CN115577818A - Passenger demand response type carpooling scheduling method and system for intelligent bus - Google Patents
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Abstract
The invention belongs to the technical field of bus scheduling, and particularly relates to a passenger demand response type carpooling scheduling method and system for an intelligent bus, wherein the method comprises the following steps: s1, a passenger logs in a small program for the passenger and starts a function of reserving car sharing travel; s2, passengers select car sharing travel services through small programs, and input travel information to generate car sharing orders; s3, the dispatching management module combines and combines a plurality of car sharing orders of passengers by running a car sharing algorithm, plans the route of the orders and inquires available vehicles; s4, generating a feasible initial vehicle path solution based on a gravity model algorithm; s5, further optimizing the initial vehicle path solution through a heuristic algorithm; and S6, issuing order tasks to the vehicles through the scheduling management module to complete the car sharing orders.
Description
Technical Field
The invention belongs to the technical field of bus dispatching, and particularly relates to a passenger demand response type carpooling dispatching method and system for an intelligent bus.
Background
The basic idea of the demand-responsive bus is to arrange the operation of buses according to the boarding and alighting time and the boarding and alighting time reserved by potential passengers, so that door-to-door transportation is realized, compared with the existing reserved taxis, the demand-responsive bus has the advantages of high bearing rate and low transportation cost, and compared with the conventional bus, the demand-responsive bus has the advantages of flexibility and convenience in traveling.
Disclosure of Invention
The invention collects travel requests of passengers in real time, performs cluster analysis on departure places of the passengers, determines demand response stations, allocates reasonable vehicles for the passengers according to station positions and the number of the passengers, and performs dynamic path planning on the vehicles, aiming at optimizing operation cost and travel experience of the passengers.
In order to achieve the above purpose, the following passenger demand response type car pooling scheduling method for intelligent buses is provided, and mainly comprises the following steps:
s1, a passenger logs in a small program for the passenger, a scheduling management module identifies the identity of the passenger, and the passenger can start a function of reserving the car-pooling trip after the identity of the passenger is successfully identified;
s2, passengers select car sharing travel services through small programs, and input travel information to generate car sharing orders;
s3, the dispatching management module combines and combines a plurality of car sharing orders of passengers by running a car sharing algorithm, plans the route of the orders and inquires available vehicles;
s4, converting the path search problem into an iterative problem of a station selection chain with the maximum attractive force between the calculation and the current station based on a gravitational model algorithm, and generating a feasible initial vehicle path solution;
s5, further optimizing the initial vehicle path solution through a heuristic algorithm;
and S6, issuing order tasks to the vehicles through the scheduling management module, and executing the car sharing function by the vehicles according to the route and station information output in the S5 so as to finish car sharing orders.
As a preferred technical solution of the present invention, the S2 includes the steps of:
s21, passengers reserve travel according to travel demands, and each passenger transmits information including a departure station, a destination station and expected arrival time of the passengers to a scheduling management module;
s22, the scheduling management module clusters all passengers according to the target station and the expected arrival time of each passenger and the principle that the actual arrival time is not later than the expected value of the passenger, the passengers with the same target station and the expected arrival time are grouped into one class, and the passengers with the same arrival time and arrival place are grouped into one class.
As a preferred technical solution of the present invention, the S3 includes the steps of:
s31, establishing a topology of a vehicle operation process, taking 30 minutes as a long decision period, regarding the long decision period, taking the topology of the vehicle operation process as a decision network, specifically abstracting a vehicle, a passenger getting-on point, a passenger getting-off point and a period termination into nodes respectively, and connecting every two nodes through a directed arc, thereby obtaining the topology of the vehicle operation process;
s32, establishing a target function, mainly considering operation profit maximization and optimal passenger trip experience in the optimization target, wherein the most critical factor influencing the passenger trip experience is the waiting time of the passenger, soft time window constraint of the waiting time of the passenger is considered, when the waiting time of the passenger exceeds a waiting time threshold value, the cost required to be paid by the passenger is reduced, and meanwhile, a certain penalty value is generated for the operation profit.
As a preferred technical scheme of the invention, according to the topology of the vehicle operation process, the method comprises the following steps:
s311, setting the set of nodes at the current position of the vehicle as K, setting the request set as R, wherein R1 represents that the vehicle is requested on the vehicle, R2 represents that the vehicle is not requested on the vehicle, and setting the set of the nodes at the vehicle-on and vehicle-off points as I, wherein I 1 - Indicating that the vehicle has got on or off the bus, i 2 + ，i 2 - Requesting departure and departure nodes, i, respectively representing binding with a vehicle 3 + ，i 3 - Respectively representing unallocated nodes requesting to get on or off the bus, setting a set of termination nodes as S, wherein the starting node of the directed arc falls in K and R, and the termination nodes fall in S;
s312, if the vehicle k passes through the directional arc from i to j, the variable x is changed ij k Is set to 1, A is used kr Indicating that vehicle k is assigned order r, using N i k Representing the number of passengers of k vehicles at i-node, using t i k Representing the time of arrival of vehicle k at pick-up node i, using p i Indicating a change in passengers at node i.
As a preferred technical solution of the present invention, the following objective function maxZ is established in S32:
wherein, f (o) r ,d r ) Passenger of order r from o r Go to d r The fee to be paid, n r For order r from o r Go to d r Number of passengers, c k D (i, j) is the distance traveled from node i to node j, cp, for the unit travel cost of vehicle k kr Penalty charges incurred in executing order r for vehicle k.
As a preferred technical solution of the present invention, the S4 includes the steps of:
s41, determining departure stations of the vehicle, and randomly extracting one station from stations with passenger boarding requirements as a departure point of a vehicle k, wherein the initial value of k is 1;
s42, judging whether similar passengers are not served, if so, jumping to S43, otherwise, jumping to S45;
s43, searching a next station, finding a station X with the greatest attraction force with the current station from the boarding stations containing similar passengers, trying to join the station X into a path selection chain, and calculating the number of passengers of the vehicle after joining the station X and the time for the vehicle to directly reach a target station after joining the station X;
s44, judging whether the vehicle route is reasonable after joining the station X, if the number of passengers served by the current vehicle does not exceed the vehicle-mounted capacity and the time for arriving at the target station does not exceed the time required by the passengers, jumping to S43 by taking the station X as a new starting point, and otherwise, jumping to S45;
and S45, judging whether all classes of passengers are scheduled for service, if no passenger is scheduled for service, scheduling the next vehicle, wherein k = k +1, and jumping to S41, otherwise, outputting all current initial paths, and finishing the algorithm for generating the initial vehicle path solution based on the gravity model.
As a preferred technical solution of the present invention, the S5 includes the steps of:
s51, firstly, balancing the station number among vehicles with the same time requirements for serving the target station and arriving at the target station, checking whether the vehicles have the condition of unbalanced station number serving, if so, transferring part of stations in the vehicle route needing to pass through the station with a large number of stations to the vehicle route with a small number of stations under the premise of ensuring to meet the vehicle-mounted capacity and the time requirements for arriving at the target station, and arranging a reasonable station sequence;
s52, trying to optimize the path between vehicles with the same time requirement for serving a target station and reaching the target station, mainly using a mode of exchanging stations between two paths of lines to search for a more optimal path, and ensuring that the vehicle-mounted capacity and the time requirement for reaching the target station are met in the exchange optimization process;
s53, performing internal optimization on the route of each vehicle, mainly trying to exchange the sequence of two stations in the same vehicle route, evaluating whether the objective function value is reduced, if so, exchanging the sequence of the stations, otherwise, abandoning the exchange, and after trying for a preset number of times, terminating the algorithm and outputting the final route result.
As a preferred technical solution of the present invention, the S6 includes the steps of:
s61, when the passenger successfully shares the car and the vehicle completes the order task, marking the car sharing order as successful, transmitting the vehicle information to the passenger small program through the mobile communication subsystem, and informing the passenger of the information related to the successful car sharing;
s62, when the passenger fails to share the car and the vehicle does not complete the order task, transmitting the failure information of the share-car order to the small program for the passenger through the communication subsystem, and informing the passenger of the information related to the share-car failure.
The invention also provides a passenger demand response type car pooling dispatching system for the intelligent bus, which comprises the following subsystems:
the bus-mounted subsystem comprises a bus-mounted mobile communication terminal, a camera and passenger flow information acquisition equipment, is used for acquiring position information and operation state information of a bus in real time, comprehensively monitoring the running state of the bus, counting the number of passengers getting on and off the bus in real time and providing functions of acquiring vehicle running data and issuing a dispatching instruction for the bus intelligent dispatching subsystem;
the intelligent bus dispatching subsystem comprises a dispatching management module and a customized bus mobile terminal module as follows:
the system comprises a dispatching management module, a bus data brain center and a visual large-screen display module, wherein the dispatching management module is used for automatically realizing an operation plan of a vehicle, realizing real-time monitoring of vehicle operation, performing holographic sensing and multi-source fusion on basic data of buses, lines, stops and operators and dynamic data of GPS, scheduling and balance, and also used for constructing the bus data brain center and displaying a series of bus service data through the visual large-screen display;
the customized bus mobile terminal module comprises a passenger APP, a passenger applet and an operator APP and is used for realizing the information service function of customized bus travel, and the customized bus mobile terminal module comprises a passenger terminal service, an operator terminal service, an operation settlement monitoring and analyzing service, a attendance rate counting service and a stop passenger flow counting service;
the bus station subsystem comprises an electronic station board and a video monitoring device and is used for realizing a station passenger flow volume counting function, a station monitoring function, a bus information inquiry function, an arrival pre-broadcasting function, a multimedia voice function and a station monitoring function;
the bus station subsystem comprises a bus parking lot and a bus command center large screen, wherein the bus parking lot comprises video monitoring equipment and inter-station networking equipment, and the command center large screen comprises an LED display system, an image and audio processing system and a transmission control system and is used for providing data support for the bus intelligent scheduling subsystem and the visual large screen;
and the mobile communication subsystem is used for connecting the bus-mounted subsystem, the bus intelligent scheduling subsystem, the bus station subsystem and the bus station subsystem by using a wireless communication network.
Compared with the prior art, the invention has the beneficial effects that at least:
the invention relates to a method for customizing bus demands in the practical bus operation, which is characterized in that passengers submit travel demands in a small program in real time, demand information comprises getting-on places, getting-off places, expected getting-on time, expected getting-off time and passenger quantity, a system distributes car sharing vehicles for the passengers according to the real-time positions of the current operation vehicles, the number of passengers on the vehicles and the planned travel paths of the vehicles, and updates the travel node sequences of the vehicles in real time according to the travel demands of the passengers, thereby realizing the flexible delivery service of the vehicles guided by the passenger demands.
Drawings
FIG. 1 is a flow chart of a passenger demand responsive ride share scheduling method for intelligent buses according to the present invention;
FIG. 2 is a flow chart of a demand-responsive passenger clustering method of the present invention;
fig. 3 is a structural diagram of the passenger demand response type car pool dispatching system for intelligent bus according to the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of the present application.
Referring to fig. 1, the inventor proposes a passenger demand response type car pooling scheduling method for intelligent buses, which mainly comprises the following steps:
s1, a passenger logs in a small program for the passenger, a scheduling management module identifies the identity of the passenger, and the passenger can start the function of reserving the car-pooling trip after the identity of the passenger is successfully identified;
s2, passengers select car pooling travel service through the small program for the passengers, and input travel information to generate car pooling orders;
s3, the dispatching management module combines and combines a plurality of car sharing orders of passengers by running a car sharing algorithm, plans the route of the orders and inquires available vehicles;
s4, converting the path search problem into an iterative problem of a station selection chain with the maximum attractive force between the calculation and the current station based on a gravitational model algorithm, and generating a feasible initial vehicle path solution;
s5, further optimizing the initial vehicle path solution through a heuristic algorithm;
s6, issuing order tasks to the vehicles through the scheduling management module, and executing a car pooling function by the vehicles according to the route and station information output in the S5 to finish car pooling orders;
specifically, the inventor finds that the mode switching or combined optimization is mostly performed according to the actual passenger flow condition on the basis of the conventional bus by the conventional demand-responsive bus scheduling method, which often affects the normal driving plan of the conventional bus, and few researches are performed in the aspect of considering the selection of the fixed station or the demand-responsive station, and most of the researches stay in the existing station and the virtual road network station, so that the inventor proposes the above-mentioned S1 to the above-mentioned S6 to solve the technical problems existing in the conventional demand-responsive bus scheduling method.
Further, the step S2 includes the steps of:
s21, passengers reserve travel according to travel demands, and each passenger transmits information including a departure station, a destination station and expected arrival time of the passengers to a scheduling management module;
s22, the scheduling management module clusters all passengers according to the target station and the expected arrival time of each passenger and the principle that the actual arrival time is not later than the expected value of the passenger, the passengers with the same target station and the expected arrival time are grouped into one class, and the passengers with the same arrival time and arrival place are grouped into one class;
specifically, in this embodiment, a K-means clustering algorithm is used for clustering, and a detailed clustering process is shown in fig. 2.
Further, the step S3 includes the steps of:
s31, establishing a topology of a vehicle operation process, taking 30 minutes as a long decision period, regarding the long decision period, taking the topology of the vehicle operation process as a decision network, specifically abstracting a vehicle, a passenger boarding point, a passenger disembarking point and a period termination into nodes respectively, and connecting every two nodes through a directed arc, thereby obtaining the topology of the vehicle operation process;
s32, establishing a target function, wherein the optimization target mainly considers maximization of operation income and optimization of travel experience of passengers, the most critical factor influencing the travel experience of the passengers is waiting time of the passengers, soft time window constraint of the waiting time of the passengers is considered, when the waiting time of the passengers exceeds a waiting time threshold value, the cost required to be paid by the passengers is reduced, and meanwhile, a certain penalty value is generated on the operation income;
specifically, for ease of understanding, the operation of a vehicle can be abstracted as: vehicle → required getting-on point 1 → required getting-on point 2 → required getting-off point 1 → required getting-off point 2 → decision cycle ends.
Further, according to the topology of the vehicle operation process, the method comprises the following steps:
s311, setting the set of the nodes at the current position of the vehicle as K, setting the request set as R, wherein R1 represents that the vehicle is requested on the vehicle, R2 represents that the vehicle is not requested on the vehicle, and setting the set of the points at which the vehicle gets on or off as I, wherein I 1 - Indicating that the vehicle has got on or off the bus, i 2 + ，i 2 - Requesting departure and departure nodes, i, respectively representing binding with a vehicle 3 + ，i 3 - Respectively representing unallocated nodes requesting to get on or off the bus, setting a set of termination nodes as S, enabling a starting node of the directed arc to fall in K and R, and enabling the termination nodes to fall in S;
s312, if the vehicle k passes through the directional arc from i to j, the variable x is changed ij k Is set to 1, A is used kr Indicating that vehicle k is assigned order r, using N i k Representing the number of passengers of k vehicles at i-node, using t i k Representing the time of arrival of vehicle k at pick-up node i, using p i Indicating a change in passengers at node i.
Further, based on the topology of the vehicle operation process, the penalty cost may be calculated in S32 as follows:
wherein cp kr Representing the penalty charge, t, incurred by vehicle k in executing order r r l Is the waiting time, t, for the passenger to initiate an order r l Is the latest waiting time of the passenger，t i k Time of arrival of vehicle k at boarding and alighting node i, t max The adjustment may be set manually for the passenger waiting time threshold, which may typically take 3 minutes, and f may be set manually for the unit time cost paid for placing the order, which may typically take 2 dollars per minute.
Further, according to the above description, the following objective function maxZ can be finally established in the above S32:
wherein, f (o) r ,d r ) Passenger of order r from o r Go to d r The fee to be paid, n r For order r from o r Go to d r Number of passengers, c k D (i, j) is the distance traveled from node i to node j, cp, for the unit travel cost of vehicle k kr Penalty charges incurred in executing order r for vehicle k;
specifically, after the objective function maxZ is established, the following constraints need to be designed:
a. ensuring that passengers already on board the vehicle and passengers already assigned to the vehicle must be serviced:
b. limiting each order to be serviced by at most one vehicle:
c. flow restriction
c1 Limit each arc to be executed at most once:
c2 Limit the vehicles arriving at the passenger nodes from the nodes:
c3 Limit each vehicle to either pick up passengers or wait on site during the operating cycle:
c4 Restrict vehicle nodes from moving to other vehicle nodes):
d. time window constraints
d1 Vehicle arrival station succession constraint:
d2 Limit the time that the vehicle reaches the boarding and disembarking stations to satisfy a certain time window:
e. vehicle capacity constraint
e1 Limiting the number of people on the vehicle reaching the next station to the number of people on the vehicle when the vehicle reaches the current station plus the number of people about to get on the vehicle at the next station:
e2 Limit the number of passengers in the vehicle to be less than the on-board passenger volume:
f. limiting the getting-on point of the same order to be positioned in front of the getting-off point:
g. the pick-up and drop-off points that limit orders must be serviced by the same vehicle:
further, by combining the above contents, the present embodiment finally establishes the following carpooling mathematical model:
s.t.
further, the step S4 includes the steps of:
s41, determining departure stations of the vehicle, and randomly extracting one station from stations with passenger boarding requirements as a departure point of a vehicle k, wherein the initial value of k is 1;
s42, judging whether similar passengers are not served, if so, jumping to S43, otherwise, jumping to S45;
s43, searching a next station, finding a station X with the greatest attraction force with the current station from the boarding stations containing similar passengers, trying to join the station X into a path selection chain, and calculating the number of passengers of the vehicle after joining the station X and the time for the vehicle to directly reach a target station after joining the station X;
s44, judging whether the vehicle route is reasonable after joining the station X, if the number of passengers served by the current vehicle does not exceed the vehicle-mounted capacity and the time for arriving at the target station does not exceed the time required by the passengers, jumping to S43 by taking the station X as a new starting point, and otherwise, jumping to S45;
and S45, judging whether all the classes of passengers are scheduled for service, if the passengers are not scheduled for service, scheduling the next vehicle, wherein k = k +1, and jumping to S41, otherwise, outputting all the current initial paths, and ending the algorithm for generating the initial vehicle path solution based on the gravity model.
Further, the step S5 includes the steps of:
s51, firstly, balancing the station number among vehicles with the same time requirements for serving the target station and arriving at the target station, checking whether the vehicles have the condition of unbalanced station number serving, if so, transferring part of stations in the vehicle route needing to pass through the station with a large number of stations to the vehicle route with a small number of stations under the premise of ensuring to meet the vehicle-mounted capacity and the time requirements for arriving at the target station, and arranging a reasonable station sequence;
s52, attempting to optimize paths between vehicles with the same time requirements for serving a target station and arriving at the target station, mainly searching a more optimal path by using a station switching mode between two paths, and ensuring that the vehicle-mounted capacity and the time requirements for arriving at the target station are met in the switching optimization process;
s53, performing internal optimization on the route of each vehicle, mainly trying to exchange the sequence of two stations in the same vehicle route, evaluating whether the objective function value is reduced, if so, exchanging the sequence of the stations, otherwise, abandoning the exchange, and after trying for a preset number of times, terminating the algorithm and outputting the final route result.
Further, the step S6 includes the steps of:
s61, when the passenger successfully shares the car and the vehicle completes the order task, marking the car sharing order as successful, transmitting the vehicle information to the passenger small program through the mobile communication subsystem, and informing the passenger of the information related to the successful car sharing;
s62, when the passenger fails to share the car and the vehicle does not complete the order task, transmitting the failure information of the share-car order to the small program for the passenger through the communication subsystem, and informing the passenger of the information related to the share-car failure.
Referring to fig. 3, the present invention further provides a passenger demand response type car pooling scheduling system for intelligent buses, which is used to implement the passenger demand response type car pooling scheduling method for intelligent buses described above, specifically, the functions of each subsystem are described as follows:
the bus-mounted subsystem comprises a bus-mounted mobile communication terminal, a camera and passenger flow information acquisition equipment, is used for acquiring vehicle position information and operation state information in real time, comprehensively monitoring the running state of a vehicle, counting the number of passengers getting on or off the vehicle in real time and providing functions of vehicle running data acquisition and dispatching instruction issuing for the bus intelligent dispatching subsystem;
the intelligent bus dispatching subsystem comprises a dispatching management module and a customized bus mobile terminal module as follows:
the dispatching management module is used for automatically realizing the operation plan of the vehicle, realizing the real-time monitoring of the vehicle operation, performing holographic sensing and multi-source fusion on basic data of public transport vehicles, lines, stations and operators and dynamic data of GPS, scheduling and balance, constructing a public transport data brain center, and displaying a series of public transport service data through a visual large screen;
the customized bus mobile terminal module comprises a passenger APP, a passenger applet and an operator APP and is used for realizing the information service function of customized bus travel, and the information service function comprises passenger terminal service, operator terminal service, operation settlement monitoring and analyzing service, attendance rate statistical service and station passenger flow statistical service;
the bus station subsystem comprises an electronic station board and a video monitoring device and is used for realizing a station passenger flow volume counting function, a station monitoring function, a bus information inquiry function, an arrival pre-broadcasting function, a multimedia voice function and a station monitoring function;
the bus station subsystem comprises a bus parking lot and a bus command center large screen, wherein the bus parking lot comprises video monitoring equipment and inter-site networking equipment, and the command center large screen comprises an LED display system, an image and audio processing system and a transmission control system and is used for providing data support for the bus intelligent scheduling subsystem and the visual large screen;
and the mobile communication subsystem is used for connecting the bus-mounted subsystem, the bus intelligent scheduling subsystem, the bus station subsystem and the bus yard subsystem by using a wireless communication network.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by instructing relevant hardware by a computer program, and the program may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of simplicity of description, all possible combinations of the technical features in the above embodiments are not described, however, as long as there is no contradiction between the combinations of the technical features, the technical features should be considered as the scope of description in the present specification.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that various changes and modifications can be made by those skilled in the art without departing from the spirit of the invention, and these changes and modifications are all within the scope of the invention. Therefore, the protection scope of the present patent should be subject to the appended claims.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.
Claims (10)
1. A passenger demand response type carpooling scheduling method for an intelligent bus is characterized by comprising the following steps:
s1, a passenger logs in a small program for the passenger, a scheduling management module identifies the identity of the passenger, and the passenger can start the function of reserving the car-pooling trip after the identity of the passenger is successfully identified;
s2, passengers select car pooling travel service through the small program for the passengers, and input travel information to generate car pooling orders;
s3, the dispatching management module performs combination on a plurality of car sharing orders of passengers by operating a car sharing algorithm, plans the route of the orders and inquires available vehicles;
s4, converting the path search problem into an iterative problem of a station selection chain with the maximum attractive force between the calculation and the current station based on a gravitational model algorithm, and generating a feasible initial vehicle path solution;
s5, further optimizing the initial vehicle path solution through a heuristic algorithm;
and S6, issuing order tasks to the vehicles through the scheduling management module, and executing the car sharing function by the vehicles according to the route and station information output in the S5 so as to finish car sharing orders.
2. The passenger demand response type carpooling scheduling method for the intelligent bus as claimed in claim 1, wherein the S2 comprises the following steps:
s21, passengers reserve travel according to travel demands, and each passenger transmits information including a departure station, a destination station and expected arrival time of the passengers to a scheduling management module;
s22, the scheduling management module clusters all passengers according to the target station and the expected arrival time of each passenger and the principle that the actual arrival time is not later than the expected value of the passenger, the passengers with the same target station and the expected arrival time are grouped into one class, and the passengers with the same arrival time and arrival place are grouped into one class.
3. The passenger demand response type carpooling scheduling method for the intelligent bus as claimed in claim 1, wherein the S3 comprises the following steps:
s31, establishing a topology of a vehicle operation process, taking 30 minutes as a long decision period, regarding the long decision period, taking the topology of the vehicle operation process as a decision network, specifically abstracting a vehicle, a passenger boarding point, a passenger disembarking point and a period termination into nodes respectively, and connecting every two nodes through a directed arc, thereby obtaining the topology of the vehicle operation process;
s32, a target function is established, the optimization target mainly considers operation profit maximization and the trip experience of passengers to be optimal, the most key factor influencing the trip experience of the passengers is the waiting time of the passengers, the soft time window constraint of the waiting time of the passengers is considered, when the waiting time of the passengers exceeds a waiting time threshold value, the cost needing to be paid by the passengers is reduced, and meanwhile, a certain punishment value is generated on the operation profit.
4. The passenger demand response type carpooling scheduling method for the intelligent bus according to claim 3, characterized by comprising the following steps according to the topology of the vehicle operation process:
s311, setting the set of the nodes at the current position of the vehicle as K, setting the request set as R, wherein R1 represents that the vehicle is requested on the vehicle, R2 represents that the vehicle is not requested on the vehicle, and setting the set of the points at which the vehicle gets on or off as I, wherein I 1 - Indicating that the vehicle has got on or off the bus, i 2 + ，i 2 - Requesting departure and departure nodes, i, respectively representing binding with a vehicle 3 + ，i 3 - Respectively representing unallocated nodes requesting to get on or off the bus, setting a set of termination nodes as S, wherein the starting node of the directed arc falls in K and R, and the termination nodes fall in S;
s312, if the vehicle k passes through the directional arc from i to j, the variable x is changed ij k Is set to 1, A is used kr Indicating that vehicle k is assigned order r, using N i k Indicating the number of passengers of k vehicles to i node, let t i k Representing the time of arrival of vehicle k at pick-up node i, using p i Indicating a change in passengers at node i.
5. The passenger demand response type carpooling scheduling method for the intelligent bus as claimed in claim 4, wherein the following objective function maxZ is established in S32:
wherein, f (o) r ,d r ) Passenger of order r from o r Go to d r The fee to be paid, n r R from o for order r Go to d r Number of passengers, c k D (i, j) is the distance traveled from node i to node j, cp, for the unit travel cost of vehicle k kr Penalty charges incurred in executing order r for vehicle k.
6. The passenger demand response type carpooling scheduling method for the intelligent bus as claimed in claim 1, wherein the S4 comprises the following steps:
s41, determining departure stations of the vehicle, and randomly extracting one departure point of a vehicle k from the stations with the passenger getting-on demand, wherein the initial value of the k is 1;
s42, judging whether similar passengers are not served, if so, jumping to S43, otherwise, jumping to S45;
s43, searching a next station, finding a station X with the greatest attraction force with the current station from the boarding stations containing similar passengers, trying to join the station X into a path selection chain, and calculating the number of passengers of the vehicle after joining the station X and the time for the vehicle to directly reach a target station after joining the station X;
s44, judging whether the vehicle route is reasonable after joining the station X, if the number of passengers served by the current vehicle does not exceed the vehicle-mounted capacity and the time for reaching the target station does not exceed the time required by the passengers, jumping to S43 by taking the station X as a new starting point, and otherwise, jumping to S45;
and S45, judging whether all classes of passengers are scheduled for service, if no passenger is scheduled for service, scheduling the next vehicle, wherein k = k +1, and jumping to S41, otherwise, outputting all current initial paths, and finishing the algorithm for generating the initial vehicle path solution based on the gravity model.
7. The passenger demand response type carpooling scheduling method for the intelligent bus as claimed in claim 1, wherein the S5 comprises the following steps:
s51, firstly, balancing the station number among vehicles with the same time requirements for serving the target station and arriving at the target station, checking whether the vehicles have the condition of unbalanced station number serving, if so, transferring part of stations in the vehicle route needing to pass through the station with a large number of stations to the vehicle route with a small number of stations under the premise of ensuring to meet the vehicle-mounted capacity and the time requirements for arriving at the target station, and arranging a reasonable station sequence;
s52, trying to optimize the path between vehicles with the same time requirement for serving a target station and reaching the target station, mainly using a mode of exchanging stations between two paths of lines to search for a more optimal path, and ensuring that the vehicle-mounted capacity and the time requirement for reaching the target station are met in the exchange optimization process;
s53, performing internal optimization on the route of each vehicle, mainly trying to exchange the sequence of two stations in the same vehicle route, evaluating whether the objective function value is reduced, if so, exchanging the sequence of the stations, otherwise, abandoning the exchange, and after trying for a preset number of times, terminating the algorithm and outputting the final route result.
8. The passenger demand response type carpooling scheduling method for the intelligent bus as claimed in claim 1, wherein the S6 comprises the following steps:
s61, when the passenger successfully shares the car and the vehicle completes the order task, marking the car sharing order as successful, transmitting the vehicle information to the small program for the passenger through the mobile communication subsystem, and informing the passenger of the information related to the successful car sharing;
s62, when the passenger fails to share the car and the vehicle does not complete the order task, the failure information of the share-car order is transmitted to the small program for the passenger through the communication subsystem, and the passenger is informed of the information related to the share-car failure.
9. Passenger demand responsive car pooling dispatching system for intelligent public transportation for implementing the method according to any of claims 1-8, comprising the following subsystems:
the bus-mounted subsystem comprises a bus-mounted mobile communication terminal, a camera and passenger flow information acquisition equipment, is used for acquiring vehicle position information and operation state information in real time, comprehensively monitoring the running state of a vehicle, counting the number of passengers getting on or off the vehicle in real time and providing functions of vehicle running data acquisition and dispatching instruction issuing for the bus intelligent dispatching subsystem;
the intelligent bus dispatching subsystem comprises a dispatching management module and a customized bus mobile terminal module as follows:
the dispatching management module is used for automatically realizing the operation plan of the vehicle, realizing the real-time monitoring of the vehicle operation, performing holographic sensing and multi-source fusion on basic data of public transport vehicles, lines, stations and operators and dynamic data of GPS, scheduling and balance, constructing a public transport data brain center, and displaying a series of public transport service data through a visual large screen;
the customized bus mobile terminal module comprises a passenger APP, a passenger applet and an operator APP and is used for realizing the information service function of customized bus travel, and the information service function comprises passenger terminal service, operator terminal service, operation settlement monitoring and analyzing service, attendance rate statistical service and station passenger flow statistical service;
the bus station subsystem comprises an electronic station board and a video monitoring device and is used for realizing a station passenger flow volume counting function, a station monitoring function, a bus information inquiry function, an arrival pre-broadcasting function, a multimedia voice function and a station monitoring function;
the bus station subsystem comprises a bus parking lot and a bus command center large screen, wherein the bus parking lot comprises video monitoring equipment and inter-station networking equipment, and the command center large screen comprises an LED display system, an image and audio processing system and a transmission control system and is used for providing data support for the bus intelligent scheduling subsystem and the visual large screen;
and the mobile communication subsystem is used for connecting the bus-mounted subsystem, the bus intelligent scheduling subsystem, the bus station subsystem and the bus station subsystem by using a wireless communication network.
10. A storage medium having stored therein instructions executable by the system of claim 9, wherein the instructions when executed by a processor comprised by the system of claim 9 are adapted to implement a passenger demand responsive ride share scheduling method for intelligent busses according to any one of claims 1-8.
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