CN114723240A - Railway passenger transport comprehensive transportation hub connection mode cooperative scheduling method and system - Google Patents

Railway passenger transport comprehensive transportation hub connection mode cooperative scheduling method and system Download PDF

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CN114723240A
CN114723240A CN202210281027.7A CN202210281027A CN114723240A CN 114723240 A CN114723240 A CN 114723240A CN 202210281027 A CN202210281027 A CN 202210281027A CN 114723240 A CN114723240 A CN 114723240A
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陈坚
陈桥
马新露
沈维平
李为为
秦正
蒋山
刘罗汉
张晓庆
易彤
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Chongqing Jiaotong University
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Abstract

The invention discloses a method and a system for cooperatively scheduling a connection mode of a comprehensive transportation hub for railway passenger transport, wherein the system comprises an acquisition module, a statistical module and a scheduling module, wherein the acquisition module acquires the operation information of the transportation hub and the transfer information of passengers arriving at a station; the statistical module is used for counting transfer information of all passengers arriving at the station and determining the initial sharing rate of each connection mode; and the scheduling module iteratively solves the optimal scheduling parameter which enables the total transfer time of the arriving passengers to be the minimum through a double-layer planning model according to the operation information and the initial sharing rate of all the connection modes. By adopting the method and the system for the connection mode collaborative scheduling of the comprehensive transportation hub for the passenger train, the connection mode selection behavior, the passenger transfer time, the schedule of each connection mode and the service rate of passengers can be synthesized, and the collaborative scheduling is realized in the connection mode, so that the connection resource allocation of the hub is optimized, the operation efficiency of the hub is improved, and safe, quick and comfortable transfer service is provided for the passengers.

Description

Railway passenger transport comprehensive transportation hub connection mode cooperative scheduling method and system
Technical Field
The invention relates to the technical field of management optimization of a transportation hub connection mode, in particular to a method and a system for cooperatively scheduling a railway passenger transport comprehensive transportation hub connection mode.
Background
As a connection point of a modern high-speed rail network and urban traffic, the railway comprehensive passenger transport hub provides transport service for urban economic development and citizen travel, has various internal transportation modes and various passenger flow groups, and evacuates passenger flows to basic targets of which the respective destinations are old-term railway passenger transport hubs; and the final purpose of the railway hub in the new period is to continuously improve the service quality of the trip of the hub, improve the trip quality of the masses and enhance the trip satisfaction of passengers.
Under the influence of the arrival time of a main railway train, the arrival passenger flow of the railway usually presents a short-time high aggregation characteristic, and great impact is caused to each transfer traffic mode. When each connection mode of the junction cannot be matched with pulse type arrival passenger flow, the phenomena of passenger detention, missing boarding and the like can be caused, the transfer efficiency is reduced, and the safety of passengers is endangered in serious cases.
In the transfer process of the passenger flow arriving at the railway in the junction, unbalanced states are presented along with the changes of time and space, the phenomena of queuing and crowding of passengers at a certain connection mode platform often occur in the junction, and the phenomenon of 'vehicles and other people' occurs at other connection mode platforms. The local congestion and the waste of transportation resources caused by different collecting and distributing capacities in different directions or unbalanced passenger flow distribution affect the passenger transfer efficiency. How to coordinate each connection mode of the junction, optimize the departure interval of the connection mode, realize the rapid evacuation of the arriving passenger flow of the railway, and ensure the normal order of each service in the junction is very important.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method and a system for cooperatively scheduling connection modes of a comprehensive transportation hub for passenger trains, which can cooperatively schedule each connection mode of the hub, optimize departure intervals of the connection modes, realize rapid evacuation of passenger flows arriving at the railway and ensure the normal order of each service in the hub. The specific technical scheme is as follows:
in a first aspect, a method for cooperatively scheduling a connection mode of a railway passenger transport integrated transportation hub is provided, which comprises the following steps:
collecting operation information of a traffic hub and transfer information of passengers arriving at a station;
counting transfer information of all arriving passengers, and determining the initial sharing rate of each connection mode;
according to the operation information and the initial sharing rate of all the connection modes, carrying out iterative solution on the double-layer planning model to obtain optimal scheduling parameters corresponding to different connection modes;
the upper layer model of the double-layer planning model is a connection time model taking the minimum connection time as a target function, and the lower layer model is a connection selection model.
With reference to the first aspect, in a first implementable manner of the first aspect, the iteratively solving by the double-layer planning model includes:
based on constraint conditions, solving the connection time model according to corresponding initial sharing rate to obtain optimized scheduling parameters of different connection modes;
according to the optimized scheduling parameters of different docking modes, calculating the selection probability of the arriving passengers for different docking modes through a docking selection model;
determining the sharing rate corresponding to different connection modes according to the corresponding selection probability;
updating the sharing rates of different connection modes, solving the connection time model again, and calculating the corresponding sharing rates of the different connection modes through the connection selection model again;
and repeating the steps until the optimal scheduling parameters corresponding to different connection modes are obtained.
With reference to the first implementable manner of the first aspect, in a second implementable manner of the first aspect, the constraint condition includes: the minimum departure interval and the maximum departure interval corresponding to the buses and the tracks, the bus departure sequence and the track departure sequence, the departure time in the connection mode is later than the arrival time of the railway train, and the service intensity of the taxis is optimized.
With reference to the first implementable manner of the first aspect, in a third implementable manner of the first aspect, the docking selection model includes:
Figure BDA0003557002270000021
Figure BDA0003557002270000031
wherein, PinFor the probability of selection of passenger n for the ith docking mode, αiConstant term, β, for the docking mode iikCalibration coefficients, x, for different characteristic variables corresponding to the docking mode iiknSelecting characteristic variables of the hub connection mode i for arriving passengers, AnIs the set of each connection mode.
With reference to the third implementable manner of the first aspect, in a fourth implementable manner of the first aspect, the method includes:
collecting characteristic variables of passengers corresponding to different connection modes, and determining calibration coefficients beta of the characteristic variables corresponding to the different connection modes through a maximum likelihood estimation methodik。
With reference to the third implementable manner of the first aspect, in a fifth implementable manner of the first aspect, the characteristic variable includes: the sex, age, occupation, monthly income, distance traveled, purpose of travel, and docking time of the traveler.
With reference to the first implementable manner of the first aspect, in a sixth implementable manner of the first aspect, the allocation rates corresponding to different connection manners are obtained by performing centralized analysis on the selection probabilities of the different connection manners.
With reference to the first aspect, in a seventh implementation manner of the first aspect, the connection time model includes at least one of a track connection time model, a bus connection time model, a taxi connection time model, and a private car connection time model.
With reference to the seventh implementable manner of the first aspect, in an eighth implementable manner of the first aspect, in the rail connection time model and the bus connection time model, a probability distribution function obeyed by the passenger walking time is:
Figure BDA0003557002270000032
wherein, alpha, eta and gamma are distribution parameters.
With reference to the eighth implementable manner of the first aspect, in a ninth implementable manner of the first aspect, the distribution parameter is calibrated by performing maximum likelihood estimation on all collected passenger transfer information.
In a second aspect, a connection type cooperative dispatching system of a railway passenger transport integrated transportation hub is provided, which comprises:
the acquisition module is configured to acquire operation information of a transportation junction and transfer information of passengers arriving at the station;
the statistical module is configured to count transfer information of all arriving passengers and determine the initial sharing rate of each connection mode;
the scheduling module is configured to obtain optimal scheduling parameters corresponding to different connection modes by performing iterative solution on the double-layer planning model according to the operation information and the initial sharing rates of the different connection modes;
the upper layer model of the double-layer planning model is a connection time model taking the minimum connection time as a target function, and the lower layer model is a connection selection model.
With reference to the second aspect, in a first implementable manner of the second aspect, the acquisition module includes:
the calling unit is configured to call the operation information of the transportation junction from the railway passenger transportation junction management system;
the positioning unit is configured to position the moving tracks of all passengers;
and the generating unit is configured to determine the connection mode and the connection walking time selected by each passenger according to the moving track of each passenger, and generate transfer information of the passengers arriving at the station according to the connection mode and the connection walking time.
With reference to the second aspect, in a second implementable manner of the second aspect, the scheduling module includes:
the optimization solving unit is configured to solve the connection time model according to the corresponding initial sharing rate based on the constraint conditions to obtain the optimized scheduling parameters of different connection modes;
the selection probability calculation unit is configured to calculate the selection probability of the passenger for different connection modes through the connection selection model according to the optimized scheduling parameters of the different connection modes;
the sharing rate calculation unit is configured to determine the sharing rate after optimization of different connection modes according to the corresponding selection probability;
and the optimization solving unit, the selection probability calculating unit and the sharing rate calculating unit sequentially and repeatedly calculate the optimized scheduling parameters, the selection probability and the sharing rate until the optimal scheduling parameters corresponding to different connection modes are obtained.
With reference to the second implementable manner of the second aspect, in a third implementable manner of the second aspect, the constraint condition of the optimal solution unit configuration includes: the minimum departure interval and the maximum departure interval corresponding to the buses and the tracks, the bus departure sequence and the track departure sequence, the departure time in the connection mode is later than the arrival time of the railway train, and the service intensity of the taxis is optimized.
With reference to the second implementable manner of the second aspect, in a fourth implementable manner of the second aspect, the connection selection model configured by the selection probability calculation unit includes:
Figure BDA0003557002270000051
Figure BDA0003557002270000052
wherein, PinFor the probability of selection of passenger n for the ith docking mode, αiConstant term, β, for the docking mode iikCalibration coefficients, x, for different characteristic variables corresponding to the docking mode iiknSelecting characteristic variables of the hub connection mode i for arriving passengers, AnIs the set of each connection mode.
With reference to the fourth implementable manner of the second aspect, in a fifth implementable manner of the second aspect, the system further includes a calibration parameter determination module, where the calibration parameter determination module is configured to collect characteristic variables of passengers corresponding to different docking manners, and determine, by using a maximum likelihood estimation method, calibration coefficients β of various characteristic variables corresponding to different docking mannersik。
With reference to the fourth implementable manner of the second aspect, in a sixth implementable manner of the second aspect, the characteristic variable includes: sex, age, occupation, monthly income, distance traveled, purpose of travel, and time of docking of the traveler.
With reference to the second implementable manner of the second aspect, in a seventh implementable manner of the second aspect, the sharing rate calculation unit obtains the sharing rates corresponding to different connection manners by performing centralized analysis on the selection probabilities of the different connection manners.
With reference to the second aspect, in an eighth implementable manner of the second aspect, the connection time model includes at least one of a track connection time model, a bus connection time model, a taxi connection time model, and a private car connection time model.
With reference to the eighth implementable manner of the second aspect, in a ninth implementable manner of the second aspect, the probability distribution function obeyed by the passenger walking time in the rail connection time model and the bus connection time model is:
Figure BDA0003557002270000061
wherein, alpha, eta and gamma are distribution parameters.
With reference to the ninth implementable manner of the second aspect, in a tenth implementable manner of the second aspect, the system further includes a distribution parameter determination module configured to calibrate the distribution parameters by performing maximum likelihood estimation on all collected passenger transfer information.
Has the advantages that: by adopting the method and the system for the connection mode collaborative scheduling of the comprehensive transportation hub for the passenger train, the selection behavior of the passenger connection mode, the passenger transfer time, the schedule of each connection mode and the service rate can be synthesized, and the collaborative scheduling is realized in the connection mode, so that the resource allocation of the hub connection is optimized, the operation efficiency of the hub is improved, and safe, quick and comfortable transfer service is provided for passengers.
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In order to more clearly illustrate the embodiments of the present invention, the drawings, which are required to be used in the embodiments, will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to actual scale.
Fig. 1 is a flowchart of a connection-mode cooperative scheduling method for a railway passenger transport integrated transportation hub according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating an iterative solution of a two-layer planning model according to an embodiment of the present invention;
fig. 3 is a system block diagram of a connection-mode cooperative dispatching system of a railway passenger transport integrated transportation hub according to an embodiment of the present invention;
fig. 4 is a system block diagram of an acquisition module of the connection-mode cooperative dispatching system of the railway passenger transport integrated transportation hub according to an embodiment of the present invention;
fig. 5 is a system block diagram of a scheduling module of the integrated transportation hub connection type collaborative scheduling system for passenger train transportation according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, the flow chart of the method for cooperatively scheduling the connection mode of the railway passenger transport integrated transportation hub includes:
step 1, collecting operation information of a traffic hub and transfer information of passengers arriving at a station;
step 2, counting transfer information of all arriving passengers, and determining the initial sharing rate of each connection mode;
step 3, according to the operation information and the initial sharing rate of all the connection modes, carrying out iterative solution on a double-layer planning model to obtain optimal scheduling parameters corresponding to different connection modes;
the upper layer model of the double-layer planning model is a connection time model taking the minimum connection time as a target function, and the lower layer model is a connection selection model.
Specifically, the proportion of different connection modes selected by the arriving passengers and the transfer time for transferring various connection modes can be calculated by establishing a connection selection model and a connection time model of the passengers. And a connection selection model is adopted as a lower-layer model, a connection time model with the minimum connection time as a target function is adopted as a double-layer planning method of an upper-layer model, coordinated scheduling optimization is realized for different connection modes, and an optimal scheduling parameter which enables the total transfer time of all passengers to be the minimum is obtained through solution, so that optimal configuration of hub connection resources is realized, hub operation efficiency is improved, and safe, quick and comfortable transfer service is provided for passengers.
The following describes the scheduling method according to the embodiment of the present invention in detail with reference to fig. 1 and fig. 2.
In step 1, the operation information of the transportation junction can be directly acquired through the railway passenger transportation junction management system, and the operation information comprises the infrastructure layout information of the transportation junction, the arrival train schedule and the number of passengers arriving at the station of each train. The existing railway passenger transport hub management system can automatically acquire and store the information, so that the information can be acquired directly through the railway passenger transport hub management system during cooperative scheduling.
For the transfer information of the passengers arriving at the station, the image information of the passenger transfer can be collected through the cameras arranged at the corresponding positions according to the layout information of the infrastructure of the transportation junction, the number of the passengers corresponding to different docking modes and the docking walking time can be determined through the collected image information, and the transfer information of the passengers arriving at the station can be generated.
For example, images at the exit can be acquired by a camera arranged at the exit of the railway, the identity of each passenger at the exit is identified by adopting an image identification technology, and the exit time of each passenger is determined. Then, the images at the connection positions are acquired through cameras arranged at different connection positions, for example, the images at the bus station can be acquired through the cameras at the bus station, the identity information of each passenger at the bus station and the arrival time at the bus station are identified by adopting an image identification technology, the connection mode and the connection walking time selected by the passenger are determined, and the transfer information of the passenger arriving at the bus station is generated according to the corresponding connection mode and the connection walking time.
It should be understood that the embodiment of the present invention merely determines the transfer information of the passengers arriving at the station by using the image recognition technology, but the present invention is not limited thereto, and other methods, such as mobile phone positioning technology, may also be used to determine the transfer information of the passengers arriving at the station.
In step 2, the transfer information of all outbound passengers can be counted, so as to determine the number of passengers corresponding to each docking mode, and the initial sharing rate corresponding to each docking mode can be calculated by combining the collected number of passengers arriving at the station.
In step 3, the iteratively solving by the double-layer planning model includes:
3-1, solving the connection time model according to the corresponding initial sharing rate based on the constraint conditions to obtain optimized scheduling parameters of different connection modes;
3-2, calculating the selection probability of the passenger for different connection modes through a connection selection model according to the optimized scheduling parameters of the different connection modes;
3-3, determining the sharing rate corresponding to different connection modes according to the corresponding selection probability;
3-4, updating the sharing rates of different connection modes, solving the connection time model again, and determining the corresponding sharing rates of the different connection modes;
repeating the steps, wherein the optimal range of the connection time in the current iteration and the later iteration is smaller than a set threshold value, and the optimal scheduling parameters corresponding to different connection modes are obtained.
Specifically, first, the initial allocation rate corresponding to each connection manner statistically determined in step 2 may be input into the connection time model, and based on the set constraint condition, the connection time model with the minimum connection time as the objective function is solved to obtain the optimized scheduling parameters of different connection manners.
minT＝min(TG+TB+TC+TS)
Then, the optimized scheduling parameters are input into a connection selection model, and the selection probability of the passengers for various connection modes is obtained through calculation of the connection selection model after the scheduling parameters of different connection modes are optimized according to the optimized scheduling parameters.
And then, determining the sharing rate of different connection modes according to the selection probability corresponding to each connection mode, updating the sharing rate of the input connection time model according to the sharing rate, and repeating the steps from 3-1 to 3-4 until the optimal scheduling parameter corresponding to each connection mode is obtained.
In this embodiment, optionally, the connection time model is composed of time models corresponding to 4 connection modes, which are a rail connection time model, a bus connection time model, a taxi connection time model, and a private car connection time model.
It should be understood that the embodiment of the present invention is only illustrated by 4 connection manners, but the present invention is not limited thereto, and the connection time model may be determined according to the connection manner actually set at the transportation junction.
Wherein, the track connection time model TGThe method comprises the following steps:
Figure BDA0003557002270000091
Figure BDA0003557002270000092
Figure BDA0003557002270000093
wherein,
Figure BDA0003557002270000094
in order to catch up with the connection time of the nearby connection rail train,
Figure BDA0003557002270000095
in order to not catch up with the connection time of the nearby connection rail train,
Figure BDA0003557002270000096
in order to connect the departure time of the train on the nearby track,
Figure BDA0003557002270000097
for arrival time of railway trains, deltaGIs the sharing rate of rail transit, Q(i)The number of passengers arriving at the station of the ith railway train, f (t) the probability distribution obeyed by the walking time of the passengers,
Figure BDA0003557002270000098
and the departure time of the rail connection train is obtained.
Bus connection time model TBThe method comprises the following steps:
Figure BDA0003557002270000099
Figure BDA00035570022700000910
Figure BDA00035570022700000911
wherein,
Figure BDA0003557002270000101
in order to catch up with the connection time of the nearby connected bus,
Figure BDA0003557002270000102
in order to be close to the connection time of the bus,
Figure BDA0003557002270000103
to connect the departure time of a bus nearby, deltaBThe sharing rate of the buses is set as the sharing rate of the buses,
Figure BDA0003557002270000104
for the time of departure of a transferred bus, f (t) is a random probability distribution obeyed by the passenger's walking time, in order to better characterize the transfer means as full and the passenger missing the nearby transfer means waiting for the next transfer meansAnd (3) increasing the mantissa of the passenger traveling time distribution which generally obeys normal distribution, adjusting the distribution parameters to be obeyed the normal deviation distribution, and expressing the condition of the normal deviation distribution by using Weibull distribution.
In the present embodiment, it is possible, alternatively,
Figure BDA0003557002270000105
wherein, alpha, eta and gamma are distribution parameters, and the distribution parameters can be calibrated by carrying out maximum likelihood estimation on the acquired transfer information.
Taxi connection time model TCThe method comprises the following steps:
Figure BDA0003557002270000106
wherein, WsFor the stay time of the passenger at the taxi platform, mu is the taxi service rate, lambda is the passenger arrival rate, deltaCAs share rate of taxi, tRCThe walking time required to change taxis. The taxi service rate can be manually set, the passenger arrival rate can be obtained by adopting a cubic curve fitting mode according to historical transfer information, and the relation between the passenger arrival rate and the number of passengers to be transferred, so that the passenger arrival rate can be determined through the taxi sharing rate and the number of passengers arriving at the station.
The private car connection time model TSThe method comprises the following steps:
Figure BDA0003557002270000107
wherein, tRSTime required for walking to a private car parking place, deltaSThe sharing rate of private cars. The time required for the passenger to walk to the private car parking place can be determined through the collected transfer information.
In this embodiment, optionally, the constraint condition includes: the minimum departure interval and the maximum departure interval corresponding to the bus and the rail train, the departure sequence of the bus and the rail train, the departure time of the connection tool is later than the arrival time of the rail train, and the service intensity of the taxi is optimized. The taxi service intensity optimization amplitude can be appointed, and then the optimized service intensity and the taxi service rate are calculated according to the current service intensity and the optimized passenger arrival rate.
In this embodiment, optionally, the docking selection model includes:
Figure BDA0003557002270000111
Figure BDA0003557002270000112
wherein, PinFor the probability of selection of passenger n for the ith docking mode, αiConstant term, β, for the docking mode iikCalibration coefficients, x, for different characteristic variables corresponding to the docking mode iiknSelecting characteristic variable of hub connection mode i for arriving passengers, AnFor each set of connection modes, e in the connection selection model represents a constant, and z is the number of characteristic variables.
In this embodiment, optionally, the characteristic variables include: the sex, age, occupation, monthly income, distance traveled, purpose of travel, and docking time of the traveler. The questionnaire can be issued to the passengers through a railway passenger transport platform, such as a ticket selling platform, and the corresponding information of the passengers about the characteristic variables can be acquired by adopting the questionnaire.
In this embodiment, optionally, the calibration coefficients β of various characteristic variables corresponding to different docking manners may be determined by collecting characteristic variables of passengers corresponding to different docking manners and using a maximum likelihood estimation methodikConstructing a log-likelihood function of the docking model:
Figure BDA0003557002270000113
and solving a first derivative of the logarithm likelihood function and making the derivative be 0 to obtain the maximum likelihood estimation of each calibration coefficient.
In this embodiment, optionally, probability set counting is performed on the selection probabilities of different connection modes, and the selection probabilities of all the passengers for a certain connection mode are added and averaged to obtain the sharing rate corresponding to the connection mode.
As shown in fig. 3, the system block diagram of the connection type cooperative dispatching system of the railway passenger transportation integrated transportation hub includes:
the acquisition module is configured to acquire operation information of a transportation junction and transfer information of passengers arriving at the station;
the statistical module is configured to count transfer information of all arriving passengers and determine the initial sharing rate of each connection mode;
the scheduling module is configured to obtain optimal scheduling parameters corresponding to different connection modes by performing iterative solution on the double-layer planning model according to the operation information and the initial sharing rates of the different connection modes;
the upper layer model of the double-layer planning model is a connection time model taking the minimum connection time as a target function, and the lower layer model is a connection selection model.
Specifically, the dispatching system is composed of an acquisition module, a statistical module and a dispatching module, wherein the acquisition module can acquire the operation information of the traffic hub and the transfer information of passengers arriving at the station.
The statistical module can calculate the number of passengers selecting different connection modes according to the transfer information of each arriving passenger collected by the collecting module, and the initial sharing rate of each connection mode can be calculated by combining the number of arriving passengers of the train.
The scheduling module can calculate the proportion of different connection modes selected by the arriving passengers and the transfer time for transferring various connection modes by establishing a connection selection model and a connection time model of the passengers. And a connection selection model is adopted as a lower-layer model, a connection time model with the minimum connection time as a target function is adopted as a double-layer planning method of an upper-layer model, coordinated scheduling optimization is realized for different connection modes, and an optimal scheduling parameter which enables the total transfer time of all passengers to be the minimum is obtained through solution, so that optimal configuration of hub connection resources is realized, hub operation efficiency is improved, and safe, quick and comfortable transfer service is provided for passengers.
The following describes the scheduling system according to an embodiment of the present invention in detail with reference to fig. 3, fig. 4, and fig. 5.
In this embodiment, optionally, the acquisition module includes:
the transfer unit is configured to transfer the operation information of the transportation junction from the railway passenger transportation junction management system;
the positioning unit is configured to position the moving tracks of all passengers;
and the generating unit is configured to determine the connection modes and the connection walking time selected by all passengers according to the positioning result of the positioning unit.
The acquisition module consists of a calling unit, a positioning unit and a generating unit, wherein the calling unit is in communication connection with the railway passenger transport hub management system, and can automatically acquire operation information from the railway passenger transport hub management system by adopting tools such as a web crawler and the like, and the acquired operation information comprises the infrastructure layout information of the hub, the arrival train schedule and the number of passengers arriving at each train.
The positioning unit can acquire images of all positions through a camera arranged in the transportation hub, and the moving track of each arriving passenger is determined by adopting an image recognition positioning technology according to the acquired images.
Because the basic setting layout information called by the calling unit comprises the position coordinates of the connecting platforms of various connecting modes and the position coordinates of the railway train exit, the generating unit can determine the connecting mode and the connecting walking time selected by the passengers arriving at the station through the moving track of each passenger arriving at the station and the basic facility layout information of the traffic junction called by the calling unit, and generate corresponding transfer information according to the connecting mode and the connecting walking time.
For example, the generating unit may determine the position coordinates of the train exit and the position coordinates of the stations in different connection modes, such as the position coordinates of the bus station, according to the infrastructure layout information. The generating unit can match the position coordinates of the train exit and the position coordinates of the platforms with different connection modes with the moving track of the arriving passenger, so that the connection mode selected by the arriving passenger, the exiting time of the arriving passenger and the arrival time of the connection platform can be determined, and the generating unit can calculate the connection walking time of the arriving passenger according to the exiting time and the arrival time.
In this embodiment, optionally, the scheduling module includes:
the optimization solving unit is configured to solve the connection time model according to the corresponding initial sharing rate based on the constraint conditions to obtain the optimized scheduling parameters of different connection modes;
the selection probability calculation unit is configured to calculate the selection probability of the passenger for different connection modes through the connection selection model according to the optimized scheduling parameters of the different connection modes;
the sharing rate calculating unit is configured to determine the sharing rate after optimization of different connection modes according to the corresponding selection probability;
and the optimization solving unit, the selection probability calculating unit and the sharing rate calculating unit sequentially and repeatedly calculate the optimized scheduling parameters, the selection probability and the sharing rate until the optimal scheduling parameters corresponding to different connection modes are obtained.
Specifically, the optimization solving unit may input the initial sharing rates corresponding to the different connection modes counted by the counting module into the connection time model, and obtain the optimized scheduling parameters of the different connection modes by solving the connection time model with the minimum connection time as the objective function based on the set constraint condition.
The selection probability calculation unit can input the optimized scheduling parameters obtained by the optimization solution unit into the connection selection model, and the selection probability of the passenger for various connection modes is obtained by calculating through the connection selection model after the optimized scheduling parameters of various connection modes are optimized.
The sharing rate calculating unit may calculate the sharing rates of different connection modes according to the selection probabilities corresponding to the various connection modes calculated by the selection probability calculating unit.
The optimization solving unit can update the input sharing rate according to the sharing rate calculated by the sharing rate, and carry out optimization solving on the connection time model again according to the updated sharing rate to obtain a new optimized scheduling parameter. And the selection probability calculation unit calculates new selection probabilities of the passengers for various connection modes according to the new optimized scheduling parameters. And finally, the sharing rate calculating unit recalculates the sharing rates of different connection modes according to the new selection probability to update the sharing rate input by the optimization solving unit. And repeating the steps until the optimal connection time optimization amplitude in the two iterations is smaller than a certain threshold value, and determining that the optimal scheduling parameters corresponding to different connection modes are obtained.
In this embodiment, optionally, the connection time model adopted by the optimization solving unit is composed of time models corresponding to 4 connection modes, which are a rail connection time model, a bus connection time model, a taxi connection time model and a private car connection time model respectively.
It should be understood that the embodiment of the present invention is only illustrated by 4 connection manners, but the present invention is not limited thereto, and the connection time model may be determined according to the connection manner actually set at the transportation junction.
Wherein, the track connection time model TGThe method comprises the following steps:
Figure BDA0003557002270000141
Figure BDA0003557002270000142
Figure BDA0003557002270000143
wherein,
Figure BDA0003557002270000151
in order to catch up with the connection time of the nearby connection rail train,
Figure BDA0003557002270000152
in order to not catch up with the connection time of the nearby connection rail train,
Figure BDA0003557002270000153
in order to connect the departure time of the train on the nearby track,
Figure BDA0003557002270000154
for arrival time of railway trains, deltaGIs the sharing rate of rail transit, Q(i)The number of passengers arriving at the station of the ith railway train, f (t) the probability distribution obeyed by the walking time of the passengers,
Figure BDA0003557002270000155
and the departure time of the rail connection train is obtained.
Bus connection time model TBThe method comprises the following steps:
Figure BDA0003557002270000156
Figure BDA0003557002270000157
Figure BDA0003557002270000158
wherein,
Figure BDA0003557002270000159
in order to catch up with the connection time of the nearby connected bus,
Figure BDA00035570022700001510
in order to keep up with the connection time of the bus,
Figure BDA00035570022700001511
for the purpose of connecting the departure time of a bus nearby, deltaBThe sharing rate of the buses is set as the sharing rate of the buses,
Figure BDA00035570022700001512
in order to connect the departure time of the bus, f (t) is the random probability distribution obeyed by the walking time of the passengers, in order to better depict the conditions that the connection tool is fully loaded and the passengers miss the nearby connection tool to wait for the next connection tool, the mantissa of the passenger walking time distribution which usually obeys normal distribution is increased, the distribution parameters are adjusted to be obeyed with the positive skewed distribution, and then the Weibull distribution is used for representing the condition of the positive skewed distribution.
In this embodiment, optionally, in the rail connection time model and the bus connection time model, the probability distribution function obeyed by the passenger walking time is as follows:
Figure BDA00035570022700001513
wherein, alpha, eta and gamma are distribution parameters.
The dispatching system also comprises a distribution parameter determining module which can calibrate the distribution parameters by carrying out the maximum likelihood estimation method on the collected transfer information of all the passengers arriving at the station. Specifically, the distribution parameter determination module may substitute the collected transfer travel time sample data of the arriving passengers in the transfer information into a probability distribution function and sum logarithms to obtain a likelihood function, and solve the likelihood function for obtaining partial derivatives about the distribution functions α, η, and γ to obtain a likelihood equation and solve the likelihood equation to obtain maximum likelihood estimates of α, η, and γ.
Taxi connection time model TCThe method comprises the following steps:
Figure BDA0003557002270000161
wherein, WsFor the stay time of the passenger at the taxi platform, mu is the taxi service rate, lambda is the passenger arrival rate, deltaCTo be outSharing rate of renting cars, tRCThe walking time required to change taxis. The service rate of the taxi can be manually set, and the arrival rate of passengers
The private car connection time model TSThe method comprises the following steps:
Figure BDA0003557002270000162
wherein, tRSTime required for walking to a private car parking place, deltaSSharing rate for private cars. The time required for the passenger to walk to the private car parking place can be determined through the collected transfer information.
In this embodiment, optionally, the constraint conditions of the configuration of the optimization solution unit include: the minimum departure interval and the maximum departure interval corresponding to the buses and the tracks, the bus departure sequence and the track departure sequence, the departure time in the connection mode is later than the arrival time of the railway train, and the service intensity of the taxis is optimized.
In this embodiment, optionally, the connection selection model configured by the selection probability calculation unit includes:
Figure BDA0003557002270000163
Figure BDA0003557002270000164
wherein, PinFor the probability of selection of passenger n for the ith docking mode, αiConstant term, β, for the docking mode iikCalibration coefficients, x, for different characteristic variables corresponding to the docking mode iiknSelecting characteristic variables of the hub connection mode i for arriving passengers, AnIs a set of docking modes.
In this embodiment, optionally, the system further includes a calibration parameter determining module, where the calibration parameter determining module is configured to collect characteristic variables of passengers corresponding to different docking manners, and pass through the maximum likelihoodThe estimation method determines the calibration coefficient beta of various characteristic variables corresponding to different connection modesik。
The coordination system also comprises a calibration parameter determination module which can acquire the characteristic variable information of passengers arriving at the station through the acquisition module and determine the calibration coefficients of various characteristic variables corresponding to different docking modes by adopting a maximum likelihood estimation method according to the characteristic variable information of each passenger.
In this embodiment, the characteristic variables extracted by the calibration parameter determination module include: the sex, age, occupation, monthly income, distance traveled, purpose of travel, and docking time of the traveler. The acquisition module can issue questionnaires to passengers riding in the railway train through the railway passenger transport platform, and acquire the information of the characteristic variables in a questionnaire mode.
In this embodiment, optionally, the sharing rate calculating unit performs centralized analysis on the selection probabilities of the different connection modes to obtain the sharing rates corresponding to the different connection modes.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.

Claims (21)

1. A method for cooperatively dispatching a railway passenger transport integrated transportation hub in a connection mode is characterized by comprising the following steps:
collecting operation information of a traffic hub and transfer information of passengers arriving at a station;
counting transfer information of all arriving passengers, and determining the initial sharing rate of each connection mode;
according to the operation information and the initial sharing rate of all the connection modes, carrying out iterative solution on the double-layer planning model to obtain optimal scheduling parameters corresponding to different connection modes;
the upper layer model of the double-layer planning model is a connection time model taking the minimum connection time as a target function, and the lower layer model is a connection selection model.
2. The method for joint connection type collaborative scheduling of the railway passenger transport integrated transportation hub according to claim 1, wherein the iterative solution through the double-layer planning model comprises:
based on constraint conditions, solving the connection time model according to corresponding initial sharing rate to obtain optimized scheduling parameters of different connection modes;
according to the optimized scheduling parameters of different docking modes, calculating the selection probability of the arriving passengers for different docking modes through a docking selection model;
determining the sharing rate corresponding to different connection modes according to the corresponding selection probability;
updating the sharing rates of different connection modes, solving the connection time model again, and calculating the corresponding sharing rates of the different connection modes through the connection selection model again;
and repeating the steps until the optimal scheduling parameters corresponding to different connection modes are obtained.
3. The method for cooperatively dispatching the integrated transportation hub connection mode for passenger train according to claim 2, wherein the constraint condition comprises: the minimum departure interval and the maximum departure interval corresponding to the bus and the rail train, the departure sequence of the bus and the rail train, the departure time of the connection tool is later than the arrival time of the rail train, and the service intensity of the taxi is optimized.
4. The method for joint connection type collaborative scheduling of the railway passenger transport integrated transportation hub according to claim 2, wherein the connection selection model comprises:
Figure FDA0003557002260000011
Figure FDA0003557002260000012
wherein, PinFor the selection probability, alpha, of the inbound passenger n for the ith mode of dockingiConstant term, β, for the docking mode iikCalibration coefficients, x, for different characteristic variables corresponding to the docking mode iiknSelecting characteristic variable of hub connection mode i for passengers arriving at station, AnIs the set of each connection mode.
5. The method for cooperatively dispatching the connection mode of the railway passenger transport integrated transportation hub according to claim 4, comprising the following steps: collecting characteristic variables of passengers corresponding to different connection modes, and determining calibration coefficients beta of the characteristic variables corresponding to the different connection modes by adopting a maximum likelihood estimation methodik。
6. The method for the joint connection type collaborative scheduling of the railway passenger transportation integrated transportation hub according to claim 4, wherein the characteristic variables comprise: gender, age, occupation, monthly income, distance traveled, purpose of travel, and docking time of the arriving passenger.
7. The method for the connection mode cooperative scheduling of the railway passenger transport integrated transportation hub according to claim 2, wherein the sharing rate corresponding to different connection modes is obtained by performing centralized analysis on the selection probabilities of the different connection modes.
8. The method for collaborative scheduling of junction connection modes of the railway passenger transport integrated transportation junction according to claim 1, wherein the junction time model comprises at least one of a rail junction time model, a bus junction time model, a taxi junction time model and a private car junction time model.
9. The method for cooperatively dispatching the connection mode of the railway passenger transport integrated transportation hub according to claim 8, wherein in the rail connection time model and the bus connection time model, the probability distribution function obeyed by the walking time of the passenger is as follows:
Figure FDA0003557002260000021
wherein, alpha, eta and gamma are distribution parameters.
10. The method according to claim 9, wherein the distribution parameters are calibrated by performing maximum likelihood estimation on the collected passenger transfer information.
11. A railway passenger transport comprehensive transportation hub connection mode cooperative dispatching system is characterized by comprising:
the acquisition module is configured to acquire operation information of a transportation junction and transfer information of passengers arriving at the station;
the statistical module is configured to count transfer information of all arriving passengers and determine the initial sharing rate of each connection mode;
the scheduling module is configured to obtain optimal scheduling parameters corresponding to different connection modes by performing iterative solution on the double-layer planning model according to the operation information and the initial sharing rates of the different connection modes;
the upper layer model of the double-layer planning model is a connection time model taking the minimum connection time as a target function, and the lower layer model is a connection selection model.
12. The integrated transportation hub connection type cooperative dispatching system for passenger trains according to claim 11, wherein the collecting module comprises:
the calling unit is configured to call the operation information of the transportation junction from the railway passenger transportation junction management system;
the positioning unit is configured to position the moving tracks of all passengers;
and the generating unit is configured to determine the connection mode and the connection walking time selected by each passenger according to the moving track of each passenger, and generate transfer information of the passengers arriving at the station according to the connection mode and the connection walking time.
13. The integrated transportation hub connection type cooperative dispatching system for passenger trains according to claim 11, wherein the dispatching module comprises:
the optimization solving unit is configured to solve the connection time model according to the corresponding initial sharing rate based on the constraint conditions to obtain the optimized scheduling parameters of different connection modes;
the selection probability calculation unit is configured to calculate the selection probability of the arriving passenger for different connection modes through the connection selection model according to the optimized scheduling parameters of the different connection modes;
the sharing rate calculation unit is configured to determine the sharing rate after optimization of different connection modes according to the corresponding selection probability;
and the optimization solving unit, the selection probability calculating unit and the sharing rate calculating unit sequentially and repeatedly calculate the optimized scheduling parameters, the selection probability and the sharing rate until the optimal scheduling parameters corresponding to different connection modes are obtained.
14. The system for joint connection type cooperative dispatching of the integrated transportation hub for passenger train according to claim 13, wherein the constraint conditions configured by the optimization solution unit comprise: the minimum departure interval and the maximum departure interval corresponding to the bus and the rail train, the departure sequence of the bus and the rail train, the departure time of the connection tool is later than the arrival time of the rail train, and the service intensity of the taxi is optimized.
15. The system for collaborative dispatching of connection modes of integrated transportation hubs for passenger trains according to claim 13, wherein the connection selection model configured by the selection probability calculation unit comprises:
Figure FDA0003557002260000041
Figure FDA0003557002260000042
wherein, PinFor the probability of selection of passenger n for the ith mode of docking, αiConstant term, β, for the docking mode iikCalibration coefficients, x, for different characteristic variables corresponding to the docking mode iiknSelecting characteristic variable of hub connection mode i for arriving passengers, AnIs the set of each connection mode.
16. The transportation junction connection mode cooperative scheduling system of claim 15, further comprising a calibration parameter determination module, wherein the calibration parameter determination module is configured to collect characteristic variables of passengers corresponding to different connection modes, and determine the calibration coefficients β of the characteristic variables corresponding to different connection modes by using a maximum likelihood estimation methodik。
17. The connection type cooperative dispatching system of the railway passenger transportation integrated transportation junction according to claim 15, wherein the characteristic variables comprise: gender, age, occupation, monthly income, distance traveled, purpose of travel, and docking time of the arriving passenger.
18. The connection mode cooperative scheduling system of the railway passenger transport integrated transportation hub according to claim 13, wherein the sharing rate calculating unit obtains the sharing rates corresponding to different connection modes by performing centralized analysis on the selection probabilities of the different connection modes.
19. The transportation junction docking mode collaborative scheduling system of claim 11, wherein the docking time model includes at least one of a track docking time model, a bus docking time model, a taxi docking time model, and a private car docking time model.
20. The transportation junction connection mode cooperative scheduling system of claim 19, wherein in the rail connection time model and the bus connection time model, the probability distribution function obeyed by the walking time of the arriving passenger is as follows:
Figure FDA0003557002260000043
wherein, alpha, eta and gamma are distribution parameters.
21. The integrated transportation hub docking mode coordinated scheduling system of passenger train according to claim 20, further comprising a distribution parameter determination module configured to calibrate said distribution parameter by performing maximum likelihood estimation on all collected passenger transfer information.
CN202210281027.7A 2022-03-21 2022-03-21 Railway passenger transport comprehensive transportation hub connection mode cooperative scheduling method and system Pending CN114723240A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116757330A (en) * 2023-08-10 2023-09-15 北京经纬信息技术有限公司 Method, system, equipment and medium for calculating minimum transit time of different stations in same city of railway

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116757330A (en) * 2023-08-10 2023-09-15 北京经纬信息技术有限公司 Method, system, equipment and medium for calculating minimum transit time of different stations in same city of railway
CN116757330B (en) * 2023-08-10 2023-11-14 北京经纬信息技术有限公司 Method, system, equipment and medium for calculating minimum transit time of different stations in same city of railway

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