the OD data generation process of one embodiment.
Fig. 5 is a flowchart of a real-time correction process of one embodiment.
Detailed Description
Embodiments are described with reference to the drawings. The embodiments described below do not limit the claimed invention, and the solution of the invention does not necessarily have to include all of the elements and combinations thereof described in the embodiments.
Fig. 1 is an overall configuration diagram of a traffic flow prediction system according to an embodiment.
The traffic flow prediction system 1 includes a past OD data storage unit 2, a movement data storage unit 3, a traffic flow prediction support device 10, a corrected OD data storage unit 4, a traffic flow simulation device 5, a display device 6, a bus running information storage unit 7, and a corrected running information storage unit 8.
The OD data storage unit 2, the mobile data storage unit 3, the corrected OD data storage unit 4, the bus running information storage unit 7, and the corrected running information storage unit 8 may be provided in the traffic flow prediction support device 10 or in another computer or the like, but in the example of fig. 1, the corrected running information storage unit is described as being stored in another computer or the like not shown.
The past OD data storage unit 2 stores OD data collected in the past. The past OD data may be OD data collected by road traffic survey (census) of a country, for example.
The mobile data storage unit 3 stores mobile data (an example of mobile information) including position information and a movement speed transmitted from various communication terminals. The movement data may also be obtained from various communication providers or collected from various communication terminals, for example. In the present embodiment, the mobile data includes an ID capable of identifying the same communication terminal as the same data and position information of the communication terminal. Here, the ID may be an ID that can directly identify the communication terminal itself, or an ID that cannot directly identify the communication terminal itself. The position information of the communication terminal may be coordinates (latitude and longitude) of a GPS (Global Positioning System ), for example. In the present embodiment, as the communication terminal, there are a portable phone carried by a person, a smart phone, a communication terminal mounted on a vehicle, and the like.
The corrected OD data storage unit 4 stores OD data generated by the traffic flow prediction supporting device 10. The OD data may be shared with the OD data of the past OD data storage unit 2, for example.
The bus operation information storage unit 7 stores bus operation information including a parking place, a movement time to each parking place, and the like for a bus in a vehicle. The corrected operation information storage unit 8 stores information (corrected operation information) related to a difference in bus operation information during actual traveling of the bus.
The traffic flow simulation device 5 performs traffic flow simulation by taking OD data, correction operation information, and the like as inputs, thereby predicting traffic flow. The display device 6 displays the traffic flow information, which is the simulation result of the traffic flow simulation device 5.
The traffic flow prediction support device 10 includes a route/traffic distribution calculator 11, a vehicle estimator 12, a vehicle matcher 13, and a monitor 14. Here, the acquisition unit, the vehicle selection unit, the OD data generation unit, and the operation determination unit correspond to the vehicle matcher 13, the divergence determination unit corresponds to the monitor 14, and the vehicle estimation unit corresponds to the vehicle estimator 12.
The route/traffic distribution calculator 11 receives the past OD data stored in the past OD data storage unit 2 as input, and calculates information (statistical data estimation information: movement information) such as the position of a model (vehicle model) corresponding to each vehicle in the OD data, the movement route to the destination, and the movement time. In the calculated statistical data estimation information, each vehicle model is managed by an identifiable ID (vehicle model ID). The route/traffic distribution calculator 11 calculates information (statistical data estimation information) such as a position, a movement route, and a movement time of a model (bus model) corresponding to the bus, based on the bus operation information of the bus operation information storage unit 7, with respect to the bus in the vehicles in the OD data.
The vehicle estimator 12 receives the movement data stored in the movement data storage unit 3, determines a communication terminal estimated to be present in the vehicle, and transmits the movement data of the communication terminal estimated to be present in the vehicle to the vehicle matcher 13. For example, it can be estimated whether the communication terminal is being held by a pedestrian or mounted in a vehicle or held by a person riding in the vehicle, in accordance with the movement speed based on the movement data. In addition, when the plurality of communication terminals are located close to each other and move at relatively high speed and the same or substantially the same speed, it can be estimated that the plurality of communication terminals are held by a person riding in a bus. In this case, the movement data of any one of the communication terminals estimated to be held by the person riding in the bus may be transmitted to the vehicle matcher 13 as movement data related to the bus.
The vehicle matcher 13 obtains statistical data estimation information of a plurality of vehicle models based on past OD data from the route/traffic distribution calculator 11, selects a vehicle model (matching vehicle model) satisfying a predetermined condition for an actual vehicle (actual vehicle) in which the communication terminal that has transmitted the mobile data is located, and manages the vehicle model accordingly. For example, there are a predetermined condition that the assumed time point (in the real-time process, the current time point) is within a predetermined distance from the position of the actual vehicle, a vehicle speed difference is within a predetermined speed, and the like. In addition, as management of correspondence between the matched vehicle model and the actual vehicle, a vehicle model ID of the matched vehicle model and an ID of the movement data may be managed in correspondence.
Instead of the movement information of the matching vehicle model, the vehicle matcher 13 generates OD data from the movement information of the movement data of the communication terminal estimated to exist in the actual vehicle to which the matching vehicle model matches. In addition, if there is no matching vehicle model satisfying the predetermined condition, the vehicle matcher 13 generates a vehicle model corresponding to the movement of the actual vehicle, and generates OD data including movement information of the generated vehicle model.
In addition, when it is determined that the movement information of the matched vehicle model is different from the movement information of the actual vehicle to which the matched vehicle model is matched, the vehicle matcher 13 selects a new vehicle model matched with the actual vehicle as a new matched vehicle model.
When it is estimated that the type of the actual vehicle is a bus, the vehicle matcher 13 selects a bus model matching the bus, determines information (for example, delay time, distance from the departure, etc.) of a difference from the actual bus operation information based on the movement information of the operation information based on the bus model and the movement information of the bus matching the bus model, and outputs the information to the corrected operation information storage unit 8.
The monitor 14 determines whether the movement information of the matching vehicle model is deviated from the movement information of the actual vehicle to which the matching vehicle model matches. Here, the case where the movement information of the matched vehicle model and the actual vehicle deviate from each other includes a case where the matched vehicle model and the actual vehicle are separated by a predetermined distance or more, a case where the path traveled by the matched vehicle model and the actual vehicle is different, and the like.
Next, OD data will be described.
Fig. 2 is a block diagram showing an example of OD data of one embodiment. The OD data shown in fig. 2 represents the OD data stored in the corrected OD data storage section 4, but the OD data stored in the past OD data storage section 2 also has the same structure.
The corrected OD data storage unit 4 stores OD data 41, 42, 43, etc. at each of a plurality of time points. The OD data 41, 42, 43, etc. are tables summarizing the number of vehicles at each start point and end point of the corresponding time points. From the OD data 41, it is found that there are 50 vehicles starting at the observation point 1 and ending at the end point 1 and 8 vehicles starting at the observation point 2 and ending at the end point 2 at the time point where the difference from the reference time (for example, the current time) is-1 hour and 20 minutes.
Next, a hardware configuration diagram of the traffic flow prediction support device 10 will be described.
Fig. 3 is a hardware configuration diagram of the traffic flow prediction support device according to one embodiment.
The traffic flow prediction support device 10 is composed of a computer such as a PC (Personal Computer ), and includes a communication interface (communication I/F) 101, a processor 102, a storage device 103, and a memory 104. The communication I/F101, processor 102, storage device 103, and memory 104 are communicatively connected via a bus 105.
The communication I/F101 is an interface such as a wired LAN card or a wireless LAN card, and communicates with other devices via a network not shown.
The processor 102 performs various processes in accordance with programs stored in the memory 104 and/or the storage device 103.
The memory 104 is, for example, a RAM, and stores a program (traffic flow prediction support program 106) executed by the processor 102 and necessary information.
The storage device 103 is, for example, an HDD (Hard disk drive), an SSD (Solid state disk), or the like, and stores programs executed by the processor 102 and data utilized by the processor 102.
In the present embodiment, the processor 102 executes the traffic flow prediction support program 106 to constitute the route/traffic distribution calculator 11, the vehicle estimator 12, the vehicle matcher 13, and the monitor 14.
Next, the processing operation of the traffic flow prediction support device 10 will be described.
Fig. 4 is a flowchart of the OD data generation process of one embodiment.
For example, the OD data generation process is performed at every predetermined time. First, the vehicle matcher 13 acquires statistical data estimation information on all vehicle models from the route/traffic distribution calculator 11 (step S10).
Next, the vehicle estimator 12 obtains movement data of the communication terminal from the movement data storage unit 3 (step S11).
Next, the vehicle estimator 12 estimates the communication terminal present in the vehicle based on the acquired movement data of the communication terminal, based on the position, speed, concentration, and the like of the communication terminal, and based on the movement data of the communication terminal, estimates information (real-time estimation information) such as the position, speed, and the like of the vehicle (actual vehicle) in which the terminal is estimated to be present (step S12). The movement data to be processed is movement data obtained from a communication terminal within a range in which OD data processed by the route/traffic distribution calculator 11 is the object.
Next, based on the real-time estimation information, the real-time correction process (see fig. 5) for correcting the information of the vehicle model is performed by the vehicle matcher 13 and the monitor 14 (step S13).
Next, the vehicle matcher 13 outputs the corrected OD data generated by the real-time correction process to the corrected OD data storage unit 4. The traffic flow simulation device 5 predicts the traffic flow by performing traffic flow simulation using the corrected OD data output to the corrected OD data storage unit 4, and outputs the prediction result to the display device 6. This enables the traffic flow simulator 5 to accurately predict the traffic flow according to the actual situation.
Fig. 5 is a flowchart of a real-time correction process of one embodiment.
First, the vehicle matcher 13 acquires unprocessed real-time estimation information corresponding to one actual vehicle from the vehicle estimator 12 (step S21). In the following description of the processing, an actual vehicle corresponding to the acquired real-time estimation information is referred to as an object actual vehicle.
Next, the vehicle matcher 13 determines whether a vehicle model matching the target actual vehicle is not selected or a vehicle model matching the target actual vehicle must be reselected (specifically, whether execution of reselection is set) (step S22).
As a result, when the vehicle model matching the target actual vehicle is not selected or when the vehicle model matching the target actual vehicle must be reselected (yes in step S22), the vehicle matcher 13 selects a vehicle model (matching vehicle model) that meets a predetermined condition based on the estimation information based on the statistics, and associates the target actual vehicle with the matching vehicle model (step S23). Thus, the matching vehicle model can be appropriately associated with the target actual vehicle. For example, in the case where reselection is necessary, a new matching vehicle model can be appropriately associated with the subject actual vehicle.
Next, the vehicle matcher 13 determines whether or not there is a matching vehicle model (step S24), and if there is a matching vehicle model (yes in step S24), advances the process to step S28, whereas if there is no matching vehicle model (no in step S24), adds a vehicle model corresponding to the subject actual vehicle to the estimation information based on statistics (step S25), and advances the process to step S28. In this way, when the vehicle model is not matched, the vehicle model corresponding to the target actual vehicle is added to the estimation information based on statistics, and therefore the estimation information based on statistics can be made to fit the actual situation.
On the other hand, in the case where the vehicle model matching the subject actual vehicle is selected in step S22 and the vehicle model matching the subject actual vehicle does not need to be reselected (step S22: no), the monitor 14 determines whether or not there is a divergence in the movement information of the subject actual vehicle and the vehicle model (step S26).
As a result, when the movement information of the subject actual vehicle and the movement information of the vehicle model are not separated (yes in step S26), the monitor 14 sets the vehicle matcher 13 to perform reselection (step S27), and the process proceeds to step S28, whereas when the movement information of the subject actual vehicle and the movement information of the vehicle model are not separated (no in step S26), the monitor 14 proceeds to step S28.
In step S28, the vehicle matcher 13 determines whether or not a predetermined set time has elapsed, that is, a time for which the OD data is to be corrected is summarized in a table format. The setting time may be set in minutes or any time.
As a result, when the predetermined set time has not elapsed (no in step S28), the vehicle matcher 13 advances the process to step S21, and further executes the process for the unprocessed real-time estimation information.
On the other hand, when the predetermined elapsed time has elapsed (yes in step S28), the vehicle matcher 13 executes a process of adjusting the number of vehicle models included in the estimation information based on the statistics (step S29). Specifically, for example, in a case where real-time estimation information about almost all vehicles can be acquired, the vehicle matcher 13 may delete information of a vehicle model that does not correspond to an actual vehicle. This makes it possible to delete information of the vehicle model that does not exist in this case from the estimated data based on statistics. In addition, when a new vehicle model is added in step S25, the vehicle matcher 13 may delete the information of the vehicle models that do not correspond to the actual vehicle, which is the same number as the number of added vehicle models. Thus, the total number of vehicle models based on the statistical estimation data is not changed, and the actual vehicle model can be obtained. In addition, when the ratio (the acquirable ratio) of the actual vehicle in which the real-time estimation information can be acquired is estimated, the number of vehicle models may be adjusted so that the ratio of the number of vehicle models corresponding to the actual vehicle to the total number of vehicle models is the same as or close to the acquirable ratio. Further, the process of step S29 is not necessarily performed.
Next, the vehicle matcher 13 generates a table of corrected OD data, here corrected OD data corresponding to a certain time range, based on the statistical-based estimated data and the matching relationship between the actual vehicle and the vehicle model (step S30). Specifically, the vehicle matcher 13 generates OD data by using movement information (such as position) corresponding to real-time estimation information of an actual vehicle, for a vehicle model matching the actual vehicle among pieces of information of vehicle models based on statistical estimation data. Thus, the information obtained from the actual vehicle can be included in the OD data without including the data of the vehicle model, and the OD data that reflects the actual situation with high accuracy can be generated.
The present invention is not limited to the above-described embodiments, and can be modified and implemented appropriately without departing from the scope of the present invention.
For example, in the above-described embodiment, the communication terminal to be the collection target of the mobile data is set as various communication terminals such as a mobile phone, a smart phone, or the like carried by a person, or a communication terminal mounted in a vehicle, but the present invention is not limited to this, and only the communication terminal mounted in the vehicle may be set as the target. In this case, the movement data from the vehicle may include destination information of the vehicle input to a navigation system or the like of the vehicle. In this way, when only the communication terminal mounted in the vehicle is targeted, it is not necessary to estimate whether or not the communication terminal is a communication terminal or the like existing in the vehicle by the vehicle estimator 12.
In the case where the destination information is included in the movement data, the vehicle matcher 13 may select a vehicle model that matches the vehicle on which the communication terminal is mounted, taking into account the destination of the destination information. This can prevent the vehicle from moving away from the vehicle model selected as the matching vehicle model due to a difference in destination, and can accurately associate the actual vehicle with the matching vehicle model, thereby further improving the accuracy of the OD data correction.
In the above embodiment, one matching vehicle model is selected for one actual vehicle, and the OD data is generated using the movement information of the actual vehicle instead of the movement information of the matching vehicle model, but the present invention is not limited to this, and for example, a plurality of matching vehicle models may be selected for one actual vehicle, and the OD data may be generated based on the movement information of the actual vehicle matched by the plurality of matching vehicle models instead of the movement information of the plurality of matching vehicle models.
In the above embodiment, the route/traffic distribution calculator 11, the vehicle estimator 12, the vehicle matcher 13, and the monitor 14 are provided in the same device, but the present invention is not limited to this, and these functional units may be provided in a distributed manner in a plurality of devices, and for example, the device provided with the route/traffic distribution calculator 11, the device provided with the vehicle estimator 12, the vehicle matcher 13, and the monitor 14 may be provided as different devices.
In the above embodiment, the traffic flow prediction support device 10 and the traffic flow simulator 5 are provided as separate devices, but the present invention is not limited to this, and the traffic flow prediction support device 10 and the traffic flow simulator 5 may be constituted by a single device.
In the above embodiment, part or all of the processing performed by the processor 102 may be performed by a hardware circuit, and the program in the above embodiment may be installed from a program source. The program source may be a program distribution server or a storage medium (e.g., a removable storage medium).
Symbol description
1: a traffic flow prediction system; 2: a past OD data storage unit; 3: a mobile data storage unit; 4: a corrected OD data storage unit; 5: traffic flow simulation device 6: a display device; 10: traffic flow prediction auxiliary device; 11: a path/traffic allocation calculator; 12: a vehicle estimator; 13: a vehicle matcher; 14: and a monitor.
Claims (10)
1. A traffic flow prediction support device is characterized by comprising:
an acquisition unit that acquires movement information of a plurality of vehicle models corresponding to each vehicle based on past OD data;
a vehicle selection unit that selects, based on movement information of a communication terminal estimated to be present in a predetermined actual vehicle, a vehicle model satisfying a predetermined condition as a matching vehicle model matching the actual vehicle with respect to the actual vehicle in which the communication terminal that transmitted the movement information is located;
an OD data generating unit that generates OD data based on movement information of the actual vehicle matched with the matched vehicle model, instead of movement information of the matched vehicle model; and
a divergence determination unit that determines whether or not movement information of the matched vehicle model and movement information of the actual vehicle matched with the matched vehicle model diverge,
when it is determined that the movement information of the matched vehicle model is deviated from the movement information of the actual vehicle matched with the matched vehicle model, the vehicle selecting unit selects a new vehicle model matched with the actual vehicle as a new matched vehicle model,
the OD data generating unit generates OD data based on the movement information of the new matched vehicle model.
2. The traffic flow prediction assistance device according to claim 1, wherein,
in the case where there is no matching vehicle model satisfying the predetermined condition, the vehicle selecting unit generates a vehicle model corresponding to the actual vehicle,
the OD data generating unit generates OD data including the generated movement information of the vehicle model.
3. The traffic flow prediction assistance device according to claim 1, wherein,
the communication terminal is a terminal mounted in an actual vehicle.
4. The traffic flow prediction assist device according to claim 3 wherein,
the movement information from the actual vehicle includes destination information indicating a destination of the actual vehicle,
the vehicle selecting unit selects a vehicle model that travels to the same destination as the destination indicated by the destination information of the actual vehicle as the matching vehicle model.
5. The traffic flow prediction assistance device according to claim 1, wherein,
the traffic flow prediction support device further includes: and a vehicle estimation unit that acquires the movement information transmitted from the communication terminal and estimates whether or not the communication terminal is a communication terminal existing in a predetermined actual vehicle based on the movement information transmitted from the communication terminal.
6. The traffic flow prediction assistance device according to claim 1, wherein,
the vehicle selection unit determines the type of the actual vehicle of the communication terminal based on the presence density of the communication terminal based on the movement information of the communication terminal, and selects a matched vehicle model based on the type of the actual vehicle.
7. The traffic flow prediction assistance device according to claim 6, wherein,
the movement information of the plurality of vehicle models based on the past OD data includes movement information of a bus model,
the vehicle selecting unit determines a communication terminal existing in a bus based on the movement information of the communication terminal, selects a bus model matching the bus,
the traffic flow prediction support device further includes: and an operation determination unit that determines and outputs a difference from the operation information of the bus based on the movement information of the operation information based on the bus model and the movement information of the bus matched with the bus model.
8. The traffic flow prediction assistance device according to claim 1, wherein,
the vehicle selecting section selects a plurality of matching vehicle models for the actual vehicle,
instead of the movement information of the plurality of matched vehicle models, the OD data generating unit generates OD data based on the movement information of the actual vehicle matched by the plurality of matched vehicle models.
9. A traffic flow prediction support method for a traffic flow prediction support device that generates OD data, characterized in that,
acquiring movement information of a plurality of vehicle models corresponding to each vehicle based on past OD data;
selecting a vehicle model satisfying a predetermined condition as a matching vehicle model matching a predetermined actual vehicle with respect to the actual vehicle in which the communication terminal that has transmitted the movement information is located, based on the movement information of the communication terminal that is estimated to be present in the predetermined actual vehicle;
generating OD data based on the movement information of the actual vehicle matched with the matched vehicle model instead of the movement information of the matched vehicle model; and
determining whether or not the movement information of the matched vehicle model is deviated from the movement information of the actual vehicle matched with the matched vehicle model, and when it is determined that the movement information of the matched vehicle model is deviated from the movement information of the actual vehicle matched with the matched vehicle model, selecting a new vehicle model matched with the actual vehicle as a new matched vehicle model, and generating OD data based on the movement information of the new matched vehicle model.
10. A computer-readable medium storing a traffic flow prediction assist program that is executed by a computer that generates OD data, the traffic flow prediction assist program causing the computer to function as:
an acquisition unit that acquires movement information of a plurality of vehicle models corresponding to each vehicle based on past OD data;
a vehicle selection unit that selects, based on movement information of a communication terminal estimated to be present in a predetermined actual vehicle, a vehicle model satisfying a predetermined condition as a matching vehicle model matching the actual vehicle with respect to the actual vehicle in which the communication terminal that transmitted the movement information is located;
an OD data generating unit that generates OD data based on movement information of the actual vehicle matched with the matched vehicle model, instead of movement information of the matched vehicle model; and
a divergence determination unit that determines whether or not movement information of the matched vehicle model and movement information of the actual vehicle matched with the matched vehicle model diverge, and when it is determined that movement information of the matched vehicle model and movement information of the actual vehicle matched with the matched vehicle model diverge, the vehicle selection unit selects a new vehicle model matched with the actual vehicle as a new matched vehicle model,
the OD data generating unit generates OD data based on the movement information of the new matched vehicle model.
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