CN107533797B - Arrival time prediction device, arrival time prediction system, and arrival time prediction method - Google Patents
Arrival time prediction device, arrival time prediction system, and arrival time prediction method Download PDFInfo
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- CN107533797B CN107533797B CN201580078801.2A CN201580078801A CN107533797B CN 107533797 B CN107533797 B CN 107533797B CN 201580078801 A CN201580078801 A CN 201580078801A CN 107533797 B CN107533797 B CN 107533797B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
- G08G1/127—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams to a central station ; Indicators in a central station
- G08G1/13—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams to a central station ; Indicators in a central station the indicator being in the form of a map
Abstract
The present invention obtains a travel speed (detection speed) of each road section from travel data (detection data) collected from a plurality of general vehicles, and estimates a traffic volume of a predetermined road section from the detection speed. Next, the estimated traffic volume (estimated traffic volume) is used to estimate the hybrid traffic travel speed in each road section in the case of hybrid traffic in which the ordinary vehicle and the road public traffic are assumed to coexist. Then, the estimated value of the hybrid traffic travel speed is used to predict the arrival time and delay time to a predetermined point when the road public traffic travels on a predetermined route.
Description
Technical Field
The present invention relates to an arrival time prediction device, an arrival time prediction system, and a method for predicting an arrival time of road public transportation at a predetermined point when hybrid transportation in which a normal vehicle and road public transportation are assumed to coexist is assumed from travel data (probe data) of a normal vehicle.
Background
Public road transportation is a means of transportation that transports an unspecified large number of passengers along a road according to a route and an operation schedule specified along the road, as typified by a tram, a bus, and the like. Even if a congestion occurs on a road scheduled to travel, such road public transportation does not take a detour action to avoid the congestion, and therefore, the road public transportation is easily involved in the congestion and the on-time operation is difficult to achieve. Thus, for example, in the route bus, there have been previous chin location systems: the current position is detected by a position detection device using a GPS (Global Positioning System) or the like mounted in the vehicle, or the current position of the bus is detected by a communication device provided in the vehicle and the bus station, and provided to the user.
In such a conventional bus positioning System, the arrival time at each bus stop is generally calculated using the travel time of a predetermined section calculated from the travel data of the bus (hereinafter referred to as "bus probe") or traffic Information provided from VICS (Vehicle Information and communication System) or the like. VICS is a registered trademark.
Since the bus probe can acquire only the traffic condition at the time of passage of the road section where the preceding bus has traveled, there is a case where the traffic condition is different from that when the preceding bus has passed when the succeeding bus travels the same road section. Even if congestion occurring in a road section other than a bus route affects congestion of the bus route in the future, it is difficult to capture such a phenomenon and predict the time-to-route of the subsequent bus, leaving a problem in the accuracy of predicting the time-to-route.
Traffic information such as VICS is mainly information targeted for general vehicles, and is mainly used as information for grasping a travel route (a route avoiding congestion, etc.) to a destination and a travel time in a car navigation system mounted in a general vehicle. The ordinary vehicle means a vehicle such as a car other than road public transportation. Unlike a general vehicle, road public transportation travels while repeatedly stopping at a predetermined bus stop provided on a travel route, and therefore, the time-to-route provided from the VICS or the like may not coincide with the actual travel state of the bus route.
For example, since the traffic information such as VICS, which is mainly provided for general vehicles, does not take into account the stop time in the bus stop unique to the bus on the route, it is expected that the time-to-route to the destination may be estimated to be shorter than the time-to-route actually traveled by the road public transportation. Therefore, if the arrival time of the predetermined bus stop is predicted using the general traffic information such as VICS, it may be predicted that the route bus will arrive at the bus stop at a time earlier than the operation schedule.
As a countermeasure, patent document 1 discloses the following technique: in a navigation system including a route bus as one of transportation means, when it is predicted that the departure arrival of the route bus at a bus stop is earlier than a schedule, the departure time of the bus is corrected by using the schedule of the bus stop, and the route time of the route bus traveling in a predetermined section is predicted.
Documents of the prior art
Patent document
Patent document 1: japanese patent laid-open publication No. 2011-185780
Disclosure of Invention
Problems to be solved by the invention
The technique described in patent document 1 is considered to be effective in the following cases: in particular, when there is no congestion, the predicted route time of the predicted route bus is often shorter than the operation schedule. However, since the correction in the case where the predicted road-to-time of the route bus becomes longer than the operation schedule due to the influence of traffic jam or the like is not performed, there is room for improvement in the prediction accuracy of the road-to-time in this case (the prediction accuracy of the arrival time and the delay time).
In addition, in mixed traffic where the route bus is mixed, deceleration at the time of arrival of an outgoing bus at a bus stop and congestion in the traffic flow hinder the travel of a general vehicle while the bus stop of the route bus is stopped. Therefore, in the hybrid traffic, the traffic flow temporarily stagnates particularly near the bus station, and the travel time may become longer than that in the case of only the ordinary vehicle. Therefore, when predicting the arrival time and delay time of road surface public transportation in the mixed transportation, it is preferable to consider the existence of road surface public transportation traveling in a road section where the mixed transportation is performed.
The present invention has been made in view of the above circumstances, and an object thereof is to predict the arrival time of road public transportation in which the actual traffic flow mixed with the road public transportation is reflected.
Means for solving the problems
An arrival time prediction device according to an aspect of the present invention includes: a probe data acquisition unit that acquires a probe speed that is a travel speed from probe data of a vehicle traveling in a road section to be measured; a map data storage unit that stores map data; a traffic characteristic storage unit that stores a traffic characteristic indicating a relationship between a travel speed and a traffic volume; a traffic amount estimation unit that estimates a traffic amount of the road section from a detection speed and traffic characteristics of the road section; a hybrid traffic speed estimation unit that estimates a travel speed of hybrid traffic in which the road surface public transportation is mixed in a normal vehicle other than the road surface public transportation, based on the estimated traffic volume and the traffic characteristics; and an arrival time prediction unit that predicts a road time of the road public transportation and an arrival time at a predetermined point in the road section based on the travel speed of the mixed transportation estimated by the mixed transportation speed estimation unit.
An arrival time prediction system according to an aspect of the present invention is configured by an in-vehicle device mounted on a vehicle and a server (corresponding to the arrival time prediction device) that receives probe data transmitted from the in-vehicle device via a network.
One aspect of the present invention is a method for realizing the functions of the arrival time prediction apparatus.
ADVANTAGEOUS EFFECTS OF INVENTION
According to the present invention, the arrival time and delay time of road surface public transportation at a predetermined point when hybrid transportation in which a normal vehicle and road surface public transportation are assumed to coexist is predicted using the detection speed acquired from the normal vehicle, instead of actually driving the road surface public transportation. Therefore, even on a road or a time zone on which the road public transportation is not actually traveling, the arrival time and the delay time of the road public transportation to which the actual traffic situation is added can be predicted.
Problems, configurations, and effects other than those described above will be apparent from the following description of the embodiments.
Drawings
Fig. 1 is a diagram showing the configuration of the entire system including an arrival time prediction apparatus according to an embodiment of the present invention.
Fig. 2 is a block diagram showing an example of the internal configuration of an arrival time prediction device according to an embodiment of the present invention.
Fig. 3 is a flowchart showing a process of estimating a hybrid traveling speed in a case where hybrid traffic in which a normal vehicle and road public transportation are assumed to coexist is estimated.
Fig. 4 is a diagram showing the 1 st traffic characteristic and the 2 nd traffic characteristic used when the mixed traffic speed of each road section is estimated from the detection speed of the normal vehicle.
Fig. 5 is a flowchart showing a process of generating traffic characteristics stored in the traffic characteristics storage unit.
Fig. 6 is a flowchart showing a process of predicting the time at which road public transportation traveling on a predetermined route reaches a predetermined point using the estimated mixed traffic speed.
Fig. 7 is a flowchart showing a process of calculating a delay time of road public transportation using an arrival prediction time at a predetermined point.
Fig. 8 shows an example of display output of the execution conditions and the execution results of the arrival time prediction and delay time prediction processing of road public transportation in the arrival time prediction device according to the embodiment of the present invention.
Fig. 9 is a flowchart showing a process of calculating an excess traffic volume of each road section constituting a predetermined route using a predicted delay time of road surface public transportation at a predetermined point on the route.
Fig. 10 is an example of display output of the excess traffic volume in each road section constituting the predetermined route in the arrival time prediction device according to the embodiment of the present invention.
Fig. 11 shows another example of display output of the estimated arrival time and the delay time of road surface public transportation.
Fig. 12 is another example of the display output of the excess traffic volume of each road section constituting the predetermined route.
Fig. 13 is a block diagram showing an internal configuration example of a terminal device connected to a communication network.
Detailed Description
An example of an embodiment of the present invention will be described below with reference to the drawings. In the drawings, components having substantially the same function or configuration are denoted by the same reference numerals, and redundant description thereof is omitted.
< 1. an embodiment
The arrival time prediction apparatus according to the present embodiment acquires a travel speed (probe speed) of each road section from travel data (probe data) collected from a plurality of general vehicles, and estimates a traffic volume of a predetermined road section from the probe speed. Next, the estimated traffic volume (hereinafter referred to as "estimated traffic volume") is used to estimate the hybrid traffic travel speed in each road section in the case of hybrid traffic in which the ordinary vehicle and the road public traffic are assumed to coexist. Then, the estimated value of the hybrid traffic travel speed is used to predict the arrival time and delay time to a predetermined point when the road public traffic travels on a predetermined route.
Further, the arrival time prediction device of the present embodiment calculates the excess traffic volume of each road section constituting a predetermined route using the predicted delay time of road surface public transportation traveling on the route.
The present embodiment will be described in detail below with reference to the drawings. In the following description, the term "vehicle" refers to a general vehicle such as a car except for (a vehicle of) road public transportation.
[ constitution of the entire System ]
Fig. 1 is a diagram showing the configuration of the entire system including an arrival time prediction apparatus according to an embodiment of the present invention.
The arrival time prediction system 10 shown in fig. 1 includes an arrival time prediction device 1 (an example of an arrival time prediction server) and an in-vehicle device 2. The arrival time prediction apparatus 1 is connected to a communication network 4, which is a wide area communication line, and is connected to an in-vehicle apparatus 2 mounted on a vehicle 5 (a normal vehicle) via a base station 3. The in-vehicle device 2 is a so-called navigation device. These devices mutually transmit and receive necessary information via the communication network 4.
(vehicle-mounted device)
The in-vehicle device 2 includes a GPS receiving unit 11, a control unit 12, a display unit 13, a communication unit 14, an operation unit 15, a storage unit 16, and the like. These parts are connected via a bus 17. The in-vehicle device 2 is a computer including a control unit 12 including a CPU (Central processing unit), a ROM (Read Only Memory), a RAM (Random access Memory), and the like. The display unit 13 is formed of an LCD (Liquid crystal display) or the like. The communication unit 14 is connected to the arrival time prediction apparatus 1 by data communication under wireless communication via the base station 3 and the communication network 4. The operation unit 15 uses operation keys, a touch panel, and the like, and the user can perform predetermined operation input and instruction using the operation unit 15. The storage unit 16 is a semiconductor device, a hard disk device, or the like.
The GPS receiving unit 11 (an example of a position information acquiring unit) receives radio waves from GPS satellites via an antenna. The radio waves received from the GPS satellites include latitude-longitude information (vehicle position information) and time information (passing time), and the control unit 12 detects the current position of the in-vehicle device 2 from the acquired information. The positional information and the time information of the vehicle 5 detected by the GPS receiving unit 11 are temporarily stored in the storage unit 16, and are transmitted to the arrival time prediction device 1 via the communication unit 14 at a predetermined time.
(arrival predicting apparatus)
As shown in fig. 1, the arrival time prediction device 1 includes a communication interface 101, a probe data acquisition unit 102, a traffic volume estimation unit 103, a hybrid traffic speed estimation unit 104, an arrival time prediction unit 105, a delay time prediction unit 106, a passing traffic volume calculation unit 107, and an input/output unit 108. The arrival time prediction device 1 includes a map data storage unit 110, a probe speed storage unit 111, a traffic characteristic storage unit 112, a mixed traffic speed storage unit 113, and a schedule storage unit 114. The arrival time prediction apparatus 1 is constituted by, for example, a general-purpose computer (information processing apparatus).
The communication interface unit 101 performs communication control between the arrival time prediction apparatus 1 and the communication network 4, and performs data transmission and reception with the in-vehicle apparatus 2 via the communication network 4.
The probe data acquisition unit 102 acquires probe data including a vehicle position (position information obtained by the GPS receiving unit 11, a passing date and time, a traveling direction, and the like) from the in-vehicle device 2 mounted on each of the plurality of vehicles via the base station 3 and the communication network 4. The probe data acquisition unit 102 reads road data (road map) corresponding to the position information of the acquired probe data from the map data storage unit 110, and specifies the position of the vehicle obtained from the probe data on the road map. Then, the probe data acquisition unit 102 generates a travel speed of the vehicle (hereinafter referred to as "probe speed") for each road section (for example, a road section connecting nodes on a road network representation such as an intersection in the map data) and stores the travel speed in the probe speed storage unit 111.
The traveling speed of the vehicle in the measurement target road section may be the traveling speed of 1 vehicle, or may be calculated from the traveling speeds of a plurality of vehicles (for example, an average value). Further, the control unit 12 of the in-vehicle device 2 of the vehicle 5 may calculate the probe speed of the own vehicle, and the probe data acquisition unit 102 may acquire the probe speed transmitted from the in-vehicle device 2.
The traffic volume estimation unit 103 calculates the traffic density of a predetermined road section based on the probe speed of the road section read from the probe speed storage unit 111 and the first traffic characteristic of the road section read from the traffic characteristic storage unit 112. The first traffic characteristic is a characteristic of a relationship between a traveling speed of a general vehicle and a traffic density.
The mixed traffic speed estimation unit 104 calculates a travel speed (mixed traffic speed) in mixed traffic of the road section from the traffic density of the road section calculated by the traffic amount estimation unit 103 and the second traffic characteristic of the road section read from the traffic characteristic storage unit 112. The second traffic characteristic is a characteristic of a relationship between a traveling speed and a traffic density in a mixed traffic in which a normal vehicle and a road public traffic are mixed. The calculated hybrid traffic speed is stored in the hybrid traffic speed storage unit 113. The first traffic characteristic and the second traffic characteristic will be described later with reference to fig. 4.
The arrival time prediction unit 105 calculates the time-in-transit of each road section from the travel speed of each road section calculated by the mixed traffic speed estimation unit 104 and the road length of each road section read from the map data storage unit 110. Then, the arrival time prediction unit 105 calculates the road time when the road public transportation travels in the predetermined route section and the arrival time of the road public transportation at the predetermined point set on the route.
The delay time prediction unit 106 calculates the delay time of road surface public transportation at a predetermined point of the route based on the difference between the predicted arrival time of road surface public transportation at the predetermined point calculated by the arrival time prediction unit 105 and the time table of the corresponding route. The delay time prediction unit 106 reads the time table of road surface public transportation on the corresponding route from the time table storage unit 114.
The excess traffic volume calculation unit 107 detects a route in which a delay time exceeding a predetermined value is generated, based on the calculation result in the delay time prediction unit 106. Then, the excess traffic volume calculation unit 107 calculates the excess traffic volume of each road section based on the difference between the traffic volume of each road section constituting the matched route and the possible traffic volume (critical traffic volume) obtained based on the second traffic characteristics stored in the traffic characteristic storage unit 112.
The input/output unit 108 receives execution conditions (a route, a start point O, an end point D, a departure time of the start point O, and the like) for executing the arrival time prediction processing and the delay time prediction processing from a user, an external system such as an operation planning system or a traffic control system, and the like. The input/output unit 108 displays and outputs the execution result (the estimated arrival time and the delay time) based on the received execution condition on the display unit 12 (fig. 2), or outputs the result to an external system such as an operation planning system or a traffic control system connected to the estimated arrival time device 1. Further, the following configuration is also possible: the operation planning system and the traffic control system are connected to the communication network 4 instead of the arrival time prediction apparatus 1, and the execution result is output to the operation planning system and the traffic control system via the communication network 4.
[ hardware configuration example of arrival time prediction device ]
Next, a hardware configuration of the arrival time prediction apparatus 1 will be described.
Fig. 2 is a block diagram showing an example of a hardware configuration of a computer used in the arrival time prediction apparatus 1.
The computer 20 includes a CPU 21a, a ROM21b, and a RAM 21c connected to a bus 26. The CPU 21a, the ROM21b, and the RAM 21c constitute a control unit 21. The computer 20 further includes a display unit 22, an operation unit 24, a storage unit 25, and a communication interface unit 101.
The CPU 21a reads out the program code of the software that realizes the functions of the present embodiment from the ROM21b and executes it. Variables, parameters, and the like generated during the arithmetic processing are temporarily written in the RAM 21 c. The display unit 22 displays the result of processing performed by the computer 20 and the like to the user using, for example, a liquid crystal display. The operation unit 24 is, for example, a keyboard, a mouse, or the like, and a user can perform predetermined operation input and instruction using the operation unit 24.
The storage unit 25 is, for example, an HDD (Hard disk drive), a flexible disk, an optical disk, a magneto-optical disk, a CD-ROM, a CD-R, a magnetic tape, a nonvolatile memory card, or the like. The storage unit 25 stores an OS (Operating System) and various parameters, and also stores a program for causing the computer 20 to realize the functions of the present embodiment. The communication Interface 101 can transmit and receive various data via any of a LAN, a dedicated line, and a wide area communication line, using, for example, an NIC (Network Interface Card).
The probe data acquisition unit 102, the traffic volume estimation unit 103, the mixed traffic speed estimation unit 104, the arrival time prediction unit 105, the delay time prediction unit 106, and the excess traffic volume calculation unit 107 of the arrival time prediction device 1 (fig. 1) are realized by the CPU 21a (control unit 21) executing a predetermined program stored in the storage unit 25. The map data storage unit 110, the probe speed storage unit 111, the traffic characteristic storage unit 112, the hybrid traffic speed storage unit 113, and the schedule storage unit 114 are configured by the storage unit 25.
[ hybrid traveling speed estimation processing ]
Next, a process of estimating a travel speed in a case where a mixed traffic in which a normal vehicle and a road public transportation are mixed is assumed will be described with reference to fig. 3 and 4.
Fig. 3 is a flowchart showing the processing of the traffic volume estimation unit 103 and the hybrid traffic speed estimation unit 104 in the arrival time prediction device 1.
The traffic volume estimation unit 103 and the mixed traffic speed estimation unit 104 read the detection speed of the normal vehicle from the detection speed storage unit 111, estimate the traffic volume of each road section, and estimate the travel speed (mixed travel speed) in the case of setting mixed traffic in which the normal vehicle and the road public traffic are mixed.
Fig. 4 is a supplementary view for explaining the flowchart of fig. 3. Fig. 4 shows an example of the first traffic characteristic and the second traffic characteristic used when the hybrid traffic speed of each road section is estimated from the detection speed of the normal vehicle. Next, each step of the flowchart of fig. 3 will be described in detail with reference to fig. 4.
In fig. 3, first, when the probe data acquisition unit 102 updates the probe speed storage unit 111, the traffic volume estimation unit 103 reads the updated time unit i and the probe speed V1(i, j) of the road section j from the probe speed storage unit 111 (step S301). The probe data is distinguished for each time unit and road section.
Next, the traffic volume estimation unit 103 determines the vehicle type to which the probe data, which is the source of the probe speed V1(i, j), is supplied (step S302). Then, if the vehicle type is a normal vehicle, the traffic amount estimation unit 103 reads the first traffic characteristic corresponding to the road section j from the traffic characteristic storage unit 112 (step S303). The first traffic characteristic used here is a characteristic showing a correlation between the speed of a general vehicle and the traffic density (the number of vehicles per unit distance), as shown by, for example, a 1 st curve 401 in fig. 4. The first traffic characteristics are assigned to each road section and stored in the traffic characteristics storage unit 112.
Further, a plurality of first traffic characteristics may be stored in the traffic characteristic storage unit 112 according to road information (lane, road width, etc.) of a road section. A certain first traffic characteristic stored in the traffic characteristic storage unit 112 is selected and read based on the road information of the calculation target road section j, and the like.
Next, the traffic volume estimation unit 103 calculates the traffic density k (i, j) from the detection speed V1(i, j) using the first traffic characteristics thus read (step S304).
Next, the hybrid traffic speed estimation unit 104 acquires the traffic density k (i, j) calculated by the traffic amount estimation unit 103, and reads the second traffic characteristic corresponding to the road section j from the traffic characteristic storage unit 112 (step S305).
The second traffic characteristic used here is a characteristic indicating a correlation between the speed and the traffic density of mixed traffic, for example, as shown by a curve 402 of fig. 4 No. 2, when ordinary vehicles and road surface public traffic are mixed in a predetermined ratio in the same lane. As with the first road characteristic, a plurality of types of second traffic characteristics may be stored in the traffic characteristic storage unit 112 for each road section or road information. Then, a certain second traffic characteristic stored in the traffic characteristic storage unit 112 is selected and read in accordance with the road information of the calculation target road section j, and the like. The hybrid traffic speed estimation unit 104 calculates the traveling speed V2(i, j) at the time of the traffic density K (i, j) using the read second traffic characteristics (step S306), and stores the calculated traveling speed V2(i, j) in the hybrid traffic speed storage unit 113 (step S307).
The traffic volume estimation unit 103 and the hybrid traffic speed estimation unit 104 execute steps S301 to S307 for all the probe speeds V1(i, j) of the target time i and road section j.
Here, the determination process of step S302 and steps S308 and S309 are processes when cooperation with a positioning system of road surface public transportation such as a bus detection system is assumed. That is, in the case where the determination result in step S302 is road surface public transportation, the probe speed V1(i, j) generated from the probe data of road surface public transportation corresponds to the traveling speed V2(i, j) at the time of hybrid transportation. Therefore, the hybrid traffic speed estimation unit 104 sets the probe speed V1(i, j) to the probe speed V2(i, j) of the hybrid traffic (step S308). For example, in the case where the arrival time prediction apparatus 1 cannot acquire sufficient probe data because the amount of traffic of a general vehicle is small, probe data obtained from road public transportation that is regularly running may be used.
The detection speed V2(i, j) at this time reflects the time when the road public transportation stops at the bus stop (parking point), and therefore, an identification flag is set for determination when the parking time is used in the following processing, and is stored in the hybrid traffic speed storage unit 113 (step S309).
[ traffic characteristic Generation processing ]
Next, a process of generating the first traffic characteristic and the second traffic characteristic used when estimating the mixed traffic speed of each road section will be described with reference to fig. 5.
Fig. 5 is a flowchart showing a process of generating traffic characteristics stored in the traffic characteristic storage unit 112 in the arrival time prediction device 1.
The arrival time prediction device 1 is premised on the fact that the first traffic characteristics and the second traffic characteristics necessary for the arrival time prediction processing are stored in the traffic characteristics storage unit 112. That is, the present embodiment does not include a function (traffic characteristic generation unit) for executing the flowchart in the basic configuration. The function of the traffic characteristic generation unit is realized by the control unit 21. The first traffic characteristics and the second traffic characteristics stored in the traffic characteristics storage unit 112 are generated in advance according to the flowchart of fig. 5.
In the present embodiment, a road section having a road characteristic (a characteristic based on road information such as a road type, the number of lanes, and a road width) similar to each road section or a measurement target road section is selected as the reference road section. Examples of the road category include national roads, prefectural roads, and general roads. Then, in the reference road section, the number of passing vehicles and the traveling speed are observed using a camera, a beacon device, or the like, and the number of passing vehicles and the representative speed are summed per unit time. In the present embodiment, the traffic characteristics of each road section are generated from the number of passing vehicles per unit time and the representative speed.
As described above, the first traffic characteristic and the second traffic characteristic may be generated for each road section and stored to the traffic characteristic storage 112. Alternatively, the first traffic characteristics and the second traffic characteristics may be generated in a plurality of reference road sections representing the respective conditions based on road information such as the road type, the number of lanes, and the width, and stored in the traffic characteristics storage unit 112. Next, each step of the flowchart of fig. 5 will be described in detail.
In fig. 5, first, the traffic characteristic generation unit sets a unit time that is a total unit of the observation data (step S501). Next, the traffic characteristic generation unit sums up the number k (x) of vehicles passing through each road section x (or the selected reference road section x) for each set time period (unit time) (step S502). The number of vehicles and the traveling speed observed here are the objects of road traffic on which ordinary vehicles travel.
Further, the traffic characteristic generation unit calculates a representative value v (x) of the traveling speed such as an average speed from the traveling speed of the general vehicle passing through the road section x (or the selected reference road section x) in the time period (step S503). The traffic characteristic generation unit executes the steps S501 to S503 for all the data observed in each road section x.
Then, the traffic characteristic generating unit generates a K-V curve, which is a relationship between traffic density and speed, as a first traffic characteristic of the road section x (or the selected road section x), from a data set of the number of vehicles K (x) and the traveling speed V (x) corresponding thereto (step S504).
Here, the traffic characteristic generation unit determines whether or not the observation data in the case where the ordinary vehicle and the road public transportation are mixed can be collected in each road section x (step S505). If the determination result is that the collection is possible (yes in step S505), the traffic characteristic generation unit observes the number of passing vehicles and the traveling speed of the traffic vehicle when the normal vehicle and the road surface public transportation are mixed, and executes the processing of steps S502 to S504 on the observation data to generate the second traffic characteristic (step 506). The plurality of second traffic characteristics may be generated based on a mixture rate of road public traffic with respect to the normal vehicle.
The traffic characteristic generation unit recognizes and classifies the first traffic characteristic and the second traffic characteristic of the generated road section x based on the road information such as the road type, the lane, and the width of the road section x, and stores the first traffic characteristic and the second traffic characteristic in the traffic characteristic storage unit 112 (step S509). When the process of step S509 is completed, the traffic characteristic generation unit ends the process of this flowchart.
On the other hand, in the determination process of step S505, when the observation data in the case where the normal vehicle and the road surface public transportation are mixed cannot be acquired (no in step S505), the traffic characteristic generation unit sets the mixed storage rate Pb of the road surface public transportation (step S507). Then, the traffic characteristic generating unit generates a second traffic characteristic using the mixed flow basic formula (step S508).
For example, as shown in literature (バスと) (される) (basic characteristics of a mixed traffic flow substantially based on traffic flow), a report set of society of civil engineering (22577), 316 (12 th 1981), pp.135-pp.143 (316 th), pp.135-pp.143) (basic characteristics of a mixed traffic flow composed of buses and cars, and a report set of society of civil engineering, 316 (12 th 1981), pp.135-pp.143), traffic characteristics of general vehicles (a relationship between a traveling speed V and a traffic density K in a constant traffic flow of a single vehicle type) are expressed by equation (1). in equation (1), Vc is a critical speed (a traveling speed at which the traffic volume is maximum), Kj is a saturation density (a maximum value of the traffic density). the basic equation of the mixed traffic flow is generated by giving a critical speed and a saturation density Kj when the traffic flow of public traffic on a road surface and is a critical speed when the road surface is present in equation (1), and the basic equation of the mixed traffic flow is determined by using a common traffic flow (Vc) as an observation coefficient, for example, and the road surface mixing ratio is expressed by equation (α) (the same as the observation data).
(number formula 1)
V＝Vcln(Kj/K)·······(1)
(number formula 2)
Kj＝1/((αPb+1)Ls)····(2)
The observation data including the number of passing vehicles and the traveling speed input to the traffic characteristic generation process shown in fig. 5 is acquired by on-road sensors, such as cameras and beacon devices, installed on the road, and probe data. Further, the travel data of each vehicle obtained from a traffic simulator that simulates a road environment may also be used as the observation data.
[ arrival time prediction processing ]
Next, a process of predicting the time at which road surface public transportation traveling on a predetermined route reaches a predetermined point will be described with reference to fig. 6.
Fig. 6 is a flowchart showing a process of the arrival time prediction unit 105 in the arrival time prediction apparatus 1.
The arrival time prediction unit 105 predicts the time at which the road public transportation traveling on the predetermined route reaches the predetermined point, using the traveling speed V2(i, j) at the time of mixed transportation in each road section calculated by the mixed transportation speed estimation unit 104. The steps in the flowchart will be described in detail below.
In fig. 6, as a premise, the calculation conditions, i.e., the route, the start point O, the end point D, and the departure time io of the start point O are set by a request input from the outside via the input/output unit 108 or the communication interface unit 101.
When the arrival time prediction processing is requested to be started, the arrival time prediction unit 105 reads the set departure time io of the route, the start point O, the end point D, and the start point O (step S601). Then, the arrival time prediction unit 105 calculates a route OD from the start point O to the end point D along the set route, and sets a road section j constituting the route OD (step S602).
Next, the arrival time prediction unit 105 reads the detection speed V2(i, j) when the road surface public transportation enters the road section j along the route OD from the hybrid traffic speed storage unit 113 (step S603). Here, the entry time i of the road section j is set by setting the departure time io as an initial value and accumulating the time-in-transit of the road section j calculated in the processing after step S604. Next, the arrival time prediction unit 105 reads the distance L of the road section j from the map data storage unit 110 (step S604). Then, the travel time Tm (i, j) of the road section j is calculated using the travel speed V2(i, j) and the distance L at the time of hybrid traffic estimated from the probe speed V2(i, j) of the normal vehicle (step S605).
Next, the arrival time prediction unit 105 refers to the route information stored in the map data storage unit 110, and determines whether or not there is a bus stop in the road section j (step S606).
In the determination processing in step S606, when a bus stop is present in the road section j (yes in step S606), the arrival time prediction unit 105 sets the parking time TS (i, j) of the bus stop based on the route information (step S607). The parking time Ts (i, j) is set as a standard value or is obtained by adding up the parking times per unit time based on the traveling data collected from the road public transportation. On the other hand, when there is no bus stop in the road section j (no in step S606), the arrival time prediction unit 105 sets the parking time Ts (i, j) of the road section j to 0.
Then, the arrival time prediction unit 105 calculates a travel time Tr from the start point O until the road section j passes through the route section according to equation (3) (step S608).
(number type 3)
Tr＝Σi,j(Tm(i,j)+Ts(i,j))····(3)
The arrival time prediction unit 105 executes the processing of steps S603 to S608 for all the road sections j constituting the route OD, and sets the travel time Tr obtained by accumulating the travel times of the road sections j from the start point O to the end point D as the travel time Tod of the route OD (step S609).
Next, the arrival time prediction unit 105 adds Tod to the departure time io and calculates the arrival time Tp of the end point D (step S610). Here, the route time of the road section in which the end point D exists is set by proportionally allocating the route time of the road section by using the distance from the connection point (entrance side) of the road section to the end point D.
Then, the arrival time prediction unit 105 outputs the arrival time Tp to the delay time prediction unit 106 and the input/output unit 108 (step S611), and the process of the present flowchart is terminated.
[ delay time prediction processing ]
Next, a process of predicting the delay time at a predetermined point using the arrival prediction time at the predetermined point will be described with reference to fig. 7.
Fig. 7 is a flowchart showing the processing of the delay time predicting unit 106 in the arrival time predicting apparatus 1.
The delay time prediction unit 106 calculates the delay time at the predetermined point using the predicted arrival time at the predetermined point calculated by the arrival time prediction unit 105. The steps in the flowchart will be described in detail below.
In fig. 7, first, delay time predicting unit 106 reads estimated arrival time Tp of end point D from estimated arrival time predicting unit 105 (step S701). Next, the delay time predicting unit 106 reads a time table (operation schedule table) of road surface public transportation corresponding to the arrival prediction time Tp from the time table storage unit 114 (step S702). Next, the delay time predicting unit 106 calculates the delay time dT based on the difference between the predicted arrival time Tp of the end point D and the departure arrival time of the end point D registered in the time chart (step S703). Then, the delay time predicting unit 106 outputs the delay time dT at the point D to the input/output unit 108 (step S704), and the process of the present flowchart is ended.
[ display examples of execution conditions and execution results (predicted arrival time and delay time) ]
Fig. 8 shows an example of display of execution conditions (such as the route, the start point O, the end point D, and the departure time from the start point O) set when the arrival time prediction apparatus 1 executes the processing by the arrival time prediction unit 105 and the delay time prediction unit 106, and execution results (the arrival prediction time and the delay time). Fig. 8 a shows a map display, fig. 8B shows an evaluation condition display, and fig. 8C shows an arrival prediction time display.
The evaluation condition display 802 is a display example of a start point O, an end point D, a departure date, a departure time, and a route, which indicate evaluation conditions set via the input/output unit 108 or the communication interface unit 101. The evaluation condition display 802 as shown in B of fig. 8 may be displayed on the display unit 22 so that the user can set the evaluation condition while confirming the content of the evaluation condition display 802.
The map display 801 is an example in which a peripheral map associated with set evaluation conditions is read from the map data storage unit 110, and evaluation conditions such as a route are displayed on the map in a superimposed manner. The point a and the point B are position information of a bus station existing on the route OD, and are stored in the map data storage unit 110, the schedule storage unit 114, or the like as route information. The user can set evaluation conditions such as a start point O, an end point D, and a route while checking the map display 801 displayed on the display unit 22.
The predicted arrival time display 803 is an example of display output of the predicted arrival time at each point calculated by the predicted arrival time prediction unit 105 based on the evaluation conditions of the evaluation condition display 802. The estimated arrival time display 803 of C in fig. 8 also displays the delay time at each point calculated by the delay time predicting unit 106. Fig. 8C shows an example of display of a place, a schedule (scheduled arrival time), an arrival predicted time, and a delay time (minute).
In each of the flowcharts (fig. 6 and 7) of the arrival time prediction unit 105 and the delay time prediction unit 106, the arrival prediction time and the delay time of the end point D, which is the end point, are output. However, as shown in C of fig. 8, the predicted arrival time and delay time at the point A, B (bus station A, B) that is the halfway point of the route OD may be included in the information of the destination D and output.
[ excess traffic volume calculation processing ]
Next, a process of calculating the excess traffic volume of the predetermined road section will be described with reference to fig. 9.
Fig. 9 is a flowchart showing a process of calculating the excess traffic volume by the excess traffic volume calculating unit 107 of the arrival time prediction apparatus 1.
When the delay time prediction unit 106 calculates a delay of a predetermined value or more on the route OD, the excess traffic volume calculation unit 107 calculates an excess traffic volume in a road section constituting the route OD. The steps in the flowchart will be described in detail below.
In fig. 9, first, the excess traffic volume calculation unit 107 reads the delay time dT at an arbitrary point (here, the end point D) on the route OD from the delay time prediction unit 106 (step S901).
Next, the excess traffic volume calculation unit 107 determines whether or not the delay time dT is equal to or greater than a predetermined value (step S902). Here, when the delay time dT is less than the predetermined value (within the allowable value) (no in step S902), the excess traffic volume calculation process is ended.
On the other hand, when the delay time dT is equal to or longer than the predetermined value (yes in step S902), the excess traffic amount calculation unit 107 sets a road section j constituting the route OD (step S903).
Next, the excess traffic volume calculation unit 107 reads the travel speed V2(i, j) (here, V2) of the mixed traffic in the road section j from the mixed traffic speed storage unit 113 along the route OD (step S904).
Next, the excess traffic volume calculation unit 107 reads the second traffic characteristic associated with the road section j from the traffic characteristic storage unit 112, and calculates the traffic volume qp (j) (═ V2 × K) for the road section based on the traffic density K corresponding to the traveling speed V2 (step S905).
Next, the excess traffic volume calculation unit 107 calculates a critical traffic volume qc (j) (═ Vc × Kc) from the critical speed Vc and the critical density Kc of the second traffic characteristic (step S906). Then, the excess traffic volume calculation unit 107 calculates the excess traffic volume qe (j) (═ qp (j)) to-qc (j)) of the road section j (step S907).
Then, the excess traffic volume calculation unit 107 executes the processing of steps S904 to S907 in all the road sections j set in step S903, and calculates the excess traffic volume qe (j) of each road section j constituting the route OD.
Then, the excess traffic volume calculation unit 107 outputs the excess traffic volume qe (j) of each road section j constituting the route OD to the input/output unit 108 (step S908), and the process of the present flowchart is ended.
[ display example of excess traffic volume ]
Fig. 10 shows an example of data output by the input/output unit 108 of the excess traffic volume calculated by the excess traffic volume calculating unit 107 of the arrival time predicting apparatus 1 according to the present embodiment. Fig. 10 a shows a route display, and fig. 10B shows an excess traffic display.
The excess traffic display 1005 (B of fig. 10) shows, for each road segment ID constituting the route OD (a of fig. 10), the passing predetermined time (which may also be the entry time of the road segment), the critical traffic Qc of the road segment, the predicted traffic Qp, the excess traffic Qe, and the bus stops existing on the road segment. A road section 1001 indicated by a thick line is a road section in which a traffic volume is generated. The points 1002 and 1003 are points (the bus station B, D) where a delay of a predetermined time or more is predicted, and the road section 1001 including such points where a serious delay is expected is displayed in a form different from a road section including points where no delay occurs or points where a delay is within an allowable value.
As shown in fig. 10, when the delay time of the travel route of the road surface public transportation exceeds a predetermined time, the excess traffic volume of each road section constituting the route is outputted. This makes it possible to take measures such as traffic control (signal control or the like) for predicting a time zone in which the amount of traffic exceeds the traffic volume, and review of the operation plan of the road public transportation. Thus, a serious delay of the road public transportation is eliminated, so that convenience of the road public transportation is improved.
As described above, in the present embodiment, the arrival time and the delay time of road surface public transportation at a predetermined point when hybrid transportation in which a normal vehicle and road surface public transportation are assumed to coexist is predicted using the detection speed acquired from the normal vehicle, instead of actually traveling the road surface public transportation. Therefore, even on a road or a time zone where the road public transportation is not actually traveling, the arrival time, the delay time, and the excess traffic volume of the road public transportation to which the actual traffic situation is added can be predicted.
Further, in the present embodiment, the travel speed of the road public transportation at the time of the hybrid transportation is estimated from the detection speed of the normal vehicle using the traffic characteristics (the first traffic characteristic and the second traffic characteristic) indicating the correlation between the travel speed and the passing traffic volume. Therefore, it is possible to predict the presence of road public transportation in a wide range of traffic conditions from non-congestion to congestion.
< 2. Another embodiment >
[ Another explicit example of execution conditions and execution results ]
Fig. 11 shows another example of display output of the arrival prediction time and the delay time of the road public transportation.
The map display 1101 of fig. 11 is a map display in which an annotation frame is displayed at each point on the route OD of the map display 801 of fig. 8 a, and the predicted arrival time and delay time of C of fig. 8 are displayed in the annotation frame.
By adopting a display mode such as the map display 1101, the user can visually recognize the estimated arrival time and the delay time at each point and intuitively grasp these pieces of information. Further, the user may select a point where the predicted arrival time and the delay time are displayed in the comment frame using the operation unit 24.
Fig. 12 shows another example of the display output of the excess traffic volume of each road section constituting the predetermined route.
The route display 1201 of fig. 12 is a display in which an annotation frame is displayed on each road segment on the route OD of the route display 1004 of fig. 10 a, and the value exceeding the traffic volume of B of fig. 10 is displayed in the annotation frame.
By adopting a display mode such as the route display 1201, the user can visually recognize the excess traffic volume at each point and intuitively grasp the information. Further, the user may select a point where the comment frame is displayed for the excess traffic volume through the operation unit 24.
< 3. other >)
Fig. 13 shows an internal configuration example of a terminal device connected to a communication network.
The display contents shown in fig. 8 and 10 to 12 may be displayed on the terminal device 1300 connected to the communication network 4. For example, a personal computer, a smart phone, a tablet type terminal, a mobile phone terminal, a wearable terminal, or the like can be used as the terminal device 1300.
The terminal device 1300 includes a CPU1311, a ROM 1312, and a RAM 1313, which are connected to a bus 1360. The CPU1311, ROM 1312, and RAM 1313 constitute a control unit 1310. Further, the terminal device 1300 includes a display unit 1320, an operation unit 1340, a storage unit 1350, and a communication interface unit 1330. Each part of terminal apparatus 1300 corresponds to each part of computer 20 (fig. 2), and has the same function as each part of computer 20.
In this way, the terminal device 1300 displays the information of the estimated arrival time of road surface public transportation, the delay time, and the amount of traffic exceeding the traffic volume on the display unit 1320, and displays the information to the user, so that the user can grasp the information.
The information on the predicted arrival time, delay time, and excess traffic volume of the road public transportation may be displayed on the display unit 13 of the in-vehicle device 2. For example, by being displayed on the in-vehicle device 2 at a station that is heading for a bus stop or a station having a bus stop, the driver and the passenger of the vehicle 5 can confirm the predicted arrival time and the delay time of the road public transportation. Further, by exceeding the traffic volume, it is possible to grasp the congestion of the vehicle traveling on the road section up to the bus stop or the road intersecting the road section.
The first and/or second traffic characteristics stored in the traffic characteristic storage unit 112 are generated in plural according to the difference in the characteristics such as the mixed traffic ratio Pb of road surface public transportation and road information obtained from map data. The road information includes, in addition to the above-described road type, number of lanes, and width, road shape, road gradient, road junction, and temporary stop point. The hybrid traffic speed estimation unit 104 selects the traffic characteristics in the road information of the prediction target road section j or the traffic characteristics of the reference road section similar to the road information as the first and/or second traffic characteristics of the road section j.
The first and/or second traffic characteristics stored in the traffic characteristic storage unit 112 may be generated in plural numbers according to the characteristics of the running environment such as weather, temperature, and road surface state. The hybrid traffic speed estimation unit 104 selects a traffic characteristic in a traveling environment (at the time of scheduled traveling) of the prediction target road section j or a traffic characteristic in a traveling environment similar to the traveling environment as the first and/or second traffic characteristics of the road section j.
Furthermore, the present invention is not limited to the above embodiments, and it is needless to say that various other application examples and modifications can be adopted without departing from the gist of the present invention described in the claims.
For example, the above-described embodiments have been described in detail and specifically with respect to the configuration of the apparatus and the system in order to explain the present invention in a manner that is easy to understand, but the present invention is not necessarily limited to all the configurations described. Note that a part of the configuration of one embodiment may be replaced with the configuration of another embodiment. In addition, the configuration of one embodiment example may be added to the configuration of another embodiment example. Further, addition, deletion, and replacement of another configuration may be performed on a part of the configuration of each embodiment.
Further, the above-described configurations, functions, processing units, processing methods, and the like may be implemented in part or all of hardware by designing on an integrated circuit, for example. Each of the above-described configurations, functions, and the like may be realized by software by a processor interpreting and executing a program for realizing each function. Information such as programs, tables, and files for realizing the respective functions can be stored in a recording device such as a memory, a hard disk, or an SSD (Solid State Drive), or a recording medium such as an IC card, an SD card, or a DVD.
The control lines and information lines are shown as those necessary for the description, and not necessarily all the control lines and information lines are shown in the product. In practice, it is also contemplated that substantially all of the components may be interconnected.
Description of the symbols
1 arrival time prediction device
10 arrival time prediction system
11 GPS receiving part
12 control part
13 display part
14 communication unit
20 computer
21 control part
21a CPU
21b ROM
21c RAM
22 display part
25 storage unit
101 communication interface unit
102 probe data acquisition unit
103 traffic volume estimating unit
104 hybrid traffic speed estimating unit
105 arrival time prediction unit
106 delay time prediction unit
107 excess traffic volume calculating unit
108 input/output part
110 map data storage unit
111 detection speed storage unit
112 traffic characteristics storage unit
113 hybrid traffic speed storage unit
114 time table storage unit
401 st curve 1
402 curve 2
801 map display
802 evaluation condition display
803 predicted arrival time display
1001 road section
1002. 1003 location
1004 route display
1005 excess traffic display
1101 map display
1201 path display
1300 terminal device.
Claims (8)
1. An arrival time prediction device comprising:
a probe data acquisition unit that acquires a probe speed that is a travel speed from probe data of a vehicle traveling in a road section to be measured;
a map data storage unit that stores map data;
a traffic characteristic storage unit that stores a first traffic characteristic represented by a correlation characteristic of a travel speed and a traffic density in a flow of a normal vehicle other than a road surface public transport and a second traffic characteristic represented by a correlation characteristic of a travel speed and a traffic density in a flow of the road surface public transport mixed in a predetermined ratio in the normal vehicle;
a traffic amount estimation unit that estimates a traffic density of the road section from a detection speed of the normal vehicle traveling in the road section using the first traffic characteristic;
a hybrid traffic speed estimation unit that estimates a travel speed of hybrid traffic in a case where hybrid traffic in which the road public transportation is assumed to coexist in the ordinary vehicle, from the estimated traffic density and the second traffic characteristic; and
and an arrival time prediction unit that predicts a road time of the road public transportation and an arrival time at a predetermined point in the road section based on the travel speed of the mixed transportation estimated by the mixed transportation speed estimation unit.
2. The arrival time prediction apparatus according to claim 1,
the arrival time prediction unit reads, from the map data storage unit, the distance of the road section for which the travel speed of the mixed traffic is estimated as the travel speed of the road public transportation by the mixed traffic speed estimation unit, calculates the time-to-transit of the road section, specifies each road section constituting a route from the start point to the end point on the predetermined route according to the specified execution condition, and calculates the arrival time of the predetermined point on the route according to the time-to-transit of each road section.
3. The arrival time prediction apparatus according to claim 2,
the arrival time prediction unit sets a stay time when a parking spot of the road public transportation is present in the road section, and adds the stay time to the road time.
4. The arrival time prediction apparatus according to claim 1,
the road traffic control system further comprises a delay time prediction unit for calculating a time difference between the predicted arrival time at a predetermined point and the time table of road public traffic read from the time table storage unit, thereby calculating the delay time at the point,
the delay time prediction unit calculates a delay time based on the specified execution condition and outputs the result to the input/output unit.
5. The arrival time prediction apparatus according to claim 4,
the vehicle control device further includes an excess traffic volume calculation unit that detects a delay time exceeding a predetermined value at a predetermined point of a predetermined route set according to a specified execution condition, reads the second traffic characteristics associated with road segments constituting the route from a traffic characteristic storage unit, and calculates an excess traffic volume from a traffic density and a critical traffic volume at a travel speed of each road segment.
6. The arrival time prediction apparatus according to claim 1,
the detection data acquisition unit determines a vehicle type of the detection data, and the mixed traffic speed estimation unit sets a detection speed when the vehicle type of the vehicle is the road surface public traffic as a mixed traffic running speed in the road section.
7. An arrival time prediction system comprising an in-vehicle device mounted on a vehicle and a server that receives probe data transmitted from the in-vehicle device via a network, the arrival time prediction system being characterized in that,
the vehicle-mounted device is provided with:
a position information acquisition unit that acquires information of a vehicle position of the vehicle; and
a communication unit that outputs the probe data including the vehicle position and the passing time to the network;
the server has:
a probe data acquisition unit that receives the probe data from the vehicle and acquires a probe speed that is a travel speed of a road section from the probe data;
a map data storage unit that stores map data;
a traffic characteristic storage unit that stores a first traffic characteristic represented by a correlation characteristic of a travel speed and a traffic density in a flow of a normal vehicle other than a road surface public transport and a second traffic characteristic represented by a correlation characteristic of a travel speed and a traffic density in a flow of the road surface public transport mixed in a predetermined ratio in the normal vehicle;
a traffic amount estimation unit that estimates a traffic density of the road section from a detection speed of the normal vehicle traveling in the road section using the first traffic characteristic;
a hybrid traffic speed estimation unit that estimates a travel speed of hybrid traffic in a case where hybrid traffic in which the road public transportation is assumed to coexist in the ordinary vehicle, from the estimated traffic density and the second traffic characteristic; and
and an arrival time prediction unit that predicts a road time of the road public transportation and an arrival time at a predetermined point in the road section based on the travel speed of the mixed transportation estimated by the mixed transportation speed estimation unit.
8. An arrival time prediction method, comprising the steps of:
acquiring a probe speed, which is a traveling speed, from probe data of a vehicle traveling in a road section to be measured;
estimating the traffic density of the road section from the detected speed of the normal vehicle traveling in the road section using a first traffic characteristic represented by a characteristic relating a traveling speed to the traffic density in a traffic flow of a normal vehicle other than the road surface public transportation;
a step of inferring a travel speed of a mixed traffic in which the road surface public traffic is assumed to be mixed in the ordinary vehicle, from the inferred traffic density and a second traffic characteristic represented by a correlation characteristic of the travel speed with the traffic density under a traffic flow in which the road surface public traffic is mixed in a prescribed ratio in the ordinary vehicle; and
and predicting a road time of the road public transportation and an arrival time at a predetermined point in the road section based on the estimated travel speed of the mixed traffic.
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|---|---|---|---|
| PCT/JP2015/062862 WO2016174745A1 (en) | 2015-04-28 | 2015-04-28 | Arrival time prediction device, arrival time prediction system, and program |
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| CN108288096B (en) * | 2017-01-10 | 2020-08-21 | 北京嘀嘀无限科技发展有限公司 | Method and device for estimating travel time and training model |
| JP6874826B2 (en) * | 2017-03-15 | 2021-05-19 | 住友電気工業株式会社 | Computer program, vehicle speed estimation device and estimation method |
| CN108765985B (en) * | 2018-06-13 | 2020-12-25 | 重庆交通大学 | Signalized intersection entrance lane delay calculation method based on arrival of first vehicle |
| JP7245084B2 (en) * | 2019-03-15 | 2023-03-23 | 日立Astemo株式会社 | Autonomous driving system |
| JP7372058B2 (en) * | 2019-06-24 | 2023-10-31 | 株式会社日立製作所 | Traffic flow prediction support device, traffic flow prediction support method, and traffic flow prediction support program |
| CN110491158B (en) * | 2019-09-25 | 2022-04-19 | 西安安邦鼎立智能科技有限公司 | Bus arrival time prediction method and system based on multi-metadata fusion |
| CN111402577A (en) * | 2020-02-27 | 2020-07-10 | 北京交通大学 | Method for predicting parking time of driver at signalized intersection |
| CN111400826B (en) * | 2020-06-04 | 2020-09-18 | 中国空气动力研究与发展中心低速空气动力研究所 | Method and system for predicting ice shape at any moment |
| JP7238853B2 (en) * | 2020-06-11 | 2023-03-14 | トヨタ自動車株式会社 | Map generation data collection device and map generation data collection method |
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| CN104243900A (en) * | 2013-06-21 | 2014-12-24 | 中兴通讯股份有限公司 | Vehicle arrival time prediction system and method |
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| JP2002024989A (en) * | 2000-07-04 | 2002-01-25 | Mitsubishi Heavy Ind Ltd | System and device for controlling signal, and system, device and method for providing traffic information |
| JP3849590B2 (en) * | 2002-06-21 | 2006-11-22 | 株式会社日立製作所 | Traffic information system |
| JP2006215671A (en) * | 2005-02-02 | 2006-08-17 | Hitachi Systems & Services Ltd | Time display system of route bus |
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| JP4329711B2 (en) * | 2005-03-09 | 2009-09-09 | 株式会社日立製作所 | Traffic information system |
| KR100804800B1 (en) * | 2006-11-03 | 2008-02-20 | 에스케이에너지 주식회사 | A system and method for providing traffic information |
| JP4957639B2 (en) * | 2008-04-25 | 2012-06-20 | 株式会社デンソー | In-vehicle device and center device |
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| CN101266149A (en) * | 2007-03-14 | 2008-09-17 | 爱信艾达株式会社 | Route-selection-supporting device and method |
| CN104243900A (en) * | 2013-06-21 | 2014-12-24 | 中兴通讯股份有限公司 | Vehicle arrival time prediction system and method |
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