CN112598177A

CN112598177A - Online passenger flow prediction and simulation system for urban rail transit emergency

Info

Publication number: CN112598177A
Application number: CN202011535098.2A
Authority: CN
Inventors: 白云云; 汪波; 黄建玲; 吴欣然; 陈文�; 吕楠; 胡清梅; 韩庆龙
Original assignee: BEIJING TRANSPORTATION INFORMATION CENTER; Beijing Subway Operation Corp
Current assignee: BEIJING TRANSPORTATION INFORMATION CENTER; Beijing Subway Operation Corp
Priority date: 2020-12-22
Filing date: 2020-12-22
Publication date: 2021-04-02
Anticipated expiration: 2040-12-22
Also published as: CN112598177B

Abstract

The invention discloses an online passenger flow prediction and simulation system for urban rail transit emergency, which comprises a real-time passenger flow monitoring module and an emergency passenger flow prediction module; the real-time passenger flow monitoring module is used for predicting the passenger flow evolution state within the future preset time length under the normal condition and generating a passenger flow index of the urban rail transit real-time-sharing granularity; the passenger flow evolution state obtained through prediction is displayed visually, and real-time passenger flow monitoring is achieved; the emergency passenger flow prediction module is used for adjusting a train operation schedule aiming at an emergency in a road network; predicting the passenger flow distribution condition under the condition of being influenced by the event according to the adjusted schedule; and the information of the affected degree of each terminal station is issued to the passengers. The urban rail transit emergency on-line passenger flow prediction and simulation system can monitor passenger flow on line in real time and automatically adjust train operation when an accident occurs.

Description

Online passenger flow prediction and simulation system for urban rail transit emergency

Technical Field

The invention relates to the technical field of urban rail transit emergency processing, in particular to an online passenger flow prediction and simulation system for urban rail transit emergency.

Background

Under the condition of urban rail transit networked operation, the network structure is more and more complex, the correlation degree of each line is more and more high, the types of emergencies are gradually diversified, the occurrence frequency is increased, the swept range is enlarged, and chain reaction can occur once the emergencies occur.

At present, the existing real-time passenger flow monitoring system cannot accurately reflect the passenger flow distribution state under the condition of an emergency. The passenger flow prediction method in a normal state is more suitable for the condition of small passenger flow fluctuation, such as a four-stage method, a non-centralized model, a neural network and the like, the sudden passenger flow fluctuation is large, and the occurrence probability of the same type of events in the same condition is small, and the applicability is poor; the non-collective model based on behavior analysis has questionnaire investigation as data base and serious manual interference, so that the conventional passenger flow prediction method generally has certain limitation in the emergency passenger flow prediction.

Disclosure of Invention

The invention provides an online passenger flow prediction and simulation system for urban rail transit emergencies, which aims to solve the technical problem of poor applicability under the condition of the emergencies of the existing real-time passenger flow monitoring system.

In order to solve the technical problems, the invention provides the following technical scheme:

an urban rail transit emergency online passenger flow prediction and simulation system comprises a real-time passenger flow monitoring module and an emergency passenger flow prediction module; wherein,

the real-time passenger flow monitoring module is used for predicting the passenger flow evolution state within the future preset time length under the normal condition and generating a passenger flow index of the urban rail transit real-time-sharing granularity; the passenger flow evolution state obtained through prediction is displayed visually, and real-time passenger flow monitoring is achieved;

the emergency passenger flow prediction module comprises an emergency train operation plan adjustment and simulation unit, an emergency passenger flow prediction and simulation unit and an emergency information service unit; wherein,

the emergency train operation plan adjusting and simulating unit is used for adjusting a train operation schedule aiming at an emergency in a road network; the emergency passenger flow prediction and simulation unit is used for predicting passenger flow distribution under the condition of being influenced by an event according to the adjusted train running schedule; the emergency information service unit is used for issuing the influence degree information of each terminal station to passengers.

Further, the passenger flow index includes: station entrance and exit volume, transfer volume, passenger volume and cross section passenger volume.

Further, when no emergency occurs in the road network, the real-time passenger flow monitoring module operates, and the emergency passenger flow prediction module stops; when an emergency occurs in a road network, the emergency passenger flow prediction module starts to operate according to an operation instruction of an operator, and the real-time passenger flow monitoring module stops.

Further, the emergency train operation plan adjusting and simulating unit is specifically configured to:

adjusting the train operation schedule of the incident line by combining the train dispatching adjustment strategy under the condition of the emergency according to the planned train operation schedule data and the emergency information, and performing train operation simulation by using the adjusted operation schedule; inputting the adjusted running schedule into the emergency passenger flow prediction and simulation unit; the emergency information comprises the occurrence time of the emergency, the occurrence position of the emergency, the type of the emergency, the expected duration time of the emergency, the extension time of the emergency, the influence direction of the emergency and the influence degree of train operation; the train operation schedule data includes a train ID, an update date, a line number, a train number, a schedule type, a schedule number, a train direction, a station name, a station number, a station type, an arrival time, a departure time, a vehicle type, a train consist, and a train controller.

Further, the emergency passenger flow prediction and simulation unit is specifically configured to:

and predicting OD passenger flow volume according to the emergency information, historical passenger flow OD data and road network basic data, adopting a preset emergency OD prediction model for the OD of the O/D on the incident line, adopting a preset real-time passenger flow prediction model for the OD of the O/D not on the incident line, distributing the passenger flow according to the updated k short path set, the adjusted train running schedule and the prediction result of the OD passenger flow volume, outputting a related passenger flow index and performing visual early warning.

Further, the emergency information service unit is specifically configured to:

and evaluating the influence degree of each station according to the OD shortest path set under normal and emergency events, the emergency event information and the road network basic data, and distributing the influence degree information from the station to other stations aiming at the group.

Further, if it is determined that the duration of the emergency event changes within the expected duration, the emergency train operation plan adjusting and simulating unit is specifically configured to:

when the duration of the emergency is prolonged, correspondingly readjusting the train running schedule, and increasing the time step of passenger flow prediction according to the prolonged duration of the emergency; and when the duration of the emergency is shortened, reducing the time step of passenger flow prediction according to the shortened duration of the emergency.

Further, when the duration of the emergency is not longer than the preset duration, the emergency passenger flow prediction module still keeps running; and when the preset time length is full after the emergency is finished, the emergency passenger flow prediction module is completely stopped, and the real-time passenger flow monitoring module starts to normally operate.

acquiring the occurrence time of the train emergency, the occurrence position of the emergency, the estimated duration time of the emergency, the extension time of the emergency, the type of the emergency, the influence direction of the emergency, the influence degree of train operation and the train operation interval during the fault period according to the emergency information manually input;

the method comprises the steps that a current planned train operation schedule is used as a basis, a train operation schedule under an emergency is automatically generated by combining running adjustment rules under different emergency conditions, after the train operation schedule is adjusted, if the emergency is judged to be not finished and the duration of the emergency is changed, the time for prolonging the emergency is manually input, and if the duration of the emergency is prolonged, the train operation schedule is automatically adjusted and a new train operation schedule is generated; repeating the judging process until no new emergency prolonged time is input; if the extension time is not input, after the duration time point is defaulted, the emergency is ended;

on the basis of newly generated train operation schedule data, a visualized train operation diagram is generated through a computer simulation technology, and an affected train range is displayed.

Further, the automatically generating the train operation schedule under the emergency based on the current planned train operation schedule and by combining the driving adjustment rules under different emergency conditions includes:

s1, pre-issue impact zone adjustment, comprising:

s11, obtaining the initial train number sequence number of the influence area before the accident according to the train departure time of the initial station of the emergency interval and the initial time of the emergency, and recording the sequence number as

The expression is as follows:

wherein ,ud_i,sIndicates the departure time of the train number i at the station S in the running schedule before adjustment, S_fromLocationThe starting station represents the emergency interval, and the fromTime represents the starting time of the emergency;

s12, obtaining the ending train number sequence number of the prior influence area according to the train departure time and the fault starting time of the current direction starting station, and recording the ending train number sequence number as

The expression is as follows:

wherein ,S₁Representing the origin of the train;

s13, calculating the time length of the prior influence area according to the starting time of the initial train number and the ending train number of the prior influence area at the initial station

wherein ,

a starting train number indicating the area of influence before issue,

an ending train number sequence number representing a pre-issue impact zone;

get the serial number set of the drawing line car as

According to the desired interval t of manual input₁ and Q_beforeDetermining the number of the drawing lines in the influence interval before the accident by the number of the middle cars

Wherein count () represents the number of cars in the set,

the rounding is performed downwards, and if the rounding result is less than 1, the result is 1;

s14, from Q_beforeUniformly extracting N at medium random_beforeThe extracted train number operation line deletes the part after the initial time of the emergency and keeps the part before the initial time of the emergency;

s2, incident area adjustment, comprising:

s21, determining the adjusted initial train number of the accident area

wherein ,

representing the initial train number of the accident area;

s22, obtaining the ending train number sequence number of the accident area according to the train departure time and the ending time of the accident at the current direction starting station

Wherein, toTime represents the end time of the emergency;

s23, calculating the time length of the accident area according to the starting time of the starting train number and the ending train number of the accident area at the starting station

wherein ,

indicating the ending train number of the accident area;

determining the sequence number set of the drawing line vehicle in the emergency area

According to the desired interval t of manual input₁ and Q_faultDetermining the number of drawing lines of the accident area by the number of middle cars

Wherein count () represents the number of cars in the set,

represents rounding down; if the result is less than 1 after the whole is obtained, the result is 1;

s24, from Q_faultUniformly extracting N at medium random_faultThe extracted running line of the train number is stopped from the starting station

The next train runs according to the original train running schedule;

s3, emergency duration adjustment, comprising:

if the estimated duration of the emergency is shortened before the emergency is ended, the adjusted train operation schedule is still kept without modification;

if an input t is received to extend the duration of the emergency event before the end time of the emergency event_{Extension of}Then, based on the adjusted train running schedule, the operations S1 and S2 are performed again, the fromTime of the new round of adjustment is the original toTime, and the toTime of the new round of adjustment is the original toTime + t_{Extension of}The expected train running interval of the new round of adjustment is manually input accident extension train running interval t₂；

If the influence direction of the emergency is unidirectional, performing S1-S3 to adjust the train number in the influence direction of the emergency; if the direction of the impact of the emergency is bidirectional, S1-S3 are executed to respectively adjust the train numbers of the ascending train and the descending train.

The technical scheme provided by the invention has the beneficial effects that at least:

the invention realizes high-precision prediction of the future short-time passenger flow evolution state through the real-time passenger flow monitoring module, and generates a passenger flow index of the urban rail transit with real-time-sharing granularity; the forecasted passenger flow evolution state is displayed visually, and real-time passenger flow monitoring is achieved; the method realizes the adjustment of the train running schedule aiming at the emergency in the road network through an emergency passenger flow prediction module; predicting the passenger flow distribution condition under the condition of being influenced by the event according to the adjusted schedule; and the information of the affected degree of each terminal station is issued to the passengers. Therefore, the passenger flow can be monitored on line in real time, and the running condition of the train can be automatically adjusted when an emergency happens.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a schematic service flow diagram of an online passenger flow prediction and simulation system for urban rail transit emergency events according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a service flow of an emergency passenger flow prediction module according to an embodiment of the present invention;

FIG. 3 is a flow chart of an implementation of an emergency train operation adjustment and simulation process according to an embodiment of the present invention;

FIG. 4 is a flow chart of emergency passenger flow prediction provided by an embodiment of the present invention;

FIG. 5 is a flow chart of the prediction of the OD of the emergency passenger flow according to the embodiment of the present invention;

FIG. 6 is a flow chart of emergency passenger flow simulation according to an embodiment of the present invention;

FIG. 7 is a flowchart of distribution of inducement information for a community, according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

The embodiment provides an online passenger flow prediction and simulation system for urban rail transit emergency, which takes historical card-swiping transaction data, real-time packed data, road network basic data, planned train schedule data, emergency information and the like as data supports, and realizes service functions through model and big data operation, storage exchange and the like among data. The business function module mainly comprises a real-time passenger flow monitoring module and an emergency passenger flow prediction module.

As shown in fig. 1, the real-time passenger flow monitoring module is configured to perform high-precision prediction on a future short-time passenger flow evolution state by using various basic data such as real-time and historical data, generate indexes such as real-time granularity OD passenger flow (OD: origin-destination point of travel, O referring to departure point of travel, D referring to destination of travel), section passenger flow, station entering and exiting amount, and transfer amount of urban rail transit, distribute and perform classification early warning on the predicted passenger flow, and perform visual display on the predicted passenger flow state to monitor the real-time passenger flow. When an emergency occurs, an operator manually switches an interface to the emergency passenger flow prediction module, and inputs emergency information, the real-time passenger flow monitoring module stops running, and the emergency passenger flow prediction module starts running.

The real-time passenger flow monitoring module mainly comprises the following functions:

1) network-level real-time passenger flow monitoring

2) Line-level real-time passenger flow monitoring

3) Zone-level real-time passenger flow monitoring

4) Station level real-time passenger flow monitoring

The emergency passenger flow prediction module is used for firstly adjusting a train operation schedule of an incident line according to emergency information occurring in a road network, respectively predicting OD passenger flows of an O/D (optical to digital) on the incident line and an OD (optical to digital) on a non-incident line, then distributing the passenger flows by applying the adjusted train operation schedule, a k short path set under the emergency and a passenger flow prediction result under the emergency, outputting a related result and carrying out visual early warning display, and simultaneously issuing arrival information to passengers, including the influenced degree information of each terminal station and the like. And when the emergency is finished, the emergency passenger flow prediction module continues to operate, and the k short path set under the normal condition is used for passenger flow distribution. And when the emergency event is finished and 30min is up, the emergency passenger flow prediction module is completely stopped, and the real-time passenger flow monitoring module continues to normally operate. The K short path set is a set of K short paths, which is used for distributing OD passenger flow volumes to specific paths to obtain cross-section passenger flow volumes, wherein the K short path set is more than one path between any two points in a road network and is sorted from small to large according to path impedance.

The emergency passenger flow prediction module mainly comprises the following functions:

1) train operation planning and simulation

2) Passenger flow prediction and simulation

3) Information service

Specifically, as shown in fig. 2, after entering the emergency passenger flow prediction module, the emergency train operation schedule adjustment and simulation unit first adjusts the train operation schedule of the event route according to the planned train schedule and the emergency information, and performs train operation simulation by using the adjusted train operation schedule, which is input into the emergency passenger flow prediction and simulation unit. The emergency passenger flow prediction and simulation unit applies the input emergency information and historical passenger flow OD data and calls road network basic data to predict OD passenger flow volume, adopts an emergency OD prediction model for the OD of the O/D on the incident line, adopts a real-time passenger flow prediction model for the OD of the O/D not on the incident line, and uses a k short path set, an adjusted train schedule and an OD prediction result which are updated according to the emergency information for passenger flow distribution, outputs a related passenger flow index and performs visual early warning. Finally, the emergency information service unit evaluates the affected degree of each station according to the OD shortest path set under normal and emergency, the emergency information and the road network basic data, and distributes the affected degree information from the station to other stations for the group.

If the duration of the accident is judged to be changed within the expected duration, the duration of the accident can be manually prolonged or shortened for a certain time through a system interface, when the duration of the emergency is prolonged, the module can correspondingly readjust the schedule, and correspondingly increase the time step length of passenger flow prediction according to the prolonged duration of the time duration; when the duration of the emergency is shortened, the time schedule does not need to be readjusted, and only the time step length of passenger flow prediction is correspondingly reduced.

When the preset duration (30 min in this embodiment) is not reached after the event duration is over, the emergency passenger flow prediction module still keeps running, and the k short path set for passenger flow distribution is restored to the k short path set in the normal condition. When the preset time length is full after the emergency is finished, the emergency passenger flow prediction module is completely stopped, and the real-time passenger flow monitoring module starts to normally operate.

Next, each functional unit of the emergency passenger flow prediction module in the present embodiment is specifically described.

1. Emergency train operation plan adjusting and simulating unit

The emergency train operation plan adjusting and simulating unit is based on the data of the planned train schedule, according to the information of the emergency (including the time, the place, the reason, the type and the influence degree of the emergency), and by combining with the train dispatching adjustment strategy under the emergency situation, an intelligent man-machine interaction system module for adjusting the train operation plan of the emergency is constructed, so that the automatic adjustment of the train operation schedule of the incident line is realized, and on the basis of the adjustment of the train operation plan of the emergency, a computer simulation technology is utilized to develop a train operation simulation system module under the condition of the visual emergency, a train operation diagram is automatically laid and drawn according to the adjusted train operation schedule, the range of the affected train is displayed, the whole process of the time-space change of the operation of all trains on the accident line in the time period of the emergency is simulated, and auxiliary decision support is provided for the actual operation.

When the emergency information is judged to be abnormal manually, early warning is triggered manually, and related accident information is input to a man-machine interaction system to provide a basis for train operation plan adjustment. The method mainly comprises the following indexes: the time of the emergency, the location of the emergency, the type of the emergency, the expected duration of the emergency, the time of the emergency extension, the direction of the emergency impact, and the train operation impact.

The train operation schedule records information such as train operation lines, arrival and departure times of stations along the train, is a basis for mastering train operation tracks, and provides basic data support for train operation plan adjustment in emergencies. The method mainly comprises the following indexes: train ID, update date, line number, train number, schedule type, schedule number, train direction, station name, station number, station type, arrival time, departure time, vehicle type, train consist, and train conductor.

1.1 workflow description

Based on the above, the working flow of the emergency train operation plan adjusting and simulating unit of the embodiment is shown in fig. 3, and specifically includes the following steps:

1) manual input of incident information

Firstly, according to the manually input emergency information, the occurrence time of the train emergency, the occurrence position of the emergency, the estimated duration, the extended time of the emergency, the type of the emergency, the influence direction, the train operation influence and the train operation interval during the fault are obtained, and the description of each index is as follows:

the time of occurrence of the emergency refers to the time of occurrence of the fault which is manually judged and input.

The emergency position refers to a fault occurrence place which is manually judged and input, the emergency position is a station or an interval, and if the type of the station is selected, the accident station of an accident route is input; if the section type is selected, an event section of the event route is input.

The emergency type refers to the type of the sudden equipment and facility faults, including signal faults, vehicle faults, shield door faults, foreign object invasion and the like.

The expected duration refers to the time length for which the possibility of the emergency lasting from the occurrence to the end is manually judged.

The emergency extension time refers to the predicted change time of the accident which is manually input if the accident duration is judged to be changed manually within the predicted duration time. The accident duration can be prolonged and shortened according to the actual situation. The first time an emergency is triggered this item defaults to 0.

The influence direction refers to the influence of an emergency on the uplink and the downlink of an incident line, and may be the uplink, the downlink or the uplink and the downlink.

The train operation influence refers to the influence of an emergency on the train operation, and comprises the stop operation of a fault section, the degraded operation of the fault section and the degraded operation of a fault train.

The train running interval during the event refers to the minimum allowable train tracking time within the event time judged manually.

2) Initial calculation of train operation plan adjustment

After the indexes are obtained, the train operation time table under the emergency is automatically generated and written into a database according to measures such as train locking, turning back, stopping and starting by taking the current planned train time table as the basis and combining the driving adjustment rules under different emergency situations. The response is required to be completed within 30s from the early warning of the artificial triggering emergency to the generation of the adjusted train operation schedule data.

3) Train operation plan adjustment correction

After the train operation plan is adjusted for the first time, the train operation plan correction process needs to be carried out, and the flow is as follows:

after the train operation plan is adjusted, if the accident is judged to be not finished manually and the accident duration is changed, the accident duration is input manually, and if the accident duration is prolonged, the system automatically adjusts and generates a new train operation schedule; if the accident duration is shortened, the timetable does not need to be readjusted. The system repeats the judging process until no new emergency prolonged time is input. If the extension time is not input, the accident is ended after the duration time point is defaulted.

4) Train operation plan adjustment visualization

And finally, generating a visual train operation diagram by a computer simulation technology on the basis of the newly generated train operation schedule data, and displaying the range of the affected train.

1.2 model Algorithm description

The algorithm for adjusting the operation train operation plan of the emergency train according to the emergency information manually input by the emergency train operation plan adjusting and simulating unit and by combining the original train operation schedule is specifically as follows:

the input of the algorithm is train operation schedule data and emergency information data, and the output is an adjusted train schedule. And the adjusted new train running schedule data is provided to the emergency passenger flow prediction module as input in an interface form and is synchronously stored in a database.

Specifically, the train operation schedule data includes ID, update date, line number, train number, schedule type, schedule number, direction, station name, station number, station type, arrival time, departure time, vehicle type, train consist, and train controller. The emergency information data comprises the occurrence time of the emergency, the expected duration, the occurrence position of the emergency and the influence direction. The time of the emergency event needs to be manually input, the time of the event is manually input, the accuracy is minute, and the system calculates the event time point by taking a node 15min closest to the event time point according to the principle of 'rounding up nearby'. The expected duration needs to be manually input, and is usually subject to the time length of the event published by the track operation enterprise. The position of the emergency event needs to be manually input, the emergency event is generally divided into a station or a section, and if the emergency event is a station, the name of the station is input; if the field is the field, the field is required to be input into the field published by the track operation enterprise.

Based on the above, the calculation process of the algorithm model is as follows:

first, for convenience of explanation, variables involved in the following calculation processes are explained as shown in table 1.

TABLE 1 operating schedule adjustment variables description Table

If the accident influence direction is one-way, the train number in the direction is adjusted according to the following steps. If the accident influence direction is bidirectional, the train numbers of the ascending train and the descending train are respectively adjusted according to the following steps of (i) - (iii). The concrete steps are shown in the formula I-III.

Firstly, the influence area is adjusted before the issue

a. A starting train number for the pre-issue impact zone adjustment is determined.

Obtaining the initial train number sequence number of the influence area before the accident according to the train departure time and the fault initial time of the initial station of the fault section, and recording the sequence number as

b. Determining an ending train number for a pre-issue impact zone adjustment

Obtaining the ending train number sequence number of the prior influence area according to the train departure time and the fault starting time of the direction starting station, and recording the ending train number sequence number as

c. Drawing wire

The time length of the prior influence area is the time difference between the starting time and the starting time of the ending train number of the prior influence area at the starting station,

the serial numbers of the drawing lines are collected as

In order to make the adjusted train number in the influence area before the departure run according to the expected interval manually input and simultaneously reduce the influence on the driven train number as much as possible, the running plan of the station where the train is located and the subsequent station when some train numbers are at the fault starting time is cancelled, and the part of the running line of the train number after the fault starting time is deleted on the running chart, so that the running interval of the train after the line drawing is close to t₁. According to the desired interval t of manual input₁ and Q_beforeThe number of the middle bus times determines the number of the drawing lines in the influence interval before the accident. Number of drawn lines in pre-issue influence area

count () represents the number of cars in the set,

indicating a rounding down. If the result is less than 1 after the rounding, the result is 1.

Example (c): t is₁＝6min，t₁＝5min，count(Q_before) When 2, then

d. According to the number N of the drawn wires_beforeDrawing wire

From Q_beforeUniformly extracting N at medium random_beforeAnd the extracted train number operation line deletes the part after the fault starting time and retains the part before the fault starting time.

Adjustment of accident area

a. Determining an adjusted starting train number for an event zone

Initial train number for event zone adjustment

b. Determining an adjusted ending train number for an event zone

Obtaining the ending train number of the accident area according to the train exit time and the fault ending time of the direction starting station, and recording the ending train number as

c. Drawing wire

The time length of the accident area is the time difference between the starting time and the ending time of the accident area at the starting station,

number set of drawing-line train in accident area

In order to make the adjusted train numbers in the accident area run at the expected interval, some train numbers are stopped from the starting station in the direction, the running line represented by the train number on the running chart is deleted, and the running interval of the train after line drawing is close to t₁. According to the desired interval t of manual input₁ and Q_faultMiddle vehicleThe number of times determines the number of the drawing lines of the accident area. Number of drawing lines in hair area

count () represents the number of cars in the set,

Example (c): t is₂＝30min，t₁＝5min，count(Q_fault) When the value is 6, then

d. According to the number N of the drawn wires_faultDrawing wire

From Q_faultUniformly extracting N at medium random_faultThe extracted running line of the train number is stopped from the starting station

And the subsequent train number runs according to the original train schedule.

Adjusting duration of accident

If the expected duration of the accident is shortened before the end of the fault, the adjusted schedule is maintained without modification.

If the system receives the input of prolonging the accident duration before the fault ending time, the operation of the first step and the second step is carried out again on the basis of the adjusted time schedule, the fromTime of the new adjustment is the original ToTime, and the ToTime of the new adjustment is the original ToTime + t_{Extension of}The expected train running interval of the new round of adjustment is manually input accident extension train running interval t₂。

2. Emergency passenger flow prediction and simulation unit

The passenger flow prediction of the emergency is based on AFC (automatic fare collection system) passenger flow data under historical emergency, OD (origin-destination) prediction and distribution are carried out based on the occurrence time of the emergency and the passenger flow rule within 30min after the occurrence time of the emergency, the passenger flow prediction under the emergency is realized through a passenger flow OD prediction and distribution model under the emergency, the indexes of time granularity OD passenger flow, section passenger flow, interval full load rate, station entrance and exit quantity, transfer quantity, OD shortest path and the like in the time range of the rail transit within 30min after the occurrence time of the emergency are generated, the influenced range including influenced stations, influenced number of people and influenced time is calculated, and data support and basis are provided for passenger flow early warning, information service and passenger flow induction measures under the emergency.

The historical passenger flow OD data mainly comprise four parts, namely a starting station, a terminal station, OD travel starting time and OD travel passenger flow. The road network basic data mainly comprises four parts, namely station-to-line matching, station spacing, OD trip shortest circuit and line classification. And a basic basis is provided for the calculation of the OD passenger flow prediction space parameters after the emergency occurs. The emergency information provides a basic basis for calculating OD passenger flow prediction time parameters after the emergency occurs, and comprises the following indexes: time of occurrence of an emergency, location of occurrence of an emergency, expected duration, time of extension of an emergency. The output result of the train operation plan adjusting and simulating unit is a train operation schedule adjusted after being influenced by an emergency, and the unit obtains the train operation schedule in an interface mode to provide a basic basis for passenger flow distribution and corresponding index calculation.

2.1 workflow description

Based on the above, the work flow of the emergency passenger flow prediction and simulation unit of the embodiment is shown in fig. 4, and specifically includes the following steps:

1) emergency OD traffic prediction period specification

Considering that the emergency has a long-term influence on the passenger flow, the time period for predicting the passenger flow of the emergency OD is defined as the time length of the occurrence of the emergency and 30min after the end of the emergency.

2) Emergency passenger flow prediction process

Based on a relevant emergency passenger flow prediction model and method, passenger flow OD data are predicted by combining passenger flow data of historical emergency. Since the emergency has a continuous influence on the passenger flow, the OD is predicted for the time when the emergency occurs and for the passenger flow 30min after the end of the emergency. And for the case that the O/D is on the incident line, adopting a passenger flow OD prediction model under an emergency to predict the OD, and for the case that the O/D is not on the incident line, adopting a real-time passenger flow OD prediction model to predict the OD. And (4) distributing the predicted road network OD by utilizing a passenger flow distribution algorithm and calculating each index by combining the adjusted train operation schedule.

(1) Emergency passenger flow OD prediction process

Aiming at passenger flow prediction of O/D on an incident line, firstly, calculating time parameters and space parameters of a prediction model by using incident information and road network basic data, and inputting historical OD passenger flow data into an incident passenger flow OD prediction model to perform OD passenger flow prediction; and aiming at the situation that the O/D is not predicted on the event line, adopting a passenger flow prediction model under a real-time passenger flow prediction module to predict. The predicted output is OD traffic at 15min granularity.

The duration of the accident can be adjusted in the prediction process, the accident duration can be prolonged/reduced according to the actual situation, and the model correspondingly reduces or increases the prediction step length after the accident duration is adjusted. Whether the accident is ended or not needs to be judged in the prediction process, and prediction is continued for 30min after the accident is ended, so that whether prediction is ended or not is judged. The flow of predicting the emergency passenger flow OD is shown in fig. 5, and the specific flow is as follows:

input of information of emergency

The required emergency information data includes the following indexes: time of occurrence of an emergency, location of occurrence of an emergency, expected duration, time of extension of an emergency.

The time of the occurrence of the emergency event needs manual input, and the time of the occurrence of the event is manually input, and the accuracy is minute.

Second, calling the basic data of road network

The road network basic data mainly comprises four parts, namely station-to-line matching, station spacing, OD trip shortest circuit and line classification. And a basic basis is provided for the calculation of the OD passenger flow prediction space parameters after the emergency occurs.

Inputting historical emergency OD passenger flow data

The historical passenger flow OD data mainly comprise four parts, namely a starting station, a terminal station, OD trip starting time and OD trip passenger flow.

OD passenger flow prediction process

After inputting historical emergency OD passenger flow data, road network basic data and emergency information, the model calculates time parameters according to the emergency occurrence time, the estimated duration time and the emergency extension time; and calculating space parameters according to the position of the emergency, the matching of the station and the line, the distance between stations, the shortest route of OD (origin-destination) travel and line classification. Adopting a passenger flow OD prediction model under an emergency for passenger flow OD prediction of O/D on an incident line; and adopting a real-time passenger flow prediction model for predicting the passenger flow OD of the O/D not on the incident line.

Fifthly, correcting the predicted time length of OD passenger flow

And manually judging whether the duration of the emergency and the OD passenger flow prediction time length need to be adjusted. If the time length is prolonged, an OD passenger flow result with increased time length needs to be correspondingly output; if the time is shortened, the accident end time plus the next 30min is predicted.

Sixthly, judging the end of the event.

After the system predicts new OD passenger flow data with the granularity of 15min, whether the event is finished and whether the prediction time length reaches the time length of the occurrence of the emergency and is added for 30min after the completion is judged, and if the demand is not met, the prediction is continued; and if the requirement is met, exiting the prediction.

(2) Emergency passenger flow simulation process

The emergency passenger flow simulation firstly needs to input predicted OD passenger flow data, and meanwhile utilizes a k short path set under the emergency generated after the train running schedule is adjusted to calculate the path impedance to distribute the passenger flow, but aiming at the OD in the road network when the event occurs, a normal k short path set before change is adopted to distribute the passenger flow. The passenger flow simulation comprises two aspects of output, on one hand, the calculation of various passenger flow indexes, the arrival amount, the departure amount, the transfer amount, the section passenger flow and the interval full load rate under the granularity of 5 min. Obtaining an OD shortest path in a passenger flow distribution process; on the other hand, the affected area is calculated, which is affected station, affected number of people, and affected time. The emergency passenger flow simulation process is shown in fig. 6, and the specific process is as follows:

input of parameters

And inputting the OD passenger flow data obtained by prediction in the previous step, inputting the adjusted train operation planning schedule, updating the impedance according to the time information of the adjusted train operation planning chart, and generating and inputting a k short path set under the emergency.

Generation of index

Passenger flow distribution index: and calculating the passenger flow simulation result to obtain the arrival volume, the departure volume, the transfer volume, the section passenger flow volume and the interval full load rate under the granularity of 5 min.

Other indexes are as follows: and obtaining the shortest path of the OD in the passenger flow distribution process.

Affected area: the calculation of the affected range comprises three parts, namely an affected station, the number of affected persons and the affected time.

Regulating predicted step length

If the accident is not finished and the accident duration is artificially judged to be changed, the accident duration can be prolonged or shortened. When the accident duration is prolonged, the train running schedule needs to be readjusted, the OD passenger flow prediction step length is correspondingly increased, and passenger flow distribution is carried out by utilizing the readjusted schedule, the OD passenger flow prediction amount after the step length is increased and the k short path set under the emergency; when the accident duration is shortened, the schedule does not need to be adjusted again, the OD prediction step length is shortened, and the OD prediction result is input into the passenger flow distribution.

Module stopping

And when the emergency is ended and the time is less than 30min after the emergency is ended, the k short path set of the passenger flow distribution application is recovered to the k short path set under the normal condition, and the emergency passenger flow prediction and simulation module keeps running. And when the time is 30min after the emergency is finished, the emergency passenger flow predicting and simulating module stops running.

2.2 model algorithm description:

the emergency passenger flow prediction and simulation module mainly comprises two important links: OD passenger flow prediction and passenger flow distribution, wherein in the two links, a passenger flow OD prediction model is applied to OD passenger flow prediction, the passenger flow distribution link needs to firstly update the impedance of each path of a k short path set under normal conditions, and then the model is applied to carry out passenger flow distribution on each path of passenger flow, and the models mainly applied in the link are a k short path set generation model and a passenger flow distribution model.

1) Passenger flow OD prediction model

(1) Model input

The input parameters of the passenger flow OD prediction model are time parameters and space parameters. The time parameters include: the event sending time point, the estimated duration, the event duration influence time and the event influence parameter. The spatial parameters include: the incident location, the shortest distance between the O/D and the incident point, the shortest distance between the OD trips and the space influence parameters.

Time parameter

a. The incident time point is as follows: the incident time point needs manual input, the time of the incident is manually input, the precision is minute, and the system calculates the incident time point by taking the 15min node closest to the incident time point according to the principle of 'rounding up nearby'.

b. The estimated time length is as follows: the estimated occurrence time of the event needs manual input, and the manual input time of a dispatcher is taken as the standard.

c. Duration of event impact time

The duration of the impact event may take a fixed default length of time, typically a default of 30 minutes.

d. Time influencing parameter

And the system automatically adjusts the system to the nearest 15min node according to the manually input event occurrence time, and then the point is converted into system time data which is Arabic numeral 1. And in the estimated incident duration and the default duration influence time, sequentially marking time data by Arabic numerals every 15min to obtain time influence parameters.

② spatial parameters

a. The incident place: the incident place needs manual input, and is generally divided into a station or a section, and if the incident place is a station, the name of the station is input; if the input is a section, an event section needs to be input.

b.O/D distance from the event point shortest distance: the system calculates the shortest distance between O or D on the incident route and the incident station or section according to the input incident point.

Od shortest path: the OD trip shortest path is a quantity which needs to be led into a system in advance, and is generally unchanged, and when a newly added line or station occurs and an emergency occurs, corresponding updating needs to be performed.

d. Spatial impact parameters: and (4) according to the basic data link space distance data, calculating the distance between the O or D and the incident station or the section and the OD shortest distance to obtain space influence parameters.

(2) Model calculation

in the formula ,

time period-j, the spatial influence parameter being

The percentage of deviation of the OD traffic from the normal traffic mean;

j-time influence parameter;

-a spatial impact parameter between the origin and the destination od during a period of-j;

t_e-an estimated duration of the event;

t_ed-sum of the accident prediction duration and the duration impact time;

a, B, C, D, E, F, J, H, I, J, K-model parameters.

(3) Model output

The model outputs OD passenger flow deviation percentage, and the OD passenger flow predicted value under the emergency can be calculated according to the following formula by means of the deviation percentage.

in the formula ,V_od-an OD passenger flow prediction value in case of an emergency;

-mean OD passenger flow at normal state;

time period-j, the spatial influence parameter being

Is a percentage of deviation from the normal mean of traffic.

2) k short path set updating model

(1) Model input

And updating the impedance of the k short path set under the normal condition by utilizing the train running interval according to the position of the occurrence of the emergency and reordering according to the updating result.

Location of occurrence of the emergency: the emergency position input manually is a station or an interval where the emergency occurs.

Train operating interval during the event: and (4) the minimum allowable train tracking time in the manually judged incident time.

K short path sets under normal conditions: under the condition of no emergency, the system calculates impedance according to an algorithm to obtain a k short path set among all ODs.

(2) Model calculation

In the formula, R (i, j) — impedance on a path R taking an i station as a starting point and a j station as an end point under an emergency; r₀(i, j) -impedance on a path r with i station as a starting point and j station as an end point under normal conditions;

h is train running interval during the accident;

d, path set affected by the event.

According to the above equation, only the path affected by the emergency is updated in impedance, and the unaffected path impedance is kept consistent with the path impedance in the normal case.

(3) Model output

And outputting each path impedance by the model, and reordering according to the calculated path impedance and the calculated impedance to obtain the k short path set.

3) Passenger flow distribution model

(1) Model input

The model inputs are the predicted OD passenger flow volume, the k short path set and the train running schedule obtained by adjustment.

Predicting OD passenger flow: and (4) outputting a prediction result in a passenger flow OD prediction link.

K short path set: and k short path set generating a model output result and corresponding impedance of each path.

Adjusting the obtained train schedule: and adjusting and simulating the train operation schedule under the emergency output by the emergency train operation plan.

(2) Model calculation

A logit model is used to represent the probability that a path is selected at the corresponding impedance, as follows:

in the formula ,C_m,i-a cost function of factors influencing passenger routing;

β_m,i-the weight of the factor;

P_i-the probability that a path i in the set of paths is selected,

the method comprises the following steps of constructing a multipath probability distribution model, synthesizing the characteristics of a deterministic path distribution model and a random multipath distribution model, and constructing the multipath probability distribution model based on user balance, wherein the method comprises the following steps:

wherein, θ: the randomness of the model is described.

(3) Model output

And (4) obtaining the passenger flow on each path according to the probability of each selected path in the path set and the probability distribution model of the multipath paths.

2.3 outcome of results

1) Predicting the result of the emergency OD passenger flow: and adding the occurrence time of the emergency and the granularity OD passenger flow data of 15min within 30min after the occurrence time of the emergency.

2) Passenger flow indexes obtained by passenger flow simulation of an emergency: the section passenger flow indexes comprise section passenger flow with 5min granularity and section full load rate; the station passenger flow indexes comprise 5min granularity station entering amount, station exiting amount and transfer amount.

(1) Section passenger flow volume: the number of passengers passing through a certain section of the subway line along the same direction in a certain time.

(2) Interval full load rate: and in unit time, the ratio of the passenger flow volume of the one-way section of the operation line to the transport capacity of the corresponding section reflects the train congestion condition of the section in unit time of the train. The calculation method comprises the following steps:

(3) station arrival amount: and calculating the number of passengers entering the station according to the passenger flow OD data obtained by prediction.

(4) Station outbound amount: the number of passengers coming out of the station is calculated from the predicted passenger flow OD data.

(5) Transfer station transfer amount: and in the counting period, the number of passengers transferred in each direction among lines of the transfer station is counted.

3) Affected range obtained by emergency passenger flow simulation

The calculation of the affected range comprises three parts, namely an affected station, the number of affected persons and the affected time.

Affected stations are all stations included in the emergency line.

The number of affected people is the sum of the quantity of the business trip and the quantity of the business trip line in the same period of the history of the business trip period.

The affected time is the time length of the emergency and 30min after the end of the emergency.

4) And (4) outputting: OD shortest path for emergency scenarios.

3. Emergency information service unit

The emergency information service is based on road network basic data, basic parameters and emergency information, an emergency passenger flow prediction module is used as a basic data source, and based on road network changes in different emergency scenes, an OD shortest path set under a normal condition is compared with an OD shortest path set after the emergency occurs, OD trip time under the normal condition is compared with OD trip time after the emergency occurs, and whether trip time to other destination stations is changed or not is judged for each station. The method comprises the steps of issuing information of the influenced degree of paths among all ODs marked by red and green colors facing passenger groups, and simultaneously storing individualized path guidance information aiming at individual passengers, namely recommended path information among input origin-destination points of the passengers. And data support is provided for the passenger query system and the related function of the mobile client road network operation information query.

The road network basic data mainly comprises four parts, namely station-to-line matching, station spacing, OD trip shortest circuit and line classification. The road network basic data is one of important basic data for inducing information formulation. The incident information data is used to present the affected time period. The method comprises the following indexes: the time of occurrence of the emergency, the location of occurrence of the emergency, the type of emergency, the expected duration of the emergency, the time of extension of the emergency, the direction of impact, and the train operation impact. And managing OD shortest path set and OD travel time under normal conditions. And the OD shortest path set and the OD travel time under the normal condition are data tables for recording the shortest path from each station to each arriving station and the corresponding travel time under the normal condition without the occurrence of emergency. And the method is used for comparing the OD shortest path data set with the OD travel time under the emergency condition and formulating the induction information. Under normal conditions, the OD shortest path set and the OD travel time include the following indexes: the number of the starting station, the number of the final station, the shortest path and the travel time. And managing an OD shortest path set and OD trip time after the emergency occurs. And the OD shortest path set and the OD trip time after the emergency occurs are data tables which record the shortest path from each station to each reachable station and the corresponding trip time under the condition that the road network topological structure is changed when the emergency occurs. Data is obtained from the passenger flow prediction module. After an emergency occurs, indexes included in an OD shortest path set and OD trip time are as follows: the number of the starting station, the number of the final station, the shortest path and the travel time. The emergency information data is input into a man-machine interaction system when an alarm is triggered manually; and after the emergency occurs, the OD shortest path set and the OD trip time are obtained from the passenger flow prediction module, and compared with the OD shortest path set and the OD trip time which are stored in the system under the normal condition so as to evaluate the influence degree of the path between the ODs.

3.1 workflow description

Based on the above, the work flow of the emergency information service unit of this embodiment is as follows:

1) information distribution period provision: after the emergency occurs, information publishing is started until the emergency is finished and all stations recover to a 'no influence' state, and the information publishing is finished.

2) Information service flow management: when an emergency occurs, the information issuing module is started. The process of distributing the inducement information for the group is shown in fig. 7. The information service comprises two aspects: and generating influence degree information from the station to other stations of the group and storing personalized passenger path guidance information for individuals.

(1) And generating influence degree information from the station to other stations of the group: and comparing the OD shortest path set and the OD trip time after the occurrence of the emergency with the normal condition according to the input event information, the OD shortest path set and the OD trip time data after the occurrence of the emergency, the road network basic data called by the module, and the OD shortest path set and the OD trip time under the normal condition.

Judging whether the shortest path and the travel time between the ODs change under a normal condition and after an emergency, if not, the final station is not influenced by the accident; if the shortest path or travel time length changes, the terminal station is affected by an emergency.

The link outputs the influence degree information from the station to other stations aiming at the group. This information can be published through the in-station PIS system, mobile clients, and passenger query systems.

(2) Passenger-personalized route guidance information preservation for individuals: passengers input the starting station and the terminal station according to personal requirements, the system selects the shortest paths among corresponding OD from the OD shortest paths after the emergency occurs, and stores the shortest paths, and the passenger personalized path guidance information for the individual passengers is not displayed visually.

3.2 result output

The module only outputs the influence degree information from the station to other stations aiming at the group.

1) The influence degree information from the station to other stations of the group is a data table for recording the influence degree between the ODs, and the specific indexes comprise a time stamp, a starting station number, a final station number, an influence grade and a rendering color.

(1) Time stamping: and recording the time stamp when the affected degree information of each station is updated.

(2) The number of the start station: when the degree of influence is evaluated, the station number is used as a starting point.

(3) And numbering the final station: when the degree of influence was evaluated, the station number was used as the end point.

(4) The affected grade: the evaluation of the degree of influence of an emergency between certain ODs is divided into two stages: unaffected and affected. The specific evaluation process is described in the above section.

(5) Color rendering: the color rendering according to the affected degree level is shown in table 2.

TABLE 2 station affected level and color rendering significance

In summary, the online passenger flow prediction and simulation system for the urban rail transit emergency according to the embodiment realizes high-precision prediction of the passenger flow evolution state in the future in a short time under normal conditions through the real-time passenger flow monitoring module, and generates a passenger flow index of the urban rail transit with real-time granularity; the passenger flow evolution state obtained through prediction is displayed visually, and real-time passenger flow monitoring is achieved; the method realizes the adjustment of the train running schedule aiming at the emergency information in the road network through an emergency passenger flow prediction module; predicting the passenger flow distribution condition under the condition of being influenced by the event according to the adjusted schedule; and the information of the affected degree of each terminal station is issued to the passengers. Therefore, the passenger flow can be monitored on line in real time under normal conditions and emergency conditions.

Furthermore, it should be noted that the present invention may be provided as a method, apparatus or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied in the medium.

Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It should also be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.

Finally, it should be noted that while the above describes a preferred embodiment of the invention, it will be appreciated by those skilled in the art that, once the basic inventive concepts have been learned, numerous changes and modifications may be made without departing from the principles of the invention, which shall be deemed to be within the scope of the invention. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.

Claims

1. An urban rail transit emergency online passenger flow prediction and simulation system is characterized by comprising a real-time passenger flow monitoring module and an emergency passenger flow prediction module; wherein,

2. The system of claim 1, wherein the passenger flow indicators comprise: station entrance and exit volume, transfer volume, passenger volume and cross section passenger volume.

3. The on-line passenger flow prediction and simulation system for urban rail transit emergency according to claim 1, wherein when no emergency occurs in the road network, the real-time passenger flow monitoring module operates, and the emergency passenger flow prediction module stops; when an emergency occurs in a road network, the emergency passenger flow prediction module starts to operate according to an operation instruction of an operator, and the real-time passenger flow monitoring module stops.

4. The online passenger flow prediction and simulation system for the urban rail transit emergency, according to claim 1, wherein the emergency train operation plan adjusting and simulation unit is specifically configured to:

5. The urban rail transit emergency online passenger flow prediction and simulation system according to claim 4, wherein the emergency passenger flow prediction and simulation unit is specifically configured to:

6. The system of claim 5, wherein the emergency information service unit is specifically configured to:

7. The system of claim 6, wherein if the duration of the emergency is determined to change within the expected duration, the emergency train operation plan adjusting and simulating unit is specifically configured to:

8. The on-line passenger flow prediction and simulation system for urban rail transit emergency according to claim 7, wherein the emergency passenger flow prediction module still keeps running when the preset duration is not met after the duration of the emergency is over; and when the preset time length is full after the emergency is finished, the emergency passenger flow prediction module is completely stopped, and the real-time passenger flow monitoring module starts to normally operate.

9. The on-line passenger flow prediction and simulation system for urban rail transit emergency, according to claim 8, wherein the emergency train operation plan adjusting and simulation unit is specifically configured to:

10. The system of claim 9, wherein the system for predicting and simulating online passenger flow during an emergency of urban rail transit is configured to automatically generate a train operation schedule during the emergency based on a current planned train operation schedule and by combining with driving adjustment rules under different emergency conditions, and comprises:

s1, pre-issue impact zone adjustment, comprising:

The expression is as follows:

The expression is as follows:

wherein ,S₁Representing the origin of the train;

wherein ,

a starting train number indicating the area of influence before issue,

an ending train number sequence number representing a pre-issue impact zone;

get the serial number set of the drawing line car as

According to the desired interval t of manual input₁ and Q_beforeDetermining the number of times of the Chinese vehicleNumber of line drawing in sound zone