JP2007249277A - System, method, and program for predicting congestion status of vehicle - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To average the degrees of congestion by predicting the congestion rate of each vehicle of a train. <P>SOLUTION: A counting server 10 computes the number of passengers who get off at each destination station read from an entrance side ticket gate 100 in every station. Based on the statistical data of the ratio of boarding to each vehicle that differs with a different destination station, a processing server 200 predicts the congestion rate of each train. A distribution server 30 displays the congestion rate of each vehicle on an electrical scoreboard 70 in each station, and sends the data about the congestion rate of each vehicle as requested by users. In this way, the congestion rates of the vehicles are averaged. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a vehicle congestion situation prediction system, method, and program, and more specifically, a vehicle congestion situation prediction system and method capable of providing, in real time, a congestion degree situation for each vehicle to train users, and It relates to a program for such a system.

  Congestion of trains during commuting to school in urban areas is a big problem. Many passengers tend to get on the vehicles that stop near the exits and stairs of the station where they get off, and the current situation is that the congestion situation varies from vehicle to vehicle. Also, when using a train for the first time on land, there is no way to know which vehicle is vacant, and sometimes it gets on a crowded vehicle. If passengers can know the congestion status of each vehicle in advance, the number of passengers in each vehicle is smoothed, and it is considered that congestion during rush hours is alleviated.

  As a method of detecting the degree of congestion of a vehicle, a method of adding a new device in a vehicle or a station platform has been conventionally proposed. Patent Document 1 describes a method of measuring the number of people passing by installing an infrared sensor at the doorway of a vehicle. Patent Document 2 describes a method of analogizing the congestion state of a vehicle by providing a sensor that senses contact of a human body with the hanging leather of the vehicle. Further, Patent Document 3 discloses a method for estimating boarding conditions by transmitting boarding data by giving a wireless communication function to a boarding ticket, reading the boarding data with a sensor installed in the vehicle or in a station platform. Proposed.

  However, in the above conventional method, it is necessary to newly install a sensor in a station or a vehicle in order to grasp the congestion state of the vehicle, and also read information on a ticket medium such as a ticket, a coupon ticket, a commuter pass, etc. In some cases, it is necessary for the ticket medium to have a communication function. For this reason, there has been a problem that enormous capital investment is required for realization. Furthermore, these conventional methods only provide the vehicle congestion status from time to time, and even if the user chooses and rides a relatively free vehicle, the next station is crowded with other vehicles. There was also a problem that the situation could be reversed.

JP-A-7-167963 JP 10-297492 A Japanese Patent Laid-Open No. 8-167058 JP 2002-193103 A

  Patent Document 4 describes a train operation management system that counts the number of users per unit time passing through an automatic ticket gate and destination data when predicting the degree of vehicle congestion. However, in the train operation management system described in this publication, although it is described that the number of vehicles is increased according to the congestion state, there is no description that the congestion state for each vehicle is predicted.

  In view of the problem of the conventional vehicle congestion status detection method, the present invention can estimate the congestion level for each vehicle with a small capital investment, and not only the congestion status for each vehicle at that time but also the next time point. It is an object of the present invention to provide a vehicle congestion situation prediction system and method, and a program that can also predict the congestion situation of each vehicle in the vehicle.

In order to achieve the above object, the vehicle congestion rate prediction system according to the present invention receives data including an alighting station read by an automatic ticket gate from a ticket passing through an automatic ticket gate on the entrance side of each station. A counting device that counts the number of people getting off at the station for each getting off station,
A user who passes through an automatic ticket gate with reference to a storage device that stores statistical data in which a boarding ticket exit station and a user having a boarding ticket that specifies the boarding station associate with a boarding ratio of getting into each vehicle. Means for predicting the vehicle on which the vehicle gets, calculating the number of people who get on and off each vehicle based on the prediction, and means for calculating the congestion rate for each vehicle by counting the number of people calculated by the means And an arithmetic processing unit having the above.

Further, the vehicle congestion rate prediction method of the present invention is a method for predicting the congestion rate for each vehicle of a train,
Receiving data including alighting station read by the automatic ticket gate from a ticket passing through an automatic ticket gate on the entrance side of each station, and counting the number of people getting off at each station,
A user who passes through an automatic ticket gate with reference to a storage device that stores statistical data that associates the boarding station of the boarding ticket and the boarding ratio at which the user having the boarding ticket specifying the boarding station gets into each vehicle. Predicting the vehicle on which the vehicle is boarded, and calculating the number of people who get on each vehicle and the number of people who get off based on the prediction;
And a step of calculating the congestion rate for each vehicle by counting the number of persons calculated in the calculation step.

Furthermore, the program of the present invention is a program for a computer that predicts a congestion rate for each train vehicle, and the computer includes:
Receiving the data including the getting-off station read by the automatic ticket checker from the ticket passing through the automatic ticket checker on the entrance side of each station, and counting the number of people getting off at each getting-off station for each getting-off station;
A user who passes through an automatic ticket gate with reference to a storage device that stores statistical data in which a boarding ticket exit station and a user having a boarding ticket that specifies the boarding station associate with a boarding ratio of getting into each vehicle. Predicting the vehicle on which the vehicle is boarded, and calculating the number of people who get on each vehicle and the number of people who get off based on the prediction;
And a step of calculating the congestion rate for each vehicle by counting the number of persons calculated in the calculation step.

  In the vehicle congestion situation prediction system, method, and program according to the present invention, the ticket information read by the ticket gate is used, and the congestion situation of the vehicle is predicted based on statistical data examined in advance. For this reason, it is not necessary to install a sensor for each station or each vehicle, and it is not necessary to provide a data transmission function for a ticket or the like, so it is possible to realize a congestion situation prediction system at a lower cost than conventional methods. . Further, by collecting data that affects the congestion status of the vehicle at each station, it is possible to predict not only the current congestion status of the vehicle but also the congestion status at the next time point. Note that the term “ticket” used in the present invention includes various ticket media in which destinations are described, such as a coupon ticket, a commuter pass, and a storage medium, in addition to a normal ticket ticket.

  The vehicle congestion situation predicted by the vehicle congestion situation prediction system of the present invention is preferably transmitted to the user's portable terminal or an electronic bulletin board in the station via the Internet. In addition, by measuring the number of people who have passed through the ticket gate at a station ahead of the station where the user gets on and updating the congestion status as needed, the user can know the congestion status up to several stations away.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. FIG. 1 is a block diagram showing a configuration of a vehicle congestion situation prediction system according to an embodiment of the present invention. In FIG. 1, a vehicle congestion situation prediction system according to the present embodiment includes a ticket gate (automatic ticket gate) 100 installed at an entrance-side ticket gate of each station, an aggregation server 10, a processing server 20, a distribution server 30, and a user. Is composed of a mobile communication network including a computer (PC) 40, a mobile phone 60, an electric bulletin board 70 in the station, a wired network such as the Internet 80, and a wireless relay station 50.

  The tabulation server 10 sequentially tabulates information on the ticket medium read by the ticket gate 100 at each station. The information to be totaled is information on the number of people who have passed through the ticket gate 100, and information on the alighting station read from a commuter pass or ticket. The aggregation server 10 transmits the aggregated data to the processing server 20 in time units. The processing server 20 predicts future boarding conditions using the total information and statistical data received from the total server 10. First, predict which train you will get from the information of the station where you get off. For example, with regard to the latest train, it is determined whether or not to take the train depending on whether the station is included in the stop station between the first station and the last station. As a result, the number of people on the train (train) from the station is predicted.

  When the number of passengers at a given station of a certain train is determined, the processing server 20 calculates the number of passengers who enter each vehicle based on the statistical data. For example, as shown in FIG. 6, a boarding ratio indicating the ratio of vehicles getting on the basis of the getting-off station is obtained, and this is adopted as statistical data. In this case, the number of passengers predicted as described above is allocated to each vehicle in proportion to the information on the getting-off station. In addition, it will be deducted at this ratio at the getting-off station. The statistical data is obtained by sampling the number of passengers of the corresponding vehicle during a certain period and using an experience value based on the sampling. In addition, it is good also considering the boarding ratio for every vehicle which considered not only the boarding station but both boarding station and boarding station as statistical data. In this case, a more accurate boarding ratio can be obtained.

  The distribution server 30 accepts access from the user's mobile phone (portable terminal) 60 and returns a prediction result of the congestion rate (congestion status) for each vehicle from the data of the processing server 20. In addition, the congestion status for each vehicle is displayed on the electric bulletin board 70 in the station.

  The congestion situation prediction system of FIG. 1 operates as follows. First, the exchange between the user and the distribution server 30 will be described. In step 1, as shown in FIG. 2, the user inputs information including a boarding station, an arrival station, and a departure time from a search screen of a portable terminal or a computer (PC), and accesses the distribution server 30. In step 2, the distribution server 30 summarizes the congestion rate (congestion status) information for each vehicle of the corresponding train calculated by the processing server 20 in response to the access from the user, and stores it in the mobile phone 60 or the PC 40 of the user. Answer. For example, as shown in FIG. 3, this congestion degree information includes the predicted congestion rate from the starting station to the boarding station used by the user, the stop station on the way, and the arrival station for each vehicle.

  Next, processing procedures of the aggregation server 10 and the processing server 20 will be described. In step 1, the tabulation server 10 tabulates the ticket medium information read by the ticket gate 100 one by one. In step 2, the aggregation server 10 transmits the aggregated data to the processing server 20 at regular time intervals. As shown in FIG. 4, the data transmitted from the aggregation server 10 to the processing server indicates, for example, every 10 minutes, the number of people who have passed through the ticket gates of each station, the passenger exit station that has passed the ticket gate, and the number of people. It is a list. The example of FIG. 4 shows an example of the B station.

  In step 3, the processing server 20 calculates the number of passengers and the number of passengers getting off for each vehicle based on the information from the aggregation server 10 and the statistical data. FIG. 5 shows the calculated data, and FIG. 6 shows the statistical data used for the calculation. The statistical data in FIG. 6 indicates the boarding ratio of each vehicle, which indicates the ratio of the vehicle that the user gets into at each departure station. For example, 10% of people getting off at station B get on the vehicle 1, and 20% get on the vehicle 2. The statistical data is obtained by sampling the number of users boarding for each vehicle for a certain period. The calculation result of FIG. 5 shows the result of predicting the congestion rate of each station from the number of passengers and the number of passengers getting off at each station predicted using the aggregate data shown in FIG. 4 and the statistical data shown in FIG.

  In the vehicle congestion situation prediction system of the above-described embodiment, as a first advantage, an automatic ticket gate that has been conventionally used is used. In addition, since the boarding situation is predicted using information read from the ticket gate and statistical data, unlike conventional systems, additional equipment such as sensors for stations and vehicles is not required, so equipment costs are low. I'll do it. Furthermore, since an existing ticket or commuter pass can be used as a boarding ticket, it is possible to provide a vehicle congestion situation at low cost.

  In the said embodiment, there also exists the 2nd advantage that the congestion rate to a station ahead of a boarding station is predictable. That is, if the number of passengers is known, the number of passengers who can get on each vehicle can be predicted based on the statistical data, so that it is possible to predict and provide the vehicle congestion situation up to several stations ahead.

  As described above, in the above embodiment, it is possible to provide a vehicle congestion situation at a lower cost than the conventionally proposed method by using the ticket information, and based on the number of people getting on and off to several stations ahead. There is an effect that it is possible to provide a crowded situation.

  In recent years, there is known a ticket medium that collects the fee for the first boarding at the ticket gate at the boarding station and settles the boarding fee at the ticket gate at the getting-off station. In the case of such a ticket medium, it is preferable that the processing server calculates the number of passengers getting off for each vehicle using data obtained by the ticket gate on the entry side. That is, since the boarding station and the getting-off station are obtained from the data obtained by the ticket gate on the entry side, the congestion rate of each vehicle is calculated according to the boarding rate determined based on the getting-off station.

  As mentioned above, although this invention was demonstrated based on the suitable embodiment, the vehicle congestion rate prediction system of this invention, a method, and a program are not limited only to the structure of the said embodiment, The structure of the said embodiment To which various modifications and changes are made within the scope of the present invention. In addition, each configuration described as a preferred aspect of the present invention or each configuration described in the embodiment is preferably used together with the essential configuration of the present invention, but about a configuration that exhibits a beneficial effect even when used alone. However, it is not always necessary to use all the configurations described as the essential configurations of the present invention.

1 is a block diagram of a vehicle congestion rate prediction system according to an embodiment of the present invention. An example of a user terminal search screen. An example of a search result screen of a user terminal. An example of a summary screen aggregated by a summary server. The example of the screen which shows the congestion condition obtained by the processing server. The example of the statistical data utilized in order to calculate a vehicle congestion rate.

Explanation of symbols

10: Total server 20: Processing server 30: Distribution server 40: PC
50: Wireless relay station 60: Mobile phone 70: Electronic bulletin board 80: Internet 100: Automatic ticket gate

Claims (7)

A counting device that receives data including the getting-off station read by the automatic ticket checker from the ticket that passes through the automatic ticket checker on the entrance side of each station, and totals the number of people getting off at each getting-off station for each getting-off station,
A user who passes through an automatic ticket gate with reference to a storage device that stores statistical data in which a boarding ticket exit station and a user having a boarding ticket that specifies the boarding station associate with a boarding ratio of getting into each vehicle. Means for predicting the vehicle on which the vehicle gets on, calculating the number of people who get on and off each vehicle based on the prediction, and means for calculating the congestion rate for each vehicle by counting the number of people calculated by the means A vehicle congestion rate prediction system comprising: an arithmetic processing unit having:   The vehicle congestion rate prediction system according to claim 1, further comprising a distribution device that receives information on at least a boarding station and a scheduled ride time from a user and distributes information on the congestion rate for each vehicle.   The vehicle congestion rate prediction system according to claim 1, further comprising a transmission device that intermittently transmits the congestion rate for each vehicle toward a display device in a station premises. A method for predicting the congestion rate of each train vehicle,
Receiving the data including the getting-off station read by the automatic ticket checker from the ticket passing through the automatic ticket checker on the entrance side of each station, and counting the number of people getting off at each getting-off station for each getting-off station;
A user who passes through an automatic ticket gate with reference to a storage device that stores statistical data that associates the boarding station of the boarding ticket and the boarding ratio at which the user having the boarding ticket specifying the boarding station gets into each vehicle. Predicting the vehicle on which the vehicle is boarded, and calculating the number of people who get on each vehicle and the number of people who get off based on the prediction;
And a step of calculating the congestion rate for each vehicle by counting the number of persons calculated in the calculation step.   The vehicle congestion according to claim 4, further comprising a step of receiving at least information about a boarding station and a scheduled boarding time from a user's portable terminal, and a step of distributing information about a congestion rate for each vehicle to the portable terminal. Rate prediction method.   The vehicle congestion rate prediction method according to claim 4 or 5, further comprising a step of intermittently transmitting the congestion rate for each vehicle toward a display device in a station premises. A program for a computer that predicts a congestion rate for each vehicle in a train, the computer comprising:
Receiving the data including the getting-off station read by the automatic ticket checker from the ticket passing through the automatic ticket checker on the entrance side of each station, and counting the number of people getting off at each getting-off station for each getting-off station;
A user who passes through an automatic ticket gate with reference to a storage device that stores statistical data that associates the boarding station of the boarding ticket and the boarding ratio at which the user having the boarding ticket specifying the boarding station gets into each vehicle. Predicting the vehicle on which the vehicle is boarded, and calculating the number of people who get on each vehicle and the number of people who get off based on the prediction;
And a step of counting the number of persons calculated in the calculation step and calculating a congestion rate for each vehicle.

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