JP7425680B2 - Navigation device and navigation method - Google Patents

Description

The present invention relates to a navigation device and a navigation method.

In response to a request for a route search from a departure point to a destination, including a railway route, from a terminal device used by a passenger who wants to go to his or her destination using a designated means of transportation, the request is made from a station near the destination. 2. Description of the Related Art A navigation device is known that searches for and outputs a route to a destination using a taxi, taking taxi waiting time into consideration (see Patent Document 1 and Patent Document 2).

In Patent Document 1, a probe information collection device is used to acquire probe information from a route guidance device used by a user, and by analyzing the acquired probe information, the taxi waiting time at each station is calculated and reflected in the route search result. A technique for doing so has been disclosed.

In addition, in Patent Document 2, the turnover rate of taxis at the taxi stand is calculated by detecting taxis departing from the taxi stand with a sensor and transmitting the detected information to the center. A technology has been disclosed for ascertaining the number of users at a taxi stand by detecting the number of users at a taxi stand and managing the transition of users entering and exiting the taxi stand, and estimating the taxi waiting time based on this information.

JP2017-090212A Japanese Patent Application Publication No. 2003-323694

The techniques disclosed in Patent Document 1 and Patent Document 2 search for a route to a destination in consideration of taxi waiting time. However, the situation when searching for a route may be unique, for example, when train operations are disrupted due to an accident.

In this regard, Patent Document 1 and Patent Document 2 propose a technology that estimates taxi waiting time based on normal conditions that can utilize past historical data and current conditions detected by sensors. be. Therefore, in unusual situations that cannot be called normal times, it is difficult to search for a taxi route that will arrive at the destination quickly while taking into consideration an appropriate taxi waiting time.

According to the inventors' study, the reason why taxi waiting times are longer when train operations are disrupted than in normal times is because the train operations are disrupted, and the number of passengers who make up the taxi queue at the station increases. The timing of when a certain percentage of railway users get off the train and exit the station creates a temporal imbalance, which temporarily disrupts the balance between the amount of taxis supplied and the number of taxi users. caused by.

For example, assume that the average taxi ride time for a taxi user at a station where one taxi is waiting is 10 minutes, and the time it takes for a taxi to make one round trip is 20 minutes. In this case, if taxi users arrive at the taxi stand one at a time evenly at 20 minute intervals, they can board a taxi with almost no waiting time. However, if three taxi users arrive at the taxi stand at the same time after no taxi users appear for a while due to a disruption in train service, the last taxi user in the taxi queue will You will have to wait for a taxi for 40 minutes as the taxi makes two round trips.

In such unique situations, such as when train operations are disrupted, taxi users arrive at the taxi stand all at once, resulting in a shortage of taxis and longer waiting times than in normal times. Depending on the route search system, it may not be possible to search for a route that indicates an appropriate travel time.

The present invention has been made in view of the current situation, and its purpose is to make it possible to properly consider the waiting time for road vehicles in route searches using railways and road vehicles. An object of the present invention is to provide a navigation device and a navigation method.

One of the present inventions for solving the above-mentioned problems is to provide a processor and a memory, and to operate a train for a passenger traveling from a departure point to a destination under conditions where the railway timetable on the travel route of the passenger is more disrupted than usual. As a travel route, a route identification process is performed to identify a route for getting off at a railway station and traveling by taxi from a boarding place near the station where you get off to the destination, and a process for determining a route by taxi from arriving at the boarding place The waiting time of the passenger at the boarding location until the start of the journey is calculated based on the predicted value of the passenger flow at the alighting station under the conditions and the statistical value based on the past number of passengers, and the passenger flow at the boarding location. Waiting time calculation processing for estimating based on statistical values based on past passenger numbers , and route travel time calculation processing for calculating travel time on the route from the departure point to the destination including the estimated waiting time. , a navigation device that executes a route search process including a result output process of outputting information on the calculated travel time.

Another aspect of the present invention for solving the above problem is that an information processing device including a processor and a memory is provided with a train schedule that is more irregular than usual on the travel route of a passenger traveling from a departure point to a destination. route identification processing for identifying a route for the passenger to alight from a railway station and travel by taxi from a boarding location near the station at which the passenger gets off the train to the destination as the travel route of the passenger under the conditions; The waiting time of the passenger at the boarding location from arrival at the station to the start of travel by the taxi is calculated based on a predicted value of the passenger flow at the alighting station under the conditions and a statistical value based on the past number of passengers. Waiting time calculation processing that estimates based on statistical values based on the past number of passengers in the flow of passengers at the boarding location , and calculation of the travel time from the departure point to the destination along the route, including the estimated waiting time. and a result output process of outputting information on the calculated travel time.

According to the present invention, route searches using railways and road vehicles can be performed while appropriately considering the waiting time for road vehicles.

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

1 is a diagram showing an example of the configuration of a navigation system according to the present embodiment. FIG. 1 is a diagram showing the configuration of a railway line assumed in this embodiment. FIG. 3 is a diagram showing an example of taxi arrival time distribution data. It is a figure which shows an example of passenger alighting time distribution data. FIG. 3 is a diagram showing an example of standard passenger arrival time distribution data. It is a figure which shows an example of standard passenger alighting time distribution data. It is a flow diagram explaining route search processing. FIG. 2 is a flow diagram illustrating details of a taxi waiting time calculation process. FIG. 3 is a diagram showing an example of generated passenger arrival time distribution data. It is a figure which shows an example of a route search result screen.

--System configuration--
FIG. 1 is a diagram showing an example of the configuration of a navigation system 1 according to the present embodiment. The navigation system 1 is an information processing system that allows a user who uses a transportation system including railways and road vehicles (in this case, taxis) to search for a route from a predetermined departure point to a destination.

The navigation system 1 includes a navigation device 10, a railway operation management system 400, one or more taxi operation management systems 300 (300A, 300B), and a mobile terminal 200.

The navigation device 10 and each of the mobile terminal 200, taxi operation management system 300A, taxi operation management system 300B, and railway operation management system 400 are connected via a LAN (Local Area Network), WAN (Wide Area Network), the Internet, etc. are communicatively connected by a wired or wireless communication network 500 .

-Mobile terminal-
The mobile terminal 200 is an information processing device such as a mobile phone, a smartphone, a PND (Personal Navigation Device), or a PDA (Personal Digital Assistant).

That is, the mobile terminal 200 is a general computer that includes a processor, a memory, an input device (such as a keyboard or touch panel), an output device (such as a monitor or display), and a communication device (such as a network interface).

The mobile terminal 200 is used by a user who searches for a route from a departure point to a destination. The mobile terminal 200 transmits route search request information, which is information including the departure point and destination input by the user, to the navigation device 10. Thereafter, the mobile terminal 200 receives information on one or more search routes corresponding to the route search request information, and displays the received information on each search route on the screen.

Here, FIG. 2 is a diagram showing the configuration of a railway line assumed in this embodiment. In this embodiment, it is assumed that A station 11, B station 12, and C station 13 exist on railway line c, and A station 11 and D station 14 exist on railway line d. Further, it is assumed that there are B station 12, C station 13, and D station 14 as stations near the user's destination 20. Further, it is assumed that the departure point is A station 11.

When a user moves from A station 11 to destination 20, the route from A station 11 to destination 20 includes (1) moving from A station 11 to B station 12 on railway line c, and then B station 12; (2) Travel from station A 11 to station C 13 on railway line c, then get off at station C 13 and take a taxi to destination 20 from the taxi stand at station B. Route to travel by taxi from the taxi stand to destination 20. (3) After traveling from A station 11 to D station 14 on railway line d, get off at D station 14 and travel from D station 14 to destination 20 by taxi. There is a route to do so.

In the present embodiment, the user of the mobile terminal 200 travels from station A 11, which is the departure point, to destination 20, under the unique condition that the train schedules for railway lines c and d are disrupted due to an accident or the like. Let us search for a route and its travel time.

Next, details of the configuration of each device will be explained.

-Navigation device-
As shown in FIG. 1, the navigation device 10 includes a CPU (Central Processing Unit)
an arithmetic unit 101 that is a processor such as a computer, a main storage device 102 such as a RAM (Random Access Memory) or a ROM (Read Only Memory), and a storage device 106 such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive). , a mobile terminal 200, a taxi operation management system 300, and a communication device 105 such as a network interface card that communicates with the railway operation management system 400.

The navigation device 10 also includes a map database 103 and a route database 104.

The route database 104 includes transportation network information, which is information on the configuration of each transportation system, such as road networks and public transportation route networks (in this case, railway networks), operation information such as public transportation timetables, and information on each transportation system. This is a database that stores information on transfers between transportation facilities.

The transportation network information includes information on nodes related to road transportation (intersections, buildings, parking lot entrances, etc.), information on nodes related to rail transportation (stations, etc.), and information on links representing connections between these nodes. include.

Node information related to road traffic includes information that identifies intersections, etc. that correspond to the node, information that identifies links that enter the node, information that identifies links that exit from the node, and other information. Information regarding a railway station located close to a node (hereinafter referred to as a nearby station; for example, a railway station located within a predetermined distance from an intersection, etc. corresponding to the node) is included. Specifically, for example, it includes a list of nodes of each nearby station, and information on taxi travel time from each nearby station in the list to an intersection corresponding to the node.

The map database 103 is a database that associates nodes in the route database 104 with positions on the map. For example, the map database 103 is used to associate the latitude and longitude, address, name, telephone number, etc. specified by the user as a destination or departure point, a position on the map, and each node in the route database 104. remembers information about.

Next, the navigation device 10 stores a route search program 161 and a taxi waiting time calculation program 162 in the storage device 106 or the like.

The route search program 161 is a program that is called when the navigation device 10 receives route search request information from the mobile terminal 200. A function of transmitting information on a plurality of search routes to the mobile terminal 200 is realized.

At this time, the navigation device 10 executes the route search process using the information received from the taxi operation management system 300 and the railway operation management system 400.

The taxi waiting time calculation program 162 is a program that is called while the route search program 161 is being executed. The taxi waiting time calculation program 162 calculates, for each station on a railway line, the waiting time at the taxi stand of the user (hereinafter referred to as taxi waiting time) from the time the user arrives at the taxi stand at that station until the start of travel by taxi. and a function that calculates based on the flow of people (movement of passengers) at the taxi stand.

Note that each of the above programs is executed, for example, by the arithmetic device 101 reading each program and each data stored in the storage device 106 to the main storage device 102.

Next, the navigation device 10 stores searched route data 163, taxi arrival time distribution data 164, passenger arrival time distribution data 165, passenger alighting time distribution data 166, standard passenger arrival time distribution data 167, standard passenger alighting time distribution data 168. , and the passenger behavior model 169 are stored in the storage device 106 or the like. The details of these data will be explained below.

(Searched route data)
The searched route data 163 is data generated when the route search program 161 is executed. The searched route data 163 includes, for example, information on the searched route.

If the route includes travel by train, the route information includes, for example, (1) information on which train, on which route, in what order, and in what order from the departure station (departure point) specified by the user; It includes a series of information such as (2) how many minutes to wait at a taxi stand at a nearby station before boarding a taxi, and (3) what time to arrive at the destination. Note that each train to be used is specified, for example, by the departure time of the boarding station and the arrival time of the alighting station, and, if necessary, also by using information on the type of train, such as rapid train or local train.

(Taxi arrival time distribution data)
FIG. 3 is a diagram showing an example of the taxi arrival time distribution data 164. Taxi arrival time distribution data 164 is data representing temporal fluctuations in the number of taxis arriving at the taxi stand at each station.

The taxi arrival time distribution data 164 is updated based on information received from the taxi operation management system 300A and the taxi operation management system 300B at a predetermined timing (e.g., at a predetermined time, at a predetermined time interval (e.g., every 5 minutes)). be done.

Specifically, the taxi arrival time distribution data 164 is data that stores information including a predetermined time period 31 and the number of taxis arriving at each station during this time period 31 (number of arrivals 32). Taxi arrival time distribution data 164 is provided for each station. In addition, in the figure, the time slot 31 for arriving at each station is set every 10 minutes.

(Passenger arrival time distribution data)
Passenger arrival time distribution data 165 is data representing temporal fluctuations in the number of passengers arriving at the taxi stand at each station. The passenger arrival time distribution data 165 is generated by the taxi waiting time calculation program 162.

Specifically, the passenger arrival time distribution data 165 is created by performing a predetermined correction on the standard passenger arrival time distribution data 167, which will be described later, based on the passenger alighting time distribution data 166 and the standard passenger alighting time distribution data 168. This is the data that will be used. Details of the passenger arrival time distribution data 165 will be described later.

(Passenger alighting time distribution data)
FIG. 4 is a diagram showing an example of the passenger alighting time distribution data 166. The passenger alighting time distribution data 166 is data representing temporal fluctuations in the number of passengers alighting from the train at each station and departing from the same station.

The passenger alighting time distribution data 166 is updated based on information received from the railway operation management system 400 at a predetermined timing (for example, at a predetermined time, at a predetermined time interval (for example, every 5 minutes)).

Specifically, the passenger alighting time distribution data 166 includes information including a predetermined time slot 51 and the number of passengers who get off the train at each station during this time slot 51 and depart from the station (number of passengers getting off the train 52). This is stored data. Passenger alighting time distribution data 166 is provided for each station. In the figure, the number of passengers getting off at each station is set in 10-minute increments.

(Standard passenger arrival time distribution data)
FIG. 5 is a diagram showing an example of standard passenger arrival time distribution data 167. The standard passenger arrival time distribution data 167 is data representing temporal fluctuations in the number of passengers arriving at the taxi rank of each station during a predetermined standard period (described later). Note that this number of passengers includes the number of users who used the railway and arrived at the taxi rank after getting off at each station, and the number of users who did not use the railway but arrived at the taxi rank by other methods.

It is assumed that the standard passenger arrival time distribution data 167 is created in advance by performing a predetermined statistical analysis on past passenger number data received from the taxi operation management system 300, for example. Further, the standard passenger arrival time distribution data 167 may be created by performing a predetermined statistical analysis based on probe information from a mobile phone or the like.

Specifically, the standard passenger arrival time distribution data 167 includes information including a predetermined time slot 61 and the number of passengers who arrived at the taxi stand at each station (number of arrivals 62) during this time slot 61 in the standard state. This is stored data. Passenger alighting time distribution data 166 is provided for each taxi stand at each station. In the figure, the number of passengers arriving at each taxi stand is set in 10-minute increments.

(Standard passenger alighting time distribution data)
FIG. 6 is a diagram showing an example of the standard passenger alighting time distribution data 168. The standard passenger alighting time distribution data 168 is data representing temporal fluctuations in the number of passengers who alight from the train at each station and depart from the same station during a predetermined standard period (described later).

It is assumed that the standard passenger alighting time distribution data 168 is created in advance by performing a predetermined statistical analysis on past data on the number of people alighting received from the railway operation management system 400, for example.

Specifically, the standard passenger alighting time distribution data 168 includes a predetermined time period 71 and the number of passengers who alighted at each station during this time period 71 under standard conditions (number of passengers alighting 72). This is data that stores information. Passenger alighting time distribution data 166 is provided for each station. In the figure, the number of passengers getting off at each station is set in 10-minute increments.

In this embodiment, the time resolution of the taxi arrival time distribution data 164, passenger arrival time distribution data 165, passenger alighting time distribution data 166, standard passenger arrival time distribution data 167, and standard passenger alighting time distribution data 168 is the same, and It is assumed that the band boundaries are also the same, but different time resolutions may be set for each piece of data. In this case, for example, you can set an interpolation function etc. assuming that the distribution is smooth, and then use this function etc. to match the data with the highest time resolution (i.e., the width of the time period is the narrowest). By adding additional data conversion processing, differences in the time resolution of each data can be absorbed.

Here, the standard period of the standard passenger arrival time distribution data 167 and the standard passenger disembarkation time distribution data 168 refers to a period in the past when the operation of the railway line was normal. For example, the standard period data is data when no accidents occur on each railway line, data on average values over a predetermined period, and the like. Note that the standard time may be set for each predetermined day of the week, weather, etc.

(Passenger behavior model)
Next, the passenger behavior model 169 is a model for calculating the ratio of taxi users to railway users.

In this embodiment, the passenger behavior model 169 is a model that stores, for each station, the ratio of the number of passengers who use taxis among the passengers who get off at that station to the total number of passengers who get off at that station. The passenger behavior model 169 holds the ratio for each station.

Next, the taxi operation management system 300 will be explained.

-Taxi operation management system-
The taxi operation management system 300 (300A, 300B) is an information processing system managed by each taxi company, and manages the operation of one or more taxis managed by the company.

The taxi operation management system 300 generates or stores information that is the basis of the taxi arrival time distribution data 164.

Specifically, for example, the taxi operation management system 300 stores the current location of each taxi, the presence or absence of passengers in the taxi, the number of passengers, and, if there are passengers in the taxi, their destination. The taxi operation management system 300 also instructs an empty taxi to dispatch to a predetermined station, and estimates the time when the empty taxi will arrive at the station.

Next, the railway operation management system 400 will be explained.

-Railway operation management system-
The railway operation management system 400 is a computer system that manages train operation while taking into account delays caused by various accidents and congestion. The railway operation management system 400 generates or stores information that is the basis of the passenger alighting time distribution data 166.

In this embodiment, the railway operation management system 400 is configured to include a travel demand prediction section 401, a train operation prediction section 402, and an on-line people flow prediction section 403.

The travel demand prediction unit 401 predicts the travel demand for the current day, including the time period after the current time, from historical information on passenger travel demand accumulated in the past and various sensor information acquired in real time.

For example, the travel demand forecasting unit 401 generates OD (Origin-Destination) data that includes information on how many passengers want to go from which station to which station in each time period with a predetermined time resolution.

Note that sensor information includes, for example, information for estimating the absolute number of passengers, such as the number of people passing through a station ticket gate, and images captured by a surveillance camera installed on a station platform. There is information for estimating the ratio of the number of people boarding up and down trains. Furthermore, the sensor information may be information for acquiring not only the passenger's entry station but also the destination station's information, such as ticket and commuter pass information acquired from an IC card reader. .

The train operation prediction unit 402 predicts the operation status of each train on that day, including the time period after the current time, using a timetable that is a train operation plan, train running records, and operation forecast information.

Note that the expected operation information is information input into the railway operation management system 400 by an operator in charge of operation management. For example, when the departure of a train at a certain station is expected to be delayed due to emergency relief, , is information for specifying ``which station and how much the departure of which train will be delayed.'' Further, the expected operation information may be information for specifying "which equipment cannot be used during which time period" when equipment such as railroad tracks cannot be used during a certain time period due to inspection or the like.

In addition to taking into account the expected operation information, the train operation prediction unit 402 also predicts each train operation while observing constraints such as "the order of trains using the same equipment will not be changed from the order determined by the timetable." By simulating the movement of trains, the future operating status of each train is predicted, and the predicted arrival time and departure time of each train at each station are output.

The on-line people flow prediction unit 403 calculates the travel demand of passengers on the day including the time period after the current time predicted by the travel demand prediction unit 401 and the time period after the current time of each train predicted by the train operation prediction unit 402. People flow prediction processing that predicts how many passengers will travel from which station to which station and how many passengers will board each train for each train on that day, including the time period after the current time, using the operation status of each train on that day. Execute.

Note that the human flow prediction process is performed based on, for example, multi-agent simulation.

The navigation device 10 uses such information generated by the railway operation management system 400 to update the passenger alighting time distribution data 166. For example, the navigation device 10 uses the train operation status (schedule) of the day, including the time period after the current time of each train, predicted by the train operation prediction unit 402 of the railway operation management system 400, and the on-line passenger flow prediction unit 403 predicted The passenger alighting time distribution data 166 is updated by receiving information such as "how many passengers are boarding each train at which station to get off the train".

Next, the processing performed in the navigation system 1 will be explained.

--process--
FIG. 7 is a flow diagram illustrating the route search process. The route search process is started, for example, when the navigation device 10 receives route search request information from the mobile terminal 200.

First, the navigation device 10 specifies the departure point, destination, and departure time (step S801).

For example, the navigation device 10 extracts data on the departure station (A station 11), data on the destination 20, and data on the departure time from the route search request information.

Next, the navigation device 10 searches for stations around the destination 20 (nearby stations) (step S802).

Specifically, the navigation device 10 refers to the map database 103 and identifies one or more nodes within a predetermined distance from the position indicated by the destination 20 acquired in step S801 (hereinafter referred to as a nearby node). For example, the navigation device 10 specifies a predetermined number of nodes that are closest to the location of the destination 20 or nodes that are within a predetermined distance from the destination 20 in order from those closest to the destination 20 .

Then, the navigation device 10 refers to the route database 104 and identifies stations around each identified nearby node (for example, identifies stations within a predetermined distance or within a predetermined number of stations). Finally, the navigation device 10 stores a set of each identified nearby node and the surrounding stations of each nearby node as a search result for nearby stations.

In this embodiment, B station 12, C station 13, and D station 14 around the destination 20 are searched as nearby stations.

Subsequently, the navigation device 10 initializes the searched route data 163 (step S803).

That is, the navigation device 10 deletes the data of previously searched routes and sets the searched route data 163 as an empty set.

The navigation device 10 performs a search from step S805 to be described later for each nearby station (more precisely, a set of a nearby node and a nearby station of the nearby node; hereinafter referred to as a processing target nearby station) searched in step S802. The processing up to step S809 is repeated (steps S804 to S809).

That is, first, in step S805, the navigation device 10 selects each railway from the departure station to a nearby station to be processed, which departs from the departure station (departure place) after the departure time, based on information on the operation schedule of each railway line. The route by line is searched, and the station arrival time, which is the arrival time at the nearby station to be processed, is calculated.

For example, the navigation device 10 acquires the timetable of each train predicted by the train operation prediction unit 402 of the railway operation management system 400. Based on this train schedule, the navigation device 10 searches for the railway route to be used and the arrival time at the nearby station to be processed using a route search technique such as Dijkstra's algorithm. Note that if the departure station and a nearby station are the same, the departure time is the station arrival time.

In this embodiment, the route from A station 11 to B station 12 and the arrival time at B station 12, the route from A station 11 to C station 13 and the arrival time at C station 13, and the route from A station 11 to D station 14 are explained. The route up to and the arrival time at station D 14 are calculated in each iterative process.

Subsequently, the navigation device 10 executes a taxi waiting time calculation process S806, which calculates a taxi waiting time based on the station arrival time determined in step S805. Details of the taxi waiting time calculation process S806 will be described later.

Then, the navigation device 10 calculates the taxi travel time by the taxi the user takes after waiting for the taxi waiting time (step S807).

Specifically, the navigation device 10 searches the route database 104 using the nearby node as a key to find the taxi travel time (taxi ride time from the nearby station to be processed to the nearby node) associated with the nearby station to be processed. get.

At this time, the navigation device 10 may correct the calculated travel time of the taxi based on the influence of weather or the like. In addition, the navigation device 10 uses a route search technique using a predetermined car navigation system or the like to select a taxi by using a nearby station to be processed as a departure point, a destination 20 or a nearby node as an arrival point, and taking the taxi waiting time into consideration. It is also possible to calculate the taxi travel time when the boarding time is the departure time.

Next, the navigation device 10 calculates the arrival time of the destination 20 (step S808).

Specifically, the navigation device 10 calculates the arrival time at the destination 20 by adding the taxi waiting time calculated in step S806 and the taxi travel time calculated in step S807 to the station arrival time calculated in step S805. Find the time.

In this embodiment, the arrival time at the destination 20 (nearby node) when going through B station 12 and a taxi, the arrival time at the destination 20 (nearby node) when going through C station 13 and a taxi, and D The arrival time at the destination 20 (nearby node) when passing through the station 14 and a taxi is calculated in each iterative process.

The navigation device 10 then registers the route search results regarding the nearby stations to be processed in the searched route data 163 (step S809).

Specifically, the navigation device 10 includes a route including a transfer station for each railway from a departure station to a nearby station to be processed, a departure time at each station on the route, an arrival time at each station on the route, and a route to be processed. A set of information on the taxi waiting time at a nearby station and the arrival time at the destination is registered in the searched route data 163.

After the navigation device 10 executes the process regarding the route from the departure point to each target nearby station for all target nearby stations (steps S805 to S809), it executes the process of step S810.

That is, in step S810, the navigation device 10 sorts the route search results of the searched route data 163 registered in step S809 using the arrival time at the destination as a key, and sorts the route search results by a predetermined number starting from the earliest arrival time at the destination. The route search result is transmitted to the mobile terminal 200.

The mobile terminal 200 displays a screen showing the route search results (route search result screen) on the screen. Details of the route search result screen will be described later.

Note that the navigation device 10 sends some information of the route search results, such as information about the distribution of taxi queue lengths in each time period, which is calculated during the route search process, to each taxi operation management system 300. It may be transmitted (S811).

With this, the route search process ends.

<Taxi waiting time calculation process>
FIG. 8 is a flow diagram illustrating details of the taxi waiting time calculation process S806 in the route search process.

First, the navigation device 10 acquires the distribution of arrival times of taxis to the processing target nearby station from the taxi arrival time distribution data 164 (step S901). Note that the taxi arrival time distribution data 164 is updated as needed based on data received from the taxi operation management system 300, as described above.

The navigation device 10 calculates the arrival time of all taxis to the processing target nearby station by considering the taxis that are not taken into account when updating the taxi arrival time distribution data 164 with respect to the arrival time distribution acquired in step S901. Correction is performed to calculate the distribution (step S902).

That is, since the information acquired by the navigation device 10 in step S901 is only the information about taxis of a predetermined company (hereinafter referred to as "this taxi") received from the taxi operation management system 300, the distribution of arrival times of taxis cannot be correctly predicted. In order to do so, it is preferable to consider the behavior of taxis of other companies (hereinafter referred to as other taxis).

Therefore, for example, the navigation device 10 acquires statistical information on the number of taxis (this taxi and other taxis) (all taxis) that were staying at a nearby station to be processed at a certain time in the past from a predetermined database or an external system. Based on this statistical information, the navigation device 10 calculates the ratio of the number of actual taxis to the total number of taxis by calculating (number of actual taxis)/(number of actual taxis + number of other taxis), and calculates the ratio of the number of actual taxis to the total number of taxis. Use as a correction parameter. Based on this correction parameter, the navigation device 10 calculates the distribution of arrival times of all taxis from the number of taxis calculated in step S901. Although there is room to consider differences in destinations when handling passengers between this taxi and other taxis, considering the overall situation, it is expected that the behavior of this taxi and other taxis will be similar. The total number of taxis determined using this method can be expected to be a good approximation of the actual number.

Further, in step S902, the navigation device 10 may perform the following processing. First, the navigation device 10 estimates the length of the taxi queue of passengers existing at the taxi stand based on information obtained in advance from a surveillance camera or the like installed at a nearby station to be processed. The navigation device 10 determines the time change of passengers who boarded a taxi at the taxi stand based on the calculated time change of the taxi queue and the time change of passengers arriving at the taxi stand. Then, the navigation device 10 calculates the number of vehicles dispatched by other taxi companies by acquiring the temporal change in the number of dispatched vehicles of the taxi company that directly corresponds to this temporal change, and uses the ratio of the number of dispatched vehicles between the two companies. Then, the distribution of arrival times of all taxis to the nearby station to be processed is calculated.

By performing the correction in step S902 as described above, even if the navigation device 10 cannot directly grasp the behavior of all the taxis that exist, the navigation device 10 can calculate the arrival time of all the taxis that is well approximated. distribution can be obtained.

Subsequently, the navigation device 10 registers the provisional value of the distribution of passenger arrival times at the taxi queue (taxi stand) at the nearby station to be processed in the passenger arrival time distribution data 165 (step S903).

Specifically, the navigation device 10 reads the standard passenger arrival time distribution data 167 and stores it in the passenger arrival time distribution data 165.

Note that if the standard passenger arrival time distribution data 167 includes data according to the weather, day of the week, etc., the navigation device 10 uses the standard passenger arrival time distribution data 167 to calculate the weather and the weather at the time of arrival at the station or around that time. It may be adjusted to a value corresponding to the day of the week.

Furthermore, the navigation device 10 acquires the distribution of the alighting times of railway passengers at the nearby stations to be processed (step S904).

Specifically, the navigation device 10 reads the passenger alighting time distribution data 166. Note that, as described above, the passenger alighting time distribution data 166 is updated from time to time based on information received from the railway operation management system 400.

Next, the navigation device 10 calculates the distribution of the arrival times of passengers at the taxi stand obtained in step S903 according to the situation of the taxi users under the search conditions (for example, the distribution of the arrival times of passengers at the taxi stand when the train service is disrupted). (step S905).

That is, the length of the taxi queue at the taxi stand at a station is greatly influenced by the number of passengers using the railway, so it is necessary to correct this according to the situation such as disruption of railway operation. However, on the other hand, there are also non-railway passengers who use taxis, regardless of the operating status of the railway. The standard passenger arrival time distribution data 167 acquired in step S903 includes information on both taxi users who use railways and taxi users who do not use railways, so under the search conditions A more accurate distribution of arrival times can be obtained by specifically taking into account changes in the number of taxi users based on railway operating conditions.

Specifically, the navigation device 10 subtracts the number of people alighting 72 in each time period 71 indicated by the standard passenger alighting time distribution data 168 from the number of people alighting 52 in each time period 51 indicated by the passenger alighting time distribution data 166. Then, the difference in the number of passengers getting off at the target nearby station in each time period between the standard time and the time indicated by the search conditions is calculated.

Then, the navigation device 10 calculates the difference in the number of passengers arriving at the taxi stand at the nearby station to be processed by applying the calculated difference in the number of passengers getting off at the nearby station to be processed to the passenger behavior model 169. demand. Then, the navigation device 10 adds the calculated difference between the number of passengers arriving at the taxi stand at the nearby station to be processed to the number of passengers arriving at the taxi stand at the nearby station to be processed in the passenger arrival time distribution data 165. By adding, the corrected passenger arrival time distribution data 165, that is, the distribution of passenger arrival times at the taxi stand of the processing target nearby station in each time period is calculated. Note that if the calculated number of passengers is a negative value, the number of arrivals is set to "0".

Here, a specific example of the corrected passenger arrival time distribution data 165 generated in step S905 will be described.

(Passenger arrival time distribution data)
FIG. 9 is a diagram showing an example of generated passenger arrival time distribution data 165. The passenger arrival time distribution data 165 is data that stores information including a predetermined time slot 41 and the number of passengers arriving at the taxi stand of each station during this time slot 41 (number of arrivals 42). Passenger arrival time distribution data 165 is provided for each taxi stand at each station. In the figure, the number of passengers arriving at each taxi stand is set in 10-minute increments.

Subsequently, in step S906 of FIG. 8, the navigation device 10 calculates the time distribution of the number of taxis arriving at the taxi stand of the nearby station to be processed, calculated in step S902, and the distribution of taxis at the nearby station to be processed, calculated in step S905. The processing target neighborhood calculated in step S805 based on the time distribution of the number of passengers arriving at the taxi stand and parameters that define how to allocate each passenger to each taxi at the taxi stand (hereinafter referred to as allocation parameters). An allocation simulation is executed to calculate the user's taxi waiting time when the user arrives at the passenger's arrival time at the taxi stand at the station.

Specifically, for example, the navigation device 10 calculates the number of passengers in each time period based on the number of taxis arriving at the taxi stand of the nearby station to be processed and the number of passengers arriving at the taxi stand in each time period. Calculate the length (number of people) of the taxi queue. At this time, the navigation device 10 assigns each passenger in the taxi queue to each taxi in order from the passenger at the head of the taxi queue (the passenger arriving earlier).

Note that the allocation parameter is, for example, the average number of passengers per taxi. The average number of passengers in the passenger assignment method parameter is calculated in advance using, for example, past taxi riding history. By using such ride assignment method parameters, each taxi can be assigned to each passenger in a realistic manner.

In this simulation, taxis that are not assigned to any passengers in a certain time slot are assigned to passengers in the next time slot or later. On the other hand, passengers who cannot be assigned a taxi in a certain time slot leave their taxi queues in the next time slot and are assigned a taxi in the next time slot.

Then, the navigation device 10 ends this simulation when all the passengers present at the taxi stand are assigned to one of the taxis by the time period to which the arrival time of the processing target nearby station calculated in step S805 belongs. do. The navigation device 10 outputs the difference between the end time in the time zone when the simulation ends and the arrival time at the processing target nearby station calculated in step S805 as the taxi waiting time.

In this case, whether all passengers have been assigned to one of the taxis can be determined by, for example, adding a new passenger to the taxi queue in the simulation after the time period that includes the specified time. In addition to adding the condition that ``no taxi is available'', all passengers are assigned to one of the taxis if ``the length of the taxi queue becomes 0 after the specified time period''. judge.

Note that in step S905, the navigation device 10 may generate and use information corresponding to the passenger alighting time distribution data 166 and the standard passenger alighting time distribution data 168 through the following process. First, the navigation device 10 acquires train location information for each railway line that is open to passengers, and applies the acquired train location information to a predetermined train operation simulation. Calculate or estimate train operation. The navigation device 10 also calculates the number of passengers on each train who will get off at the nearby station to be processed, based on this information on the operation of each train and each route search request information that the navigation device 10 has received so far. Estimate whether there are any.

Here, the route search result screen 1000 displayed on the mobile terminal 200 as a result of executing the route search program 161 and the taxi waiting time calculation program 162 described above will be described.

(Route search results screen)
FIG. 10 is a diagram showing an example of a route search result screen 1000.

This route search result screen 1000 displays a plurality of routes 1010 in descending order of arrival time 1001 to the destination 20.

The information on the plurality of routes 1010 includes information 1011 on each boarding station and alighting station on each route 1010, and information 1012 regarding taxi usage.

Further, the information 1012 related to taxi usage includes a taxi waiting time 1013 and the number of people waiting in the taxi queue 1014 at the time of arrival at a taxi stand at a nearby station (the time period belonging to the time of arrival).

By referring to the route search result screen 1000, the user can rationally determine which route should be used to reach the destination 20 according to the user's circumstances.

Note that the route search results on the route search result screen 1000 are displayed in order of earliest arrival time, lowest usage fee, lowest number of transfers, shortest taxi waiting time, or lowest number of people waiting for a taxi. The order may be based on various criteria. Further, as the information 1011 for each boarding station and alighting station on each route 1010, the departure time and arrival time of each station may also be written.

As described above, the navigation system 1 of the present embodiment calculates the taxi waiting time at a taxi stand based on the flow of passengers at nearby stations and the taxi stand, and calculates the taxi waiting time at the taxi stand in route searches by railways and taxis. The travel time from the starting point to the destination is calculated and displayed.

In this manner, the navigation system 1 of the present embodiment can perform route searches using railways and road vehicles while appropriately considering the waiting time for use of road vehicles.

In particular, if train operations are disrupted, the flow of passengers will be significantly different from normal, and the waiting time for taxis at each station may change significantly, greatly affecting the overall travel time. However, the navigation system of this embodiment According to No. 1, even in such a case, appropriate information can be provided to a user searching for a route.

The present invention is not limited to the embodiments described above, and includes various modifications. The embodiments described above are described in detail for better understanding of the present invention, and are not necessarily limited to those having all the configurations described.

For example, some or all of the functions of each device may be provided in another device, or the functions of multiple devices may be combined into one device. For example, some or all of the functions of the taxi traffic management system 300 and the railway traffic management system 400 may be provided in the navigation device 10. Further, the navigation device 10 may display the screen of the mobile terminal 200.

Further, in this embodiment, a taxi is used as a road vehicle to be searched, but other types of road vehicles that run on roads, such as a route bus, may be used. By performing an allocation simulation based on a predetermined maximum number of passengers under the condition that vehicles are dispatched according to the timetable, it is possible to estimate the waiting time in the same way as in the case of a taxi.

Further, in this embodiment, the road vehicle is limited to use from a nearby station to the destination 20, but the road vehicle may be used for other journeys to the nearby station.

Further, in this embodiment, an example is shown in which there is always one taxi stand at each nearby station, but the present invention is not limited to this. Specifically, for example, stations that do not have a taxi stand are excluded from the search for travel routes using taxis. Furthermore, if there are multiple taxi stands at a nearby station, taxi arrival time distribution data 164, passenger arrival time distribution data 165, standard passenger arrival time distribution data 167, and passenger behavior model 169 may be set for each taxi stand. Then, a simulation is executed to find the taxi waiting time for each passenger at each taxi stand (taxi queue). When searching for a route that includes a travel route using a taxi, the taxi travel time from each taxi stand to a nearby node is determined, and each route is searched based on the previously calculated taxi waiting time and taxi travel time. Then, the route that arrives at the destination quickly is the route presented to the mobile terminal 200.

Further, in this embodiment, the navigation device 10 is configured to receive information regarding train operation and information regarding passenger flow within the railway from the railway operation management system 400, but other configurations may be used. The navigation device 10 only needs to be able to obtain the same information as the information held by the railway operation management system 400 periodically or in response to an active request. For example, it may be configured to receive information from one or more railway information systems (systems that handle information regarding railway operation status and passenger flow).

Furthermore, in the present embodiment, the navigation device 10 obtains the passenger arrival time distribution data 165 based on the passenger alighting time distribution data 166, the standard passenger arrival time distribution data 167, and the standard passenger alighting time distribution data 168. Although the simulation regarding the allocation of each taxi to each passenger was performed with reference to the taxi arrival time distribution data 164 (step S906 in FIG. 9), other methods may be used. For example, by installing a sensor that detects a taxi queue at a taxi stand and using the information from that sensor to estimate the length of the taxi queue at the current time, the estimated length of the queue can be calculated. As a reference, simulations may be performed in each time period after the current time. This makes it possible to ignore errors related to the state before the current time, so it is possible to estimate the taxi waiting time more accurately.

Further, information such as standard passenger arrival time distribution data 167, standard passenger disembarkation time distribution data 168, and passenger behavior model 169 may be provided for each condition such as seasonal event, month, day of the week, time, or weather. . Note that this information is obtained from past performance data under corresponding conditions. Thereby, the waiting time for a taxi can be estimated more accurately.

The above description of this specification clarifies at least the following. That is, in the present embodiment, in the waiting time calculation process, the navigation device calculates the waiting time based on temporal fluctuations in the number of other passengers arriving at the boarding location and temporal fluctuations in the number of road vehicles arriving at the boarding location. It may also be estimated based on.

In this way, by estimating the waiting time for a taxi based on the temporal variation in the number of passengers arriving at the taxi stand and the temporal variation in the number of taxis arriving at the taxi stand, the waiting time for the taxi can be accurately estimated. be able to.

Further, in the present embodiment, the navigation device, in the waiting time calculation process, includes information indicating a trend in the number of passengers who get off at the station where they get off under the conditions, based on the temporal fluctuation in the number of passengers arriving at the boarding location; The estimation may be made based on the number of passengers present at the boarding location at a predetermined time in the past, which has been obtained in advance.

In this way, we can compare the temporal fluctuations in the number of passengers arriving at the taxi stand with the trends in the number of passengers arriving at the taxi stand after getting off at the station under the search conditions and the trends in the number of passengers arriving at the taxi stand after getting off at the station in normal times. By estimating this based on the trends in the number of passengers that normally exist at taxi stands, even if the time of search is not during normal train operation (for example, during a time when train service is disrupted), the operation status can be estimated. It is possible to accurately estimate both the number of taxi users from railways who are affected by the current situation and the number of other taxi users whose operation status is not affected. That is, among the temporal distribution of the number of passengers arriving at the taxi queue, the distribution regarding passengers who are both railway users and taxi users and the distribution regarding other taxi users can be managed simultaneously.

Further, in the present embodiment, the navigation device may estimate, in the waiting time calculation process, a temporal change in the number of road vehicles arriving at the boarding location based on information received from a predetermined road vehicle management system. .

In this way, by estimating the temporal variation in the number of taxis arriving at the taxi stand based on information received from an external system such as the taxi operation management system 300, it is possible to more accurately estimate the temporal variation in the number of taxis. can do.

Further, in the present embodiment, in the waiting time calculation process, the navigation device receives a temporal change in the number of road vehicles arriving at the boarding location from the road vehicle management system, and calculates the presence of vehicles at the boarding location under the conditions. The estimation may be made based on the number of road vehicles present at the boarding location and the number of road vehicles that were present at the boarding location at a predetermined time in the past, which was obtained in advance.

In addition to taxis of companies managed by the taxi operation management system 300, there are taxis of other companies not managed by the taxi operation management system 300 at nearby stations. Therefore, the time fluctuation in the number of taxis arriving at the taxi stand is calculated based on the number of taxis existing at the taxi stand at the time of the search received from the taxi operation management system 300 and the number of taxis existing at the taxi stand at the time of the search (not limited to the above taxis, for example, all taxis) at a predetermined time in the past. ) By estimating based on the number of taxis, it is possible to estimate the taxi waiting time more accurately based on the number of taxis arriving at nearby stations.

Further, in the present embodiment, in the waiting time calculation process, the navigation device calculates temporal fluctuations in the number of passengers present at the boarding location at each time after the current time including the arrival time of the passenger at the boarding location. , estimated based on the temporal variation in the number of passengers arriving at the boarding location and the temporal variation in the number of road vehicles arriving at the boarding location, and in the route search process, the temporal variation in the calculated number of passengers; The information may be transmitted to the road vehicle management system.

In this way, by estimating the number of people waiting for taxis in each time period after the current time at the taxi stand and transmitting the estimated number to the taxi operation management system 300, each taxi operation management company can thereafter estimate the number of people waiting for taxis at each time period after the current time. Since it becomes possible to update the taxi dispatch plan by taking length into consideration, it is possible to realize taxi operations that are more convenient for both taxi suppliers and taxi users.

Further, in the present embodiment, in the waiting time calculation process, the navigation device calculates the number of passengers present at the boarding location at the arrival time of the predetermined passenger at the boarding location, and the number of passengers arriving at the boarding location. and the time variation of the number of road vehicles arriving at the boarding location, and in the route search process, outputting information on the estimated number of passengers existing at the boarding location. Good too.

In this way, by estimating and displaying the number of people waiting for a taxi at a taxi stand, the user can obtain useful information when making a decision regarding whether or not to use a taxi or how to use it.

1 navigation system, 10 navigation device, 300 taxi operation management system, 400 railway operation management system

Claims (8)

It includes a processor and a memory, and
As a travel route for a passenger traveling from a departure point to a destination under conditions where the railway schedule is more disrupted than usual, a passenger gets off at a railway station and a boarding location near the station from which the passenger gets off. a route identification process for identifying a route to travel by taxi from to the destination;
The waiting time of the passenger at the boarding location, from arrival at the boarding location to the start of travel by the taxi , is based on the predicted value under the conditions of the passenger flow at the alighting station and the past number of passengers. Waiting time calculation processing that estimates based on statistical values and statistical values based on past passenger numbers of passenger flow at the boarding location ;
Route travel time calculation processing that calculates the travel time from the departure point to the destination along the route, including the estimated waiting time;
executing a route search process including a result output process of outputting information on the calculated travel time;
navigation device. In the waiting time calculation process, the waiting time is set to the predicted value and statistical value of the temporal fluctuation of the number of passengers leaving the station where the train gets off the train, and the predicted value and statistical value of the flow of passengers at the station where the train gets off the train. A statistical value of the temporal change in the number of passengers arriving at a location is used as a statistical value of the flow of passengers at the boarding location, and further estimated based on an estimated value of the temporal fluctuation in the number of taxis arriving at the boarding location.
The navigation device according to claim 1. In the waiting time calculation process, a predicted value of the temporal fluctuation of the number of passengers arriving at the boarding location is combined with information indicating a trend in the number of passengers getting off at the station at which the train gets off at the station at which the train gets off at the station at which the train gets off at the station at which the boarding station is set. Based on statistical information showing trends in the number of passengers at a predetermined time in the past, and the number of passengers who were present at the boarding location at a predetermined time under normal conditions where the railway timetable is not disrupted, which has been obtained in advance. Estimate based on
The navigation device according to claim 2. In the waiting time calculation process, information indicating the time at which each taxi will arrive at the alighting station is received from a taxi operation management system that manages the operation of each taxi based on temporal fluctuations in the number of taxis arriving at the boarding location. Estimate based on
The navigation device according to claim 2. In the waiting time calculation process, a predicted value of a temporal change in the number of taxis arriving at the boarding location is combined with a predicted value of the number of taxis existing at the boarding location under the conditions received from the taxi operation management system. , based on the number of taxis of the business operator related to the taxi operation management system and the number of taxis of a business different from the aforementioned business operator, which were obtained in advance and were present at the boarding place at a predetermined time in the past. presume,
The navigation device according to claim 4. In the waiting time calculation process, at each time after the current time including the arrival time of the passenger at the boarding location, time fluctuations in the number of passengers present at the boarding location are calculated as the number of passengers arriving at the boarding location. Estimating based on time variation and time variation in the number of taxis arriving at the pick-up location,
In the route search process, transmitting the calculated temporal change in the number of passengers to the taxi operation management system;
The navigation device according to claim 4. In the waiting time calculation process, the number of passengers present at the boarding location at the arrival time of the passenger traveling from the departure point to the destination under the condition at the boarding location is calculated by calculating the number of passengers who are present at the boarding location and getting off the train under the condition. Information showing trends in the number of passengers getting off at the station, statistical information showing trends in the number of passengers getting off at the station at a given time in the past, and past changes in the number of passengers arriving at the boarding location over time. Estimating based on a statistical value based on the number of taxis and an estimated value of the temporal variation of the number of taxis arriving at the pick-up location,
In the route search process, outputting information on the estimated number of passengers existing at the boarding location;
The navigation device according to claim 1. An information processing device including a processor and a memory,
As a travel route for a passenger traveling from a departure point to a destination under conditions where the railway schedule is more disrupted than usual, a passenger gets off at a railway station and a boarding place near the station from which the passenger gets off. a route identification process for identifying a route to travel by taxi from to the destination;
The waiting time of the passenger at the boarding location, from arrival at the boarding location to the start of travel by the taxi , is based on the predicted value under the conditions of the passenger flow at the alighting station and the past number of passengers. Waiting time calculation processing that estimates based on statistical values and statistical values based on past passenger numbers of passenger flow at the boarding location ;
Route travel time calculation processing that calculates the travel time from the departure point to the destination by the route, including the estimated waiting time;
executing a route search process including a result output process of outputting information on the calculated travel time;
navigation method.

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