JP2015076005A - Moving route estimation system moving route estimation method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To estimate a moving route even if probe information is information on partial routes (fragmentary routes).SOLUTION: A moving route estimation system includes: an information acquisition unit that acquires probe information; a probe-information storage unit storing the acquired probe information; a route estimation unit that connects fragmentary routes on the basis of positional information and time information so as to estimate a moving route on which a mobile terminal that outputs the probe information moves, if the probe information is the position information on origin-destination that is an origin and a destination of each of the fragmentary routes and the time information on the position information, the fragmentary routes being at least two partial routes; and an output unit that outputs positional information on an origin and a destination of the estimated moving route, time information on the positional information, and route information including means of transportation.

Description

  The present invention relates to a movement route estimation method for grasping traffic demand of an entire city for a plurality of transportation means such as roads and railways, and more particularly, a method of estimating a movement route from probe information acquired from a mobile terminal such as a smartphone. About.

  Conventionally, as a method for estimating the traffic demand of the entire city, there are used a non-aggregation model for traffic behavior for each individual, and an aggregation model for handling collective behavior for zones. Of these, the aggregation model uses a four-stage estimation method that is divided into stages such as prediction of traffic generation / concentration, prediction of distributed traffic, allocation of transportation means, and prediction of allocated traffic. Person behavior data used in the aggregation model is generally person trip survey data in the survey area. The person trip survey data is the daily traffic data (also called trip data) of each person such as the origin (Origin), the destination (Destination), the purpose of travel, the means of transportation, and the time. is there. The person trip survey data is data that can be used to examine the sharing of means of transportation, etc., since trips that have been moved using a plurality of means of transportation are obtained. However, the person trip survey data is currently being surveyed as needed for surveys, or as often as once every 10 years. Newly constructed urban structures such as facilities, roads, railways, etc. It is difficult to immediately reflect the change in human behavior due to the change in operation time in the model and use it for estimation. Especially in emerging countries, population growth and traffic development in urban areas are remarkable, and accordingly human behavior changes in a short time, so it is difficult to keep track of the substance through questionnaire surveys.

  In recent years, the use of a probe car is expected as a new method for grasping the traffic volume on the road. The probe car is a system that uses a vehicle as a sensor and acquires travel data such as travel speed and position from a traveling vehicle to generate road traffic information. By using a probe car, it is possible to obtain continuous observation data of the traveling speed (or travel time) of all road sections, but the traveling data collected from the probe car is based on the communication load (communication time, data volume, usage). In order to reduce (fee), it is composed of representative points that are thinned out according to a predetermined rule, not travel data collected frequently. Therefore, there is a need for some method for estimating the movement route from travel data (hereinafter referred to as probe information) such as the position, time, speed, etc. of the representative point obtained from the probe car.

  Patent Document 1 discloses a method for estimating a travel route of a vehicle from vehicle probe information collected using a probe car. The position between the two points is matched with the position on the map from the probe information, and the traveling route between the two points matched on the map is estimated by searching using the road network. If a plurality of routes are obtained as a result of the search, the estimated route obtained by the route search is compared with the actual travel distance obtained from the probe car, and the estimated route closest to the actual travel distance is traveled between the two points. A method for specifying the route is described. As the vehicle probe information, travel data from the start to the stop of the engine is mainly collected as a function of the car navigation device. Therefore, the travel route from the departure point O and the arrival point D where the user originally traveled is disclosed in Patent Document 1. Can be reproduced by the route estimation method disclosed in FIG.

JP 2003-178396 A (Patent No. 3770541)

  However, unlike the probe information of the vehicle, the probe information collected from the mobile terminal is not collected unless the user intentionally enables the probe function. Since only the movement data of the place and time zone where the user enabled the probe function is collected, if the route from the departure place where the user originally moved to the arrival place is the true movement route, the probe information of the mobile terminal is It becomes fragmentary probe information in which the true path is divided. The car navigation device is constantly powered, so there is no concern about battery consumption, but in the case of a mobile device, the battery consumption increases while the probe function is enabled. There is anxiety that major apps such as email, music, and web access will become unavailable due to battery exhaustion. Therefore, the user's intention to use the probe function as much as possible and to suppress the battery consumption during movement as much as possible works.

  Probe information collected from vehicles can be limited to travel routes on roads, but probe information collected from mobile terminals is not limited to roads and includes travel data in railways and facilities. It is necessary to estimate the route in consideration of various transportation methods and places. In particular, in order to obtain a series of travel routes from probe information traveled using a plurality of transportation means such as walking → bus → railway, it is necessary to consider the transportation means and its connection time (transfer time and waiting time). In the conventional route estimation method that targets only the probe information of vehicles, multiple transportation means and waiting time are not assumed, so if the transportation means are different or waiting time may occur, it may be treated as another moving route There is a problem that the true departure point and arrival point and the movement route between them cannot be estimated.

  In view of the above-described problems of the prior art, an object of the present invention is to provide a movement path estimation system that estimates a movement path from fragmentary probe information collected from a mobile terminal having a position detection function such as a smartphone or a mobile phone, and its Is to provide a method.

  The disclosed movement route estimation system includes an information acquisition unit that acquires probe information, a probe information storage unit that stores the acquired probe information, and a fragment route that includes at least two fragmentary routes (fragment routes). Based on the position information and the time information of the start and end points that are the start point and the end point, based on the position information and the time information, the fragment routes are combined with each other, and the movement route traveled by the mobile terminal that has output the probe information is estimated. And an output unit that outputs position information of the estimated start and end points of the travel route, time information thereof, and route information including transportation means.

  ADVANTAGE OF THE INVENTION According to this invention, even if probe information is a fragmentary path | route (fragment path | route) using the probe information collected from the portable terminal which has position detection functions, such as a smart phone and a mobile telephone, a movement path | route can be estimated.

  Problems, configurations, and effects other than those described above will be clarified by the following description of embodiments.

It is the figure which showed the whole structure of the traffic demand estimation system with which the movement route estimation method in connection with embodiment is performed. It is a processing flow of the movement path | route estimation part in the center apparatus of a traffic demand estimation system. It is a supplementary explanatory drawing for demonstrating the processing flow of a movement path | route estimation part. It is a processing flow of the upper limit time setting part for the stop time determination in the process of a movement path | route estimation part. It is a processing flow of the upper limit speed setting part for the movement speed determination in the process of a movement path | route estimation part. It is a processing flow of an OD zone calculation part. It is a supplementary explanatory diagram for explaining the processing flow of the OD zone calculation unit. It is a supplementary explanatory diagram for explaining the processing flow of the OD zone calculation unit. It is a processing flow of a traffic action estimation part. It is a supplementary explanatory drawing for demonstrating the processing flow of a traffic action estimation part. It is a processing flow of the traffic volume calculation estimation part in the process of a traffic action estimation part. It is an output example of the traffic volume (number of people moving) between OD zones estimated by the traffic behavior estimation unit. It is an output example of the traffic volume (number of people moving) between OD zones estimated by the traffic behavior estimation unit. It is an output example of the traffic volume (number of people moving) between OD zones estimated by the traffic behavior estimation unit.

  An outline of the present embodiment will be described. The movement route estimation system and the movement route estimation method of the present embodiment include at least two fragment routes (fragment routes) from fragment probe information obtained from a mobile terminal having a position detection function such as a smartphone or a mobile phone. Based on the position of each start / end point and its time information, the two fragment routes are connected to each other, and the original departure point O and arrival point D where the user carrying the mobile terminal has moved and the movement route between them are estimated.

  Specifically, the movement time and movement speed between the start and end points of the two fragment routes are calculated, and when the calculated movement time and speed are equal to or less than a predetermined value, the two fragment routes are regarded as components of the same movement route, The two fragment paths are connected using the start and end points as the connection points.

  The determination of the travel time between the start and end points is based on the average travel time (including stop time) calculated from past probe information in the vicinity of the start and end points, and the average POI (for example, in the station premises and in the store) near the start and end points. The reference value (upper limit value) is set for any one of the travel time, the transport means used on the travel route, and the travel time set for each transfer pattern (for example, railway → bus, bus → rail).

  The reference speed (upper limit value) is set for the moving speed between the start and end points based on the transportation means or the map information (for example, on the road or in the facility) near the start and end points.

  As a result, the moving route estimation method of the present invention can detect the start and end points of a fragment route even if the probe information collected from a mobile terminal having a position detection function such as a smartphone or a mobile phone is a fragmented route (fragment route). Based on the position and time, the travel time and travel speed between the start and end points are calculated and the routes are combined. Therefore, the original departure place O and arrival place D where the user carrying the mobile terminal has moved, the OD The route traveled between can be reproduced.

  Furthermore, since the reference time is set based on the past probe information, POI or map information, transportation means, transfer patterns, etc. existing in the vicinity, the moving time and moving speed between the start and end points can be set from the fragment route. It is possible to estimate the travel route using the means of transportation.

  Hereinafter, this embodiment will be described in detail with reference to the drawings.

<Overall configuration>
FIG. 1 is a diagram showing an overall configuration of a traffic demand estimation system in which a movement route estimation method according to an embodiment of the present invention is executed. This embodiment will be described below on the assumption that the movement route estimation method is performed in the center device 1 in the present embodiment.

  In FIG. 1, a center apparatus 1 according to the present embodiment is connected to a communication network 2 and connected to a mobile terminal 5 such as a smartphone or a mobile phone via a base station 3. The portable terminal 5 is used in places (including moving means) such as a train 6, a car 7, and a walk 4, and is connected to the center device 1. The portable terminal 5 includes a display unit 50, a communication unit 51, a main body unit 52, an operation unit 53, a GPS reception unit 54, a storage unit 55, a photographing unit 56, and the like. The portable terminal 5 is a computer centered on the main body 52 that is executed by the terminal CPU. The display unit 50 is configured by an LCD (Liquid Crystal Display) or the like. The communication unit 51 is connected to the center apparatus 1 through data communication by wireless communication via the base station 3 and the communication network 2. The GPS receiver 54 receives radio waves from GPS satellites and detects the current position of the mobile terminal 5. The photographing unit 56 is connected to a camera (not shown) and captures image information photographed by the camera. The storage unit 55 is configured by a memory card or the like, and stores image information captured by the imaging unit 56 and position information obtained by the GPS receiving unit 54.

  The main body 52 includes a communication interface unit 521, an information acquisition unit 522, a storage unit 523, a position information acquisition unit 524, an image information acquisition unit 525, an input / output interface unit 526, an information provision unit 527, and the like. Each part of the main body 52 is realized by the main body 52 executing a predetermined program stored in a program memory (not shown).

  The communication interface unit 521 performs communication control with respect to the communication unit 51 that performs communication using a mobile phone network, a wireless LAN, and the like, and transmits and receives data to and from the center apparatus 1 via the base station 3 and the communication network 2. The input / output interface unit 526 converts input information from the switches, buttons, voice, touch panel, and the like of the operation unit 53 into various information such as an information request and provision to the center device 1 and inputs the information to the main body unit 52. Display information and audio information are output to the display unit 50 and / or the operation unit 53.

  The location information acquisition unit 524 stores GPS information detected by the GPS reception unit 54 such as latitude and longitude, altitude (position information), and time information of the mobile terminal 5 as movement data (probe information) that provides the center device 1. Stored in the unit 55. Although not shown, a position detection sensor such as a gyroscope or an acceleration sensor is further provided, and the attitude detection of the mobile terminal and GPS information may be used for correction.

  The information acquisition unit 522 acquires various information such as a route and operation information transmitted from the center device 1 via the communication unit 51 and the communication interface 521. The information acquisition unit 512 stores the acquired information in the storage unit 523.

  The image information acquisition unit 56 adds the position information and time information acquired from the position information acquisition unit 524 and the shooting direction to the image information shot by the camera connected to the shooting unit 56 and stores the information in the storage unit 523. You may make it provide to the center apparatus 1 as probe information.

  When the information is requested by the input / output interface unit 526 or at a specified time, the information providing unit 527 moves the location acquired by the position information acquisition unit 524 (a series of data including position and time), an image, and the like. Image information with position information obtained from the acquisition unit 525 is read from the storage unit 523 and transmitted to the center apparatus 1 as probe information.

  Although not shown, the center device 1 is configured by a so-called computer (information processing device) including a center CPU and a storage device including a semiconductor and a hard disk device. As shown in FIG. 1, the center device 1 includes a communication interface unit 11, an information acquisition unit 12, a movement route estimation unit 13, an OD zone calculation unit 14, a traffic behavior estimation unit 15, an output unit 16, an information provision unit 17, a probe. An information storage unit 20, an OD storage unit 21, a route storage unit 22, map data 23, and the like are included.

  The information acquisition unit 12, the travel route estimation unit 13, the OD zone calculation unit 14, the traffic behavior estimation unit 15, the output unit 16, and the information provision unit 17 are realized by executing a predetermined program stored in the program memory. . The probe information storage unit 20, the OD storage unit 21, the route storage unit 22, and the map data 23 are included in the storage device.

  The communication interface unit 11 performs communication control corresponding to the communication network 2 and transmits / receives data to / from the mobile terminal 5 via the communication network 2.

  The information acquisition unit 12 accepts a request transmitted from the mobile terminal 5 or receives user movement data including position information (coordinates such as latitude and longitude, time) detected by the mobile terminal 5 via the communication interface unit 11. And stored in the probe information storage unit 20.

  The movement route estimation unit 13 reads the probe information acquired from the portable terminal 5 from the probe information storage unit 20, combines the fragmented routes if they exist, and the original route (true Route) is reproduced, and the reproduced true route data is stored in the route storage unit 22, and its departure point O and arrival point D are stored in the OD storage unit 21.

  The OD zone calculation unit 14 reads the positions of O and D on the true path from the OD storage unit 21, calculates the area where O concentrates, the area where D concentrates, or the area where O and D concentrate, and sets the OD zone. Generate. At this time, the map information of each zone or an area including the vicinity thereof is read from the map data 23, and O to D exist in the area at a high density, or exist in the vicinity of a point representing the zone such as the center point of the zone. POI (Point Of Interest) and facility information are searched and used as additional information for each zone. In addition, a network (inter-zone traffic network) connecting the generated zones is generated.

The traffic behavior estimation unit 15 includes the OD zone network and OD zone information (zone representative point, area (mesh code or radius from the representative point) generated by the OD zone calculation unit 14, and the positions of O to D constituting the zone. Information, valid time of the zone, etc.) and estimate the traffic volume between networks or the traffic distribution amount for each means of transportation.
The output unit 16 outputs the distribution of traffic means between each OD zone generated by the OD zone calculation unit 14 and each zone estimated by the traffic behavior estimation unit 15 and each traffic volume to a display device or the like. The traffic volume is also provided to an external system (or terminal or device) via the information providing unit 17 and the communication interface unit.

  FIG. 2 shows a processing flow of the movement route estimation unit 13 in the center device 1 of the traffic demand estimation system according to the present embodiment. FIG. 3 is a supplementary diagram for explaining the processing flow of FIG.

  The movement path estimation unit 11 reads the probe information collected from the mobile terminal 5 such as a smartphone or a mobile phone from the probe information storage unit 20 and combines the fragmented paths to generate a true path originally traveled by the user. Then, the positions and times of the departure point O and the arrival point D, and the route between the ODs are output to the OD storage unit 21 and the route storage unit 22. Hereinafter, each step will be described in detail.

  First, the movement route estimation unit 11 reads a fragment route from the probe information unit 20 (S200). The movement route estimation unit 11 sorts the read fragment routes based on the time information of the fragment route start point O ′ for each identification ID for identifying the mobile terminal 5 or the user (S201). The movement route estimation unit 11 reads the time and position of the end point D ′ (i) of the fragment route i (30 in FIG. 3) and the start point O ′ (i + 1) of the next fragment route i + 1 (31 in FIG. 3) (S202). ). The movement path estimator 11 selects the end point D ′ (i) of the fragment route i (30 in FIG. 3) and the point-to-point D ′ (i) for the start point O ′ (i + 1) of the fragment route i + 1 (31 in FIG. 3). ) → O ′ (i + 1) is calculated (S203). The straight line distance between points is used as the movement distance between points. Alternatively, the point is matched with a map (a road, a railway line, etc.) read from the map data 23, a route between the two points on the map is searched to estimate a traveling route, and the obtained traveling route on the map is obtained. Calculate travel distance. If the travel distance is included in the probe information, it may be used as the travel distance. When the movement route estimation unit 11 determines that a point between the points where the moving speed V is equal to or less than a predetermined value (for example, 1 km / h or less) is to be stopped (S204), it acquires the means of transportation between the points from the probe information, or The transportation means is estimated from the map information (on the road or on the railway), and the upper limit time Ts is set according to the estimated transportation means (S205). The processing of the upper limit time setting unit for setting Ts will be specifically described with reference to FIG. When the stop time T between the points exceeds the upper limit value Ts (S206 is yes), the movement route estimation unit 11 determines that the points do not become a connection point (that is, the fragment routes i and i + 1 are different). The process from S202 is repeated until all fragment paths with the same ID are processed in S210 (S210 is no).

  When the movement route estimation unit 11 determines that the movement is between the points (S204), the movement route estimation unit 11 sets an upper limit speed Ve based on the location between the points and the transportation means of the fragment routes i and i + 1 (S207). The processing of the upper limit speed setting unit for setting Ve will be specifically described with reference to FIG. If the movement speed V between the points is equal to or less than the upper limit speed Ve (S208 is yes), the movement route estimation unit 11 is a fragment route that forms the same route, and the points between the points are joined points. Are combined (S209). Since the moving speed V between the points where the moving speed V between the points exceeds the upper limit speed Ve (S208 is no) becomes a moving speed that cannot be the transportation means, the moving path estimation unit 11 must be a connecting point between the points. The determination is made (that is, the fragment routes i and i + 1 are determined to be different travel routes), and the processing from S202 is repeated until all fragment routes having the same ID are processed in S210 (S210 is no).

  When the movement route estimation unit 11 finishes the processing for all fragment routes having the same ID (S210 is yes), the combined route and the information of O ′ and D ′ are converted into the original movement route, the departure point O, and the arrival point. D is output to the OD storage unit 21 and the route storage unit 22 (S211). If the movement path estimation unit 11 has finished the process for all the fragment paths of the probe information stored in the probe information storage unit 20 (S212 is yes), the process ends.

  In this way, the fragment route 30 and the fragment longitude 31 shown in FIG. 3 are combined as a route where Di and Oi + 1 are combined as a route forming the same route, and a moving section 32 exists between them, and a series of routes is generated. . The moving section 32 can be estimated using the method disclosed in Patent Document 1, but since Patent Document 1 is only for roads, in order to generate the moving route 32, which transportation means is further searched. It is necessary to specify whether or not. This can be estimated to some extent by map information (for example, on a road or on a track) where Di and Oi + 1 exist, or by means of transportation of fragment routes i and i + 1 (for example, a car or a railroad).

  In this process, a fragment route 33 that cannot be reached in time from the fragment route 30 is treated as another movement route even with the same ID. Further, the fragment route 34 is determined to be stopped based on the moving speeds of Oi + 2 and Di + 1, and when the stop time exceeds the upper limit time (such as in a store or staying at a hotel), it is treated as another moving route, and Di + 1 is a fragment route 31. Is the arrival point D of the travel route that includes.

  FIG. 4 is a process flow of the upper limit time setting unit that sets the upper limit time Ts of the stop determination of S205 in the process flow of the movement route estimation unit 13 of FIG.

  The upper limit time setting unit determines a moving situation (for example, transfer or stay in a facility) based on the traffic means of the fragment route and the positions of the start and end points O ′ and D ′, and determines the fragment route that is a candidate for combination. An upper limit time Ts for determining whether to treat as a series of movement routes or other movement routes is set.

  First, the upper limit time setting unit sets the initial value Tinit to the upper limit time Ts. Next, the process is distributed according to a preset setting method (S401).

  Method 1 is a method in which an average stop time is acquired from probe information in the vicinity where the start and end points of the fragment path exist and is set to the upper limit time Ts. The upper limit time setting unit reads probe information existing in the vicinity of the start point D ′ (i) of the fragment route i or the end point O ′ (i + 1) of the fragment route i + 1 (S402). Here, the start and end points O ′ and D ′ of all fragment paths including the fragment paths of other IDs to be read are used. If there are no fragment routes in the same time zone, the fragment routes in other time zones are also targeted. The upper limit time setting unit executes S403 for the read start and end points O 'and D' in the vicinity of D '(i) to O' (i + 1), and detects a stop point (S403). This is executed for all read fragment paths (S404). The upper limit time setting unit calculates the average stop time Tav at the stop point obtained in S404 (S405), and if the average stop time Tav is within the preset maximum time Tmax (S406 is yes), Tav is set to Ts. Setting is made (S407). If Tav exceeds Tmax (S406 is no), Ts is not updated by Tav and the process is terminated (not shown, but in this case, another method 2 or method 3 may be executed).

  Method 2 is a method of setting the upper limit time Ts based on the nearby POI or map information where the start and end points of the fragment path exist. The upper limit time setting unit reads from the map data 23 the POI or map information existing in the vicinity of the start point D ′ (i) of the fragment route i or the end point O ′ (i + 1) of the fragment route i + 1 (S408). The upper limit time setting unit acquires the waiting time in the read POI or map information and sets it as Tav (S409). For example, if the acquired information is map information and a facility such as a station, the average waiting time at the station is Tav. The waiting time in the facility is calculated in advance for each facility or each category. Even in the category of the same station, the vehicle operation interval varies depending on the area and time zone in which the station exists. The same applies to the case where the acquired information is POI, and the average staying time of a store, an amusement facility, etc. is acquired and set as Tav. The waiting time at such a facility or POI may be acquired from an external system via the communication interface unit 11. Subsequent processing is the same as in Method 1, and if the average stop time Tav is within the preset maximum time Tmax (S406 is yes), Tav is set to Ts (S407). If Tav exceeds Tmax (S406 is no), Ts is not updated by Tav, and the process ends.

  Method 3 is a method of setting the upper limit time Ts based on the transportation means of the fragment route. The upper limit time setting unit identifies whether or not the transportation means of the fragment route i + 1 is public transportation (S410). Here, it is assumed that the means of transportation of each fragment route is provided as probe information. However, when the information is not obtained from the probe information, the route shape data is read from the map data 23 and matches the fragment route i + 1. Identify the route of the section (eg road or track). If it is a track, the segment route i + 1 is assumed to be public transportation. If it is a road, a plurality of means of transportation such as buses, taxis, and private cars are candidates. Therefore, the average speed of the bus and other vehicles in the section (or nearby route) is calculated in advance, and the one closer to the moving speed of the fragment route i + 1 is selected as the transportation means of the fragment route i + 1. The upper limit time setting unit sets the average waiting time (transfer time) of the public transportation selected in S410 to Ts (S411). Here, the average waiting time is calculated in advance for each public transport, or is acquired from an external system via the communication interface 11. Since the operation time of public transport varies depending on the region and time zone, the waiting time is set in consideration thereof. The upper limit time setting unit sets an appropriate waiting time to Ts according to the transportation means (car, motorcycle, walking, etc.) other than public transportation for the fragment route i + 1 (S412).

  FIG. 5 is a process flow of the upper limit speed setting unit that sets the upper limit speed Ve of the abnormal speed determination of S207 in the process flow of the movement route estimation unit 13 of FIG.

  The processing of the upper limit speed setting unit sets an upper limit speed Ve for detecting an impossible movement (abnormal speed) from the positions of the start and end points O ′ and D ′ of the fragment route and the transportation means used.

  First, the upper limit speed setting unit sets the initial value Vinit to Ve (S500). The upper limit speed setting unit refers to the map data 23 and obtains map information in which the start point D ′ (i) of the fragment route i or the end point O ′ (i + 1) of the fragment route i + 1 exists (S501). The upper limit speed setting unit identifies in which category on the map the start and end points exist from the acquired map information (S502). If the category is on the road, the upper limit speed setting unit identifies one of the transportation means used on the road (S503). In this process, it is assumed that the transportation means of each fragment route is also provided as probe information, but the moving speed or required time from the start point D ′ (i) to the end point O ′ (i + 1) is calculated. There is also a method of comparing the travel speed of the transportation means that can move from the start point D ′ (i) to the end point O ′ (i + 1) and the required time, and identifying the most similar transportation means as the transportation means of this section. Therefore, the upper limit speed setting unit sets the moving speeds Vv and Vw corresponding to the transportation means as the upper limit speed Ve (S505, S506). In this example, the vehicle and the walk are shown as typical transportation means on the road, but other transportation means such as buses and two-wheels are the same. When the start / end point exists on the track (S502) and the means of transportation is railway (S507 is yes), the upper limit speed setting unit sets the average moving speed Vt when using the railway as the upper limit speed Ve. (S508). Here, the means of transportation is identified in the same manner as S503.

  If the start / end point is in the facility (S502) and the transportation means is walking (S509 is yes), the upper limit speed setting unit sets the average speed Vb of walking movement in the facility to the upper limit speed Ve.

  FIG. 6 is a processing flow in which the OD zone calculation unit 14 sets the OD zone based on the departure point O, the arrival point D, and the movement route therebetween. 7 and 8 are supplementary diagrams for explaining the processing flow of FIG.

  The OD zone calculation unit 14 reads position information of O and D corresponding to a predetermined time zone (for example, weekdays AM 8:00 to 9:00) from the OD storage unit 21 (S600). Based on the position information of O, the OD zone calculation unit 14 generates a group of Os existing in the vicinity (S601). For group generation, a classification method such as cluster analysis is used. O is classified into several groups, and the group organization is repeated so that the sum of the distances from the center point of each group to each O forming the group is minimized to generate a plurality of groups. Here, among a plurality of generated O groups, a group in which O concentrates is defined as an O zone having a high trip occurrence frequency (movement occurrence frequency). For example, as the O zone, a group in which a predetermined number or more of O exist and a group in which the average distance between the center point and each O is within a predetermined value (O is concentrated in a narrow area) are selected. For example, in FIG. 7, a point indicated by “◯” like a point 700 is O. Reference numerals 701, 704, 706, and 708 denote groups generated by being classified by neighboring Os. The “x” in each group is the center point. 701, 704, and 706 where a large amount of O exists are O zones.

  The OD zone calculation unit 14 executes the same process as S601 based on the position information of D, and generates a D zone with a high trip occurrence frequency (S602). For example, in FIG. 7, a point indicated by “Δ” as the point 702 is D. 703, 705, and 707 are D zones.

  The OD zone calculating unit 14 generates an OD zone by associating the O zone and the D zone that are close to the center point between the O zone generated in S601 and the D zone generated in S602 (S603). In FIG. 8, 800 is an OD zone in which the O zone 704 and the D zone 705 are associated, and 801 is an OD zone in which the O zone 706 and the D zone 707 are associated. The O zone 701 and the D zone 703 are O-only and D-only zones, respectively.

  The OD zone calculation unit 14 generates a traffic network that connects the zones generated in S603 (S604). Since the O zone 701 is a zone where a trip occurs, the O zone 701 is connected to another zone in the outflow direction as indicated by 802. Since the D zone 703 is a zone where trips are concentrated, it is connected to other zones in the inflow direction as indicated by 803. Since the OD zone is a zone where both generation and concentration occur, they are connected by an inflow / outflow network.

  S600 to S605 are repeatedly executed until a zone for all designated time zones is generated (S606).

  By the processing of the OD zone calculation unit 14, an OD zone and a traffic network for each time zone are generated. That is, as shown in 710 in FIG. 7 and 810 in FIG. 8, different OD zones and traffic networks are generated depending on the time zone, so it is possible to roughly grasp the occurrence and concentration of traffic and the traffic movement between them as time elapses. become able to.

  FIG. 9 is a processing flow for estimating the traffic volume between the OD zones in the traffic behavior estimating unit 15. FIG. 10 is a supplementary explanatory diagram for explaining the processing flow of FIG. 9. Here, an example for calculating the traffic volume between zones connected by the traffic network generated by the OD zone calculation unit 14 is shown.

  First, the traffic behavior estimation unit 15 reads from the route storage unit 22 a travel route that takes O belonging to the designated zone as a departure point and D as an arrival point (S900). The traffic behavior estimating unit 15 matches the read travel route with the map data (route data such as roads and tracks) read from the map data 23, and generates route information composed of the route data (S901). The route data includes node or link IDs such as roads and tracks, and attribute information such as distances, route names and numbers. Based on the route information obtained in S901, the traffic behavior estimation unit 15 determines a transportation means that occupies most of the travel route (S902). The traffic behavior estimation unit 15 evaluates the similarity of travel routes for each means of transportation and classifies the travel routes with high similarity (S903). In the evaluation of similarity, for example, the sum of distances using the same route and the same type (road type in the case of roads) in each moving route, or the ratio of the number of links constituting the same route to each moving route is a predetermined value. Assume that the above paths have high similarity. Here, as shown in FIG. 10, when the network from the O zone 701 to the D zone 703 is targeted, the route 1000 is a route between them according to the transportation means and based on the similarity of the route. Class. Here, when the transportation means is an automobile, a plurality of different travel routes from the O zone 701 to the D zone 703 are used only for roads. In that case, the travel route composed of roads is classified based on similarity. For example, in the routes 1000 to 1002, the route 1000 and the route 1001 are roads, and the route 1002 is a route mainly using the railway. The traffic behavior estimation unit 15 estimates the traffic volume or the number of people traveling for each route classified in S903 (S904). The traffic behavior estimating unit 15 calculates the traffic volume or the number of people moving from the O zone to the D zone from the traffic amount or the number of people on each classification route estimated in S903 (S905). The processes in S900 to S905 are repeated until the traffic volume between the designated zones or the number of moving persons is calculated (S906).

  FIG. 11 is a processing flow of the traffic estimation unit that calculates the traffic of each classification route in S905 in the processing flow of the traffic behavior estimation unit 15 that estimates the traffic between the OD zones of FIG.

  The process of the traffic volume estimation unit is an example of a process for calculating the traffic volume for each route classified in S904 of the traffic behavior estimation unit 15. Although there are various methods for estimating the traffic volume between zones, a simple method is used here as long as the rough traffic volume and its increase / decrease trend can be grasped as time elapses.

  The traffic estimation unit refers to the route information for each route classified in 904 (S1100). If the route information is a route using a road (S1101 is yes), the traffic estimation unit assumes that the route connecting the ODs is composed of roads of the same condition, and calculates the average speed from the route information. And the traffic volume Q of the route is obtained based on road characteristics generally indicated by speed and traffic volume (traffic density) (S1102).

Here, the road characteristics will be described. The traffic volume Q (vehicles / h) of the road is the traffic density k (vehicles / km), which is the number of vehicles existing within a unit distance at a certain moment, and the average speed v (km / h) of vehicles passing through the unit distances. ) To (Equation 1).
Q = k × v (Formula 1)
Since it is difficult to directly observe the traffic volume Q or the traffic density k in all road sections, for example, using a traffic model based on the correlation between the traffic density k and the travel speed v, the traffic density can be calculated from the travel speed v. It is possible to estimate k. In the traffic model, various models have been conventionally used, for example, an underwood model shown in (Expression 2) and a Greenberg model expressed in (Expression 3).
v = vf × exp (−k / k0) (Formula 2)
Here, exp () represents an exponential function, Vf represents a free speed at which the traffic density is zero, and k0 represents a critical density which is the traffic density when the traffic volume is maximum.
v = c × log (kj / k) (Formula 3)
Here, log is a logarithm, c is a constant, and kj is a saturation density that is a traffic density when the speed is zero. Further, when the relationship between the speed v and the traffic density k is expressed by an exponential function, for example, it is expressed by (Expression 4) where α and β are constants.
v = α × exp (β × k) (Formula 4)
Thus, the average speed v and the traffic density k can be expressed by their relational expressions and parameters. In the Underwood model of (Equation 2), these parameters are represented by a free velocity Vf and a critical velocity k0, and in the Greenberg model of (Equation 3), a constant c, a saturation density kj, and an exponential function (Equation 4). ) Are constants α and β. Here, these relational expressions are called road characteristics, and the parameters are called road characteristic parameters. When the target is road traffic, the traffic behavior estimation unit 15 estimates the road characteristic parameters of the representative road section. Here, although the underwood traffic model of (Equation 2) is shown as the road characteristic, the traffic model is not limited to the underwood, and the same applies when other traffic models are used.

  In step S1102, the traffic volume estimation unit calculates typical constants α and β for each road type in advance, and estimates the traffic volume Q using the traffic model of the road type of the main road constituting the route from the route information. To do.

The traffic volume estimation unit calculates the number of people Pj traveling along the route from the traffic volume Q (= the number of vehicles) and the preset average number of rides m (Equation 5) (S1103).
Pj = Q × m (Formula 5)
If the road information does not correspond to a road that uses a road (S1101 is no), the traffic volume estimation unit determines that the route is via the communication interface 11 if the route information is a route that uses a railroad (S1104 is yes). Survey information such as the number of passengers in the section being used or the occupancy rate is acquired from an external system, and the number of people Pj is set (S1105).

  The traffic volume estimation unit outputs the number of people Pj traveling on the classified route (S1106), and repeats S1100 to S1106 until the traffic volume of all the classified routes moving between the zones has been estimated (S1107).

  For routes that are neither roads nor railroads, the traffic estimation unit determines the number of people traveling on the classified route as unknown (S1108).

  In this example, the approximate amount of traffic (number of people) traveling between zones depending on whether the route uses roads or railroads is used for transportation such as motorcycles, walking, buses, and ships. Accordingly, it is possible to estimate the traffic volume of each moving route by combining with the method of estimating the number of users.

  12, 13, and 14 are output examples of the traffic volume (number of people moving) between the OD zones estimated by the traffic behavior estimation unit 15 in the output unit 16. The estimation result output to the output unit 16 is also provided to an external user via the information providing unit 17 and the communication interface unit 11.

FIG. 12 is an example in which a traffic network connecting each zone and the traffic volume between the zones are expressed in a macro manner. In a certain time zone t, the traffic volume (number of people) P (z1, z2, t) moving from the O zone z1 (701) to the D zone z2 (703) is the traffic volume of each classification route obtained in S904 ( It is calculated by (Expression 6) according to (number of people traveling) (S905 of the traffic behavior estimating unit 15).
P (z1, z2, t) = ΣPj (Expression 6)
FIG. 13 is an output example in which the throttle traffic volume is output as the inflow volume into the zone 1300. The inflow traffic volume into the zone 1300 is represented by the outflow traffic volume from the zones 1301, 1302, and 1303. Reference numeral 1304 indicates the date and time when the zone relationship and the traffic volume were obtained. Reference numeral 1305 denotes information added to the zone. By displaying such additional information together with the zone, an effective tool (system) for examining the reason why the traffic volume is concentrated in each zone can be provided. Become.

  FIG. 14 is an output example in which the travel route from the specific zone 1302 to 1300 in FIG. 13 and the estimated traffic of each travel route are output. By switching the estimated traffic volume to the detailed display as shown in FIG. 12 to FIG. 13 and FIG. 14 in accordance with an input instruction such as an enlarged display, a tool (system) that can grasp the moving traffic between the zones more easily understood. Can be provided.

  According to the described embodiment, even if the probe information is a fragmentary route (fragment route) using probe information collected from a mobile terminal having a position detection function such as a smartphone or a mobile phone, the fragment route Based on the position and time of the start and end points, the travel time and the movement speed between the start and end points are calculated and the paths are combined. Therefore, the original departure point O and the arrival point D where the user carrying the mobile terminal has moved The traveled path between the ODs can be reproduced.

  Furthermore, since the reference time is set based on the past probe information, POI or map information, transportation means, transfer patterns, etc. existing in the vicinity, the moving time and moving speed between the start and end points can be set from the fragment route. The original travel route using the means of transportation can be estimated.

  1: Center device, 2: Communication network, 3: Base station, 4: Person, 5: Mobile terminal, 6: Railway, 7: Automobile, 11: Communication interface unit, 12: Information acquisition unit, 13: Travel path estimation unit , 14: OD zone calculation unit, 15: traffic behavior estimation unit, 16: output unit, 17: information providing unit, 20: probe information storage unit, 21: OD storage unit, 22: route storage unit, 23: map data.

Claims (20)

  An information acquisition unit that acquires probe information, a probe information storage unit that stores the acquired probe information, and the probe information is a start point and an end point of the fragment path of at least two fragment paths (fragment paths) A path for estimating a moving path along which the mobile terminal that has output the probe information is moved by combining the fragment paths based on the position information and the time information when the position information is the start / end position information and the time information A movement route estimation system comprising: an estimation unit; and an output unit that outputs position information of the estimated start and end points of the movement route, time information thereof, and route information including transportation means. The movement route estimation system according to claim 1,
The route estimation unit calculates a stop time of the start / end point from the position information and the time information of the start / end points of the fragment route, and is preset according to the calculated stop time and the movement means of the fragment route The fragment route is combined based on the upper limit value of the stopped time. The movement route estimation system according to claim 1,
The route estimation unit reads point information (POI) existing in the vicinity of the start and end points of the fragment route from map data, sets an average waiting time related to the POI as an upper limit value of the stop time, A travel route characterized in that the stop time of the start / end point is calculated from the position information and the time information of the start / end points, and the fragment routes are combined based on the calculated stop time and the upper limit value. Estimation system. The movement route estimation system according to claim 1,
The route estimation unit sets an upper limit of a stop time from the probe information from the vicinity of the start and end points of the fragment route, and determines the start and end points from the position information and the time information of the start and end points of the fragment route. A travel route estimation system, wherein a stop time is calculated, and the fragment routes are combined based on the calculated stop time and the upper limit value. The movement route estimation system according to claim 1,
The route estimation unit calculates a movement speed between the start and end points of the fragment path, and combines the fragment paths based on the movement speed and a preset upper limit speed. system. The movement route estimation system according to claim 5,
The said route estimation part acquires the map information of the position where the said start / end point of the said fragment route exists, and sets the said upper limit speed according to the acquired said map information, The movement route estimation system characterized by the above-mentioned. The movement route estimation system according to claim 6, wherein
The said route estimation part sets the said upper limit speed according to the moving means between the acquired said map information and the said start and end of the said fragment route, The moving route estimation system characterized by the above-mentioned. The movement route estimation system according to claim 1,
Based on the estimated position information of the departure place O and the arrival place D of the travel route, the departure place O and the arrival place D existing in a predetermined area are separated from each other in the zone between the departure places O, and The moving route estimation system further comprising: an OD zone generation unit that classifies in any of the zones of the arrival destinations D that are close to each other and generates an OD zone according to the classification result. The movement route estimation system according to claim 8, wherein
The OD zone generation unit includes an average distance from the center point of the generated OD zone to the departure point O and the arrival point D, and the number of the departure point O and the arrival point D constituting the OD zone. A moving route estimation system characterized in that an OD zone having a high trip occurrence frequency is set based on at least one of them. The movement route estimation system according to claim 8, wherein
A traffic behavior estimation unit that generates a traffic network having the generated OD zone as a node and estimates a traffic volume between the OD zones of the generated traffic network based on at least the probe information. Travel path estimation system. A movement path estimation method in a movement path estimation system, wherein the movement path estimation system includes:
Get probe information,
Storing the acquired probe information;
When the probe information is the position information and the time information of the start and end points that are the start and end points of the fragment path of at least two fragmentary paths (fragment paths), based on the position information and the time information, Estimating the movement path that the mobile terminal that has output the probe information has moved by combining the fragment paths,
A travel route estimation method comprising: outputting the estimated location information of the start and end points of the travel route, time information thereof, and route information including transportation means. The movement route estimation method according to claim 11, wherein the movement route estimation system includes:
Calculate the stop time of the start and end points from the position information and the time information of the start and end points of the fragment path,
A movement path estimation method, comprising: combining the fragment paths based on the calculated stop time and an upper limit value of a stop time set in advance according to the movement means of the fragment paths. The movement route estimation method according to claim 11, wherein the movement route estimation system includes:
Point information (POI is read from map data) existing near the start and end points of the fragment route,
Set the average waiting time for the POI to the upper limit of the stop time,
Calculate the stop time of the start and end points from the position information and the time information of the start and end points of the fragment path,
The movement route estimation method, wherein the fragment routes are combined based on the calculated stop time and the upper limit value. In the movement route estimation method according to claim 11,
The movement path estimation system sets an upper limit value of the stop time from the probe information from the vicinity of the start and end points of the fragment path,
Calculate the stop time of the start and end points from the position information and the time information of the start and end points of the fragment path,
The movement route estimation method, wherein the fragment routes are combined based on the calculated stop time and the upper limit value. In the movement route estimation method according to claim 11,
The movement route estimation system calculates a movement speed between the start and end points of the fragment route,
A moving route estimation method, wherein the fragment routes are combined based on the moving speed and a preset upper limit speed. The movement route estimation method according to claim 15,
The movement path estimation system acquires map information of a position where the fragment path has the start and end points, and sets the upper limit speed according to the acquired map information. The movement route estimation method according to claim 16, wherein
The movement route estimation system sets the upper limit speed according to the movement means between the acquired map information and the start and end points of the fragment route. In the movement route estimation method according to claim 11,
The movement route estimation system is configured to determine the departure point O and the arrival point D existing in a predetermined area based on the estimated position information of the departure point O and the arrival point D of the movement route. A moving route estimation method, characterized in that classification is performed between the departure points O and the arrival points D existing in the vicinity of each other, and an OD zone is generated according to the classification result. The movement path estimation method according to claim 18,
The movement route estimation system calculates an average distance from the center point of the generated OD zone to the departure point O and the arrival point D, and the number of the departure point O and the arrival point D constituting the OD zone. A moving route estimation method characterized in that an OD zone having a high trip occurrence frequency is set based on at least one of them. The movement path estimation method according to claim 18,
The movement route estimation system generates a traffic network having the generated OD zone as a node,
A movement route estimation method, wherein the traffic volume between the OD zones of the generated traffic network is estimated based on at least the probe information.

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