CN115481777A - Multi-line bus dynamic schedule oriented collaborative simulation optimization method, device and medium - Google Patents

Multi-line bus dynamic schedule oriented collaborative simulation optimization method, device and medium Download PDF

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CN115481777A
CN115481777A CN202210913339.5A CN202210913339A CN115481777A CN 115481777 A CN115481777 A CN 115481777A CN 202210913339 A CN202210913339 A CN 202210913339A CN 115481777 A CN115481777 A CN 115481777A
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郑亮
陶志雄
刘鹏杰
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Abstract

The invention discloses a collaborative simulation optimization method, equipment and medium for a multi-line bus dynamic schedule, wherein the method comprises the following steps: extracting passenger flow distribution rules of all stops from historical operation data of a plurality of bus lines; considering the random travel time between stations and the full load rate of the vehicles, constructing a dynamic departure schedule optimization model taking the total waiting time of passengers at all stations as a target, wherein variables to be optimized comprise departure intervals of adjacent train numbers of all lines; according to the passenger flow distribution rule of each station, carrying out simulation calculation on the total waiting time of passengers of all stations in the optimization model; taking the simulation calculation result as a fitness value to perform iteration of an optimization algorithm; and if the iteration termination condition is reached, generating an optimal schedule scheme. The invention can simulate and optimize the dynamic bus departure timetable which meets the cooperative transfer requirement of passengers at the transfer station and keeps the riding comfort according to the historical passenger flow arrival rule of a plurality of bus lines, thereby improving the bus service quality.

Description

Multi-line bus dynamic schedule oriented collaborative simulation optimization method, device and medium
Technical Field
The invention relates to the field of urban public transport and schedule optimization decision-making, in particular to a dynamic schedule collaborative simulation optimization method, equipment and medium for multi-line buses.
Background
The prior development of urban public transport is an important measure for relieving urban traffic jam and promoting the sustainable green and healthy development of cities, but most urban public transport systems still have the problems of low service level, low operation efficiency and the like due to the fact that the construction of public transport infrastructures is not perfect and public transport enterprises are lack of scientific public transport operation planning. Therefore, how to improve the bus service level through effective management becomes an urgent problem to be solved. The design of the bus schedule is a more complicated and important sub-problem in the bus scheduling process, and the reliable and flexible schedule can not only meet the requirements of passengers with uneven space and time and adapt to complex road traffic states, but also help to reduce the waiting time of the passengers and improve the bus service quality. Therefore, how to compile a scientific schedule becomes an important means for solving the bus operation problem.
In the past, the research and the use of the timetable are always in the stage of fixed departure interval and long-term unchanged repeated use, but because the vehicle is influenced by the real environment when running between stops, the passenger capacity of the vehicle is limited to a certain extent by the influence of riding comfort, and passengers also need to realize the transfer requirement between the bus numbers of different lines, so that the fixed timetable cannot adapt to the complex bus system.
Disclosure of Invention
Based on the problems of the existing bus route tables, the invention aims to provide a collaborative simulation optimization method, device and medium for a multi-route bus dynamic schedule, which can simulate and optimize the bus dynamic departure schedule meeting the collaborative transfer requirements of passengers at transfer stops and keeping the riding comfort according to the historical passenger flow arrival rule of a plurality of bus routes.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
a collaborative simulation optimization method for a multi-line bus dynamic schedule comprises the following steps:
s1, processing historical operation data of a plurality of bus lines, and extracting passenger flow distribution rules of all bus stops;
s2, considering the random travel time between stations and the full load rate of the vehicles, constructing a dynamic departure schedule optimization model taking the total waiting time of passengers at all stations as a target, wherein variables to be optimized comprise departure intervals between adjacent train numbers of all lines;
s3, carrying out simulation calculation on the total waiting time of passengers at all stations in the optimization model according to the passenger flow distribution rule of each station;
s4, taking departure intervals between adjacent train numbers of all lines as variables to be optimized, taking a simulation calculation result as a fitness value, and performing iteration of an optimization algorithm;
s5, judging whether an iteration termination condition is reached: if the iteration termination condition is reached, generating an optimal schedule scheme; otherwise, the step S3 is returned to.
Further, the historical operation data of the bus routes includes: the bus stop position information, the bus route information, the bus GPS track data and the passenger card swiping data.
Further, it is characterized in that the total waiting time of all station passengers includes: the waiting time of the passengers at all the non-transfer stations, the waiting time of the passengers at all the transfer stations and the waiting time of the non-transfer passengers.
Further, the dynamic departure schedule optimization model is as follows:
Figure BDA0003774630480000021
s.t
Figure DEST_PATH_FDA0003866071990000012
Figure BDA0003774630480000023
Figure BDA0003774630480000024
Figure BDA0003774630480000025
Figure BDA0003774630480000026
Figure BDA0003774630480000027
Figure BDA0003774630480000028
Figure BDA0003774630480000029
in the formula, l represents any bus line to be optimized in the departure schedule, r is the number of the bus lines, and a and b are any two of the bus lines; j represents any number of bus lines, m l Representing the total departure times of the line l in the optimization period; i denotes an arbitrary site, n l Represents the total number of unidirectional sites for line l; k ab Representing a set of transfer stations between two lines a, b,
Figure BDA0003774630480000031
Figure BDA0003774630480000032
representing the total number of transfer stations between the two lines a, b, k representing any transfer station in the lines;
Figure BDA0003774630480000033
indicates the time, f, at which the train number j of the line l arrives at any non-transfer station i li (t) represents the distribution rule of passengers arriving at the station i on the line l along with the time, namely the passenger flow distribution rule;
Figure BDA0003774630480000034
representing the number of passengers staying at the stop after the train number j of the line l reaches any non-transfer stop i; NO indicates the order of vehicles arriving at transfer station k,
Figure BDA0003774630480000035
Figure BDA0003774630480000036
respectively waiting for the number of the passengers at the station a at the transfer station k, the number of the passengers at the station a, the extra waiting time of the passengers at the station a, and the normal waiting time of all the passengers,
Figure BDA0003774630480000037
respectively waiting for the number of the passengers staying at the station, the number of the passengers not staying at the station, the extra waiting time of the passengers staying at the station and the normal waiting time of all the passengers at the transfer station k; logic variable delta ab =1 indicates that there is a transfer station between the lines a, b, otherwise δ ab ＝0；
Figure BDA0003774630480000038
Representing the departure interval between the train number j and the train number j +1 of the line l as a variable to be optimized;
Figure BDA0003774630480000039
representing the slack time for the train number j of line l to reach station i,
Figure BDA00037746304800000310
definition of
Figure BDA00037746304800000311
The value range of (a); s represents a bus stop set, L represents a bus route set, and J represents a route departure number set; d l 1 denotes the first moment of the line l, C denotes the optimization period,
Figure BDA00037746304800000312
represents the maximum section passenger flow of the line l in a certain period of time, Q l,v Indicating the nominal passenger capacity, mu, of the vehicle on the line l l Represents the planned vehicle load rate for route l;
Figure BDA00037746304800000313
the interval is the departure interval between any train number of the line l; n is a radical of * Representing a positive integer.
Further, the simulation calculation method in step S3 is:
(1) And (3) calculating the waiting time of the passengers at the non-transfer stations:
calculating the number of passengers getting off when the vehicle arrives at any station i and the remaining passenger capacity in the vehicle after the passengers get off; updating and calculating the number of the passengers staying at the station, the residual passenger capacity in the bus and the waiting time of the passengers at the non-transfer station after the passengers get on the bus;
(2) Calculating the waiting time of passengers at the transfer station:
generating a vehicle arrival sequence schedule Order _ k at the transfer station k; calculating the average passenger transfer ratio of the vehicles on the lines a, b at the station k
Figure BDA00037746304800000314
And
Figure BDA00037746304800000315
updating and calculating the class of the vehicles arriving at the transfer station k according to the class corresponding to the NO element in Order _ k
Figure BDA00037746304800000316
And
Figure BDA00037746304800000317
(3) And summing to calculate the waiting time of the passengers arriving at all stations of all the trains on all the lines.
Further, the number of the passengers staying at the station, the residual passenger capacity in the vehicle and the waiting time of the passengers at the non-transfer station after the passengers get on the vehicle are calculated and updated according to the following three conditions:
when in use
Figure BDA0003774630480000041
The method comprises the following steps:
Figure BDA0003774630480000042
when in use
Figure BDA0003774630480000043
The method comprises the following steps:
Figure BDA0003774630480000044
when in use
Figure BDA0003774630480000045
The method comprises the following steps:
Figure BDA0003774630480000046
in the formula (I), the compound is shown in the specification,
Figure BDA0003774630480000047
representing the remaining passenger capacity of the train number j of the line l after the passengers disembark and disembark at the non-transfer station i,
Figure BDA0003774630480000048
representing the extra waiting time of the dead passengers on route i at the non-transfer station i,
Figure BDA0003774630480000049
indicating the normal waiting time of all passengers on the line l at the non-transfer station i.
Further, the update calculates a transfer siteAt k is
Figure BDA00037746304800000410
And
Figure BDA00037746304800000411
Figure BDA00037746304800000412
specifically, calculation and updating are carried out according to two types of the line a and the line b:
(1) J-th vehicle of route a from initial station
Figure BDA00037746304800000413
Arrival at site k:
when in use
Figure BDA00037746304800000414
The method comprises the following steps:
Figure BDA00037746304800000415
when the temperature is higher than the set temperature
Figure BDA00037746304800000416
The method comprises the following steps:
Figure BDA00037746304800000417
when the temperature is higher than the set temperature
Figure BDA0003774630480000051
The method comprises the following steps:
Figure BDA0003774630480000052
updating:
Figure BDA0003774630480000053
(2) J-th vehicle of route b from initial station
Figure BDA0003774630480000054
Arrival at site k:
when in use
Figure BDA0003774630480000055
The method comprises the following steps:
Figure BDA0003774630480000056
when in use
Figure BDA0003774630480000057
The method comprises the following steps:
Figure BDA0003774630480000058
when in use
Figure BDA0003774630480000059
When the method is used:
Figure BDA00037746304800000510
updating:
Figure BDA00037746304800000511
further, the optimization algorithm is a genetic algorithm, a simulated annealing algorithm or a particle swarm algorithm.
An electronic device comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor is enabled to realize the collaborative simulation optimization method for the dynamic schedule of the multi-line bus according to any technical scheme.
A computer readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method for optimizing the collaborative simulation of the dynamic schedule of multi-line buses according to any one of the above technical solutions.
Advantageous effects
The multi-line bus dynamic schedule collaborative simulation optimization method can optimize and obtain the bus dynamic departure schedule which meets the collaborative transfer requirement of passengers at transfer stops and keeps the riding comfort according to the historical passenger flow arrival rule of a plurality of bus lines, thereby improving the bus service quality. In addition, the invention considers the random travel time among the buses, so that the dynamic departure timetable obtained by optimization can resist and adapt to the complex and random operation environment of the bus system, and the invention has practical value.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of a multi-route bus structure according to the present invention;
fig. 3 is a schematic diagram of the arrival situation and the passenger waiting state of the vehicle at the non-transfer station i on the route a;
fig. 4 is a schematic diagram of the arrival situation and the waiting state of passengers at the transfer station k between the line a and the line b according to the present invention.
Detailed Description
The following describes embodiments of the present invention in detail, which are developed based on the technical solutions of the present invention, and give detailed implementation manners and specific operation procedures to further explain the technical solutions of the present invention.
The embodiment provides a collaborative simulation optimization method for a multi-line bus dynamic schedule, which is shown in fig. 1 and comprises the following steps:
step 1, processing historical operation data of a plurality of bus lines, and extracting passenger flow distribution rules of all bus stops. Wherein, the public transit historical operation data includes: the bus station position information, the bus route information, the bus GPS track data, the passenger card swiping data and the like. The number distribution of passengers taking stations is fitted by adopting Poisson distribution, the arrival density function of the passengers is fitted by adopting normal distribution, and the distribution of the relaxation time among the buses is fitted by adopting logarithmically normal distribution.
And 2, considering the random travel time between stations and the full load rate of the vehicle, and constructing a dynamic departure schedule optimization model aiming at minimizing the total waiting time of passengers at all stations, wherein variables to be optimized comprise departure intervals between adjacent train numbers of all lines. The content comprises the following steps:
step 2.1: and constructing a scene of the problem of dynamic bus departure schedule compilation in a random environment. Referring to fig. 2, r bus routes are provided, and a mutual transfer relationship may exist between every two bus routes, n l Represents the total number of unidirectional stations of the line, m l Representing the total number of departures of the route during the optimization cycle. Suppose that the lines a and b are any two of the r lines, K ab Representing a set of transfer stations between two lines,
Figure BDA0003774630480000061
representing the total number of transfer stations between two lines, k representing any one transfer station in a line,
Figure BDA0003774630480000062
step 2.2: constructing a dynamic departure schedule optimization model, wherein the content comprises the following steps:
Figure BDA0003774630480000071
s.t
Figure 529156DEST_PATH_FDA0003866071990000012
Figure BDA0003774630480000073
Figure BDA0003774630480000074
Figure BDA0003774630480000075
Figure BDA0003774630480000076
Figure BDA0003774630480000077
Figure BDA0003774630480000078
Figure BDA0003774630480000079
in the formula, l represents any bus line to be optimized in the departure schedule, r is the number of the bus lines, and a and b are any two of the bus lines; j represents any number of bus lines, m l Representing the total departure times of the line l in the optimization period; i denotes an arbitrary site, n l Represents the total number of unidirectional sites for line l; k ab Representing a set of transfer stations between two lines a, b,
Figure BDA00037746304800000710
Figure BDA00037746304800000711
representing the total number of transfer stations between the two lines a, b, k representing any transfer station in the lines;
Figure BDA00037746304800000712
indicates the time, f, at which the train number j of the line l arrives at any non-transfer station i li (t) represents the distribution rule of passengers arriving at the station i at the line l along with the time, namely the passenger flow distribution rule, wherein the distribution rule is determined according to the specific actual case, and generally can be considered to be respectively at the peak, the low peak and the flat peakThe passengers arrive uniformly in the time period of (2), and the passenger density may be normally distributed in a certain time.
Figure BDA00037746304800000713
Representing the number of passengers staying at the stop after the train number j of the line l reaches any non-transfer stop i; NO indicates the order of vehicles arriving at transfer station k,
Figure BDA00037746304800000714
respectively the number of the passengers staying at the station, the number of the passengers not staying at the station, the extra waiting time of the passengers staying at the station and the normal waiting time of all the passengers,
Figure BDA0003774630480000081
respectively waiting for the number of the passengers staying at the station, the number of the passengers not staying at the station, the extra waiting time of the passengers staying at the station and the normal waiting time of all the passengers at the transfer station k; logical variable delta ab =1 indicates that there is a transfer station between the lines a, b, otherwise δ ab ＝0；
Figure BDA0003774630480000082
Representing the departure interval between the train number j and the train number j +1 of the line l as a variable to be optimized;
Figure BDA0003774630480000083
representing the slack time for the train number j of line l to reach station i,
Figure BDA0003774630480000084
definition of
Figure BDA0003774630480000085
The value range of (a); s represents a bus stop set, L represents a bus route set, and J represents a route departure number set; d l 1 denotes the first moment of the line l, C denotes the optimization period,
Figure BDA0003774630480000086
represents the maximum section passenger flow of the line l in a certain period of time, Q l,v Indicating the nominal passenger capacity, mu, of the vehicle on the line l l Represents the planned vehicle load rate for route l;
Figure BDA0003774630480000087
the interval is the departure interval between any train number of the line l; n is a radical of * Represents a positive integer; the formula (1) is an objective function to represent that the total waiting time of passengers is minimum, and the objective function is composed of two parts:
Figure BDA0003774630480000088
represents the total waiting time of passengers at non-transfer stations on the bus line,
Figure BDA0003774630480000089
and the total waiting time of passengers at transfer stations among the bus lines is represented. The formula (2) represents the range of departure intervals between adjacent train numbers on the same line, and the constraint condition can ensure that the sequence of arriving at a transfer station of the adjacent train numbers on the same line accords with the departure sequence under the condition that the departure intervals and the relaxation time are random, so that the phenomenon of overtaking in the station is prevented. Equation (3) represents the time when the train number j reaches the ith station, and the time when the train starts on the line l
Figure BDA00037746304800000810
Sum of all departure intervals before the train number j
Figure BDA00037746304800000811
Mean time of flight T between stations l i And relaxation time
Figure BDA00037746304800000812
And (4) forming. Equation (4) to handle the extremes: slack time if the j-1 st vehicle on the route l arrives at each station
Figure BDA00037746304800000813
All get
Figure BDA00037746304800000814
And slack time for the jth vehicle on line i to reach each station
Figure BDA00037746304800000815
All get
Figure BDA00037746304800000816
When the departure interval of the two vehicles is only equal to or larger than the requirement
Figure BDA00037746304800000817
Then, the situation that the jth vehicle exceeds the jth-1 vehicle at a certain station does not occur. . Equation (5) defines the range of departure time of the first line from the starting station, equation (6) represents that the departure time of the last line from the starting station is within the optimization period of the model, equation (7) represents that the departure time and the departure interval of the first line are both positive integer values, and equations (8) and (9) respectively define the calculation modes of the departure frequency and the optimization period of the line.
And 3, carrying out simulation calculation on the total waiting time of passengers at all the stations in the optimization model according to the passenger flow distribution rule of each station. The content comprises the following steps:
step 3.1, calculating the waiting time of passengers at the non-transfer station:
referring to fig. 3, according to the passenger flow distribution rule of each station and the average travel time between buses obtained in step 1, the passenger waiting time of the bus line non-transfer station in the total passenger waiting time in step 2 is calculated by using a numerical simulation method: firstly, calculating the number of passengers getting off when the vehicle arrives at any station i and the remaining passenger capacity in the vehicle after the passengers get off, and then updating and calculating the number of passengers staying at the station, the remaining passenger capacity in the vehicle and the waiting time of the passengers at the non-transfer station after the passengers get on the vehicle, which can be described as equations (10), (11) and (12):
when in use
Figure BDA0003774630480000091
The method comprises the following steps:
Figure BDA0003774630480000092
when in use
Figure BDA0003774630480000093
The method comprises the following steps:
Figure BDA0003774630480000094
when in use
Figure BDA0003774630480000095
The method comprises the following steps:
Figure BDA0003774630480000096
in the formula (I), the compound is shown in the specification,
Figure BDA0003774630480000097
representing the remaining passenger capacity of the train number j of the line l after the passengers disembark and disembark at the non-transfer station i,
Figure BDA0003774630480000098
representing the extra waiting time of the dead passengers on route i at the non-transfer station i,
Figure BDA0003774630480000099
the normal waiting time of all passengers on the line l at the non-transfer station i is represented by the train number j;
the expression (10) shows that the remaining passenger capacity in the current train is less than the number of the passengers staying at the station i after the last train leaves the station, and the passengers staying at the station i after the last train leaves the station still remain part of the passengers staying at the station i
Figure BDA00037746304800000910
Passengers who successively arrive at the station during this period are all detained. The expression (11) represents that the remaining passenger capacity in the current train is larger than or equal to the number of the passengers left in the station i after the last train leaves the station, but the number of the passengers left in the station i is
Figure BDA00037746304800000911
Successive multiplication to station during this periodThe guest has a partial stay. The expression (12) represents that the remaining passenger capacity in the current train is more than or equal to the sum of the number of the remaining passengers at the station i after the last train leaves the station and the number of the remaining passengers at the station i
Figure BDA00037746304800000912
The number of passengers arriving at the station in succession during this time, and no passengers are left at the station.
Step 3.2, calculating the waiting time of the passengers at the transfer station:
referring to fig. 4, the passenger waiting time of the transfer station between the bus lines in the total waiting time of the passengers in step 2 is calculated by using a numerical simulation method: firstly, a vehicle arrival sequence schedule Order _ k at a transfer station k is generated, and secondly, the passenger average transfer ratio of the vehicles on the lines a and b at the station k is calculated
Figure BDA0003774630480000101
And
Figure BDA0003774630480000102
then updating and calculating the vehicle class at the transfer site k according to the corresponding arriving vehicle class of the NO-th element in Order _ k
Figure BDA0003774630480000103
And
Figure BDA0003774630480000104
Figure BDA0003774630480000105
can be described as formula (13-20).
(1) J-th vehicle of route a from initial station
Figure BDA0003774630480000106
Arrival at site k:
when in use
Figure BDA0003774630480000107
The method comprises the following steps:
Figure BDA0003774630480000108
when in use
Figure BDA0003774630480000109
The method comprises the following steps:
Figure BDA00037746304800001010
when in use
Figure BDA00037746304800001011
The method comprises the following steps:
Figure BDA00037746304800001012
updating:
Figure BDA00037746304800001013
the expression (13-16) indicates that when the NO-th element in Order _ k corresponds to the vehicle
Figure BDA00037746304800001014
And (4) arriving at the station. The expression (13) represents that the remaining passenger capacity in the current train is less than the sum of the number of the remaining passengers staying at the stop and the number of the passengers not staying at the stop of the vehicle on the waiting line a after the last train leaves the stop. At the moment, the passengers staying at the station and not staying at the station of the vehicle on the waiting line a are still partially staying after the previous vehicle leaves the station
Figure BDA00037746304800001015
During this period, the passengers of the vehicles on the line a are waited for staying all. The expression (14) represents that the remaining passenger capacity in the current train is greater than or equal to the sum of the number of the remaining passengers staying at the stop and the number of the passengers staying at the stop on the waiting line a after the last train leaves the stop, but a part of the remaining passengers is
Figure BDA00037746304800001016
During this time periodAnd waiting for passengers of the vehicle on the line a successively. The expression (15) represents that the remaining passenger capacity in the current secondary vehicle is more than or equal to the sum of the number of the remaining passengers in the left station and the number of the passengers in the non-left station of the vehicles on the waiting line a after the last vehicle leaves the station
Figure BDA0003774630480000111
The number of passengers waiting for the vehicle on the route a is added up in succession to the station.
(2) J-th vehicle of route b from initial station
Figure BDA0003774630480000112
Arrival at site k:
when in use
Figure BDA0003774630480000113
The method comprises the following steps:
Figure BDA0003774630480000114
when in use
Figure BDA0003774630480000115
The method comprises the following steps:
Figure BDA0003774630480000116
when in use
Figure BDA0003774630480000117
The method comprises the following steps:
Figure BDA0003774630480000118
updating:
Figure BDA0003774630480000119
the expression (17-20) indicates that when the NO element in Order _ k corresponds to the vehicle
Figure BDA00037746304800001110
And (4) arriving at the station. Equation (17) represents that the remaining passenger capacity in the current train is less than the sum of the number of the remaining passengers at the left stop and the number of the passengers at the non-stop on the waiting line b after the last train leaves the stop. At the moment, the passengers staying at the station and not staying at the station of the vehicle on the waiting line b are still partially staying after the previous vehicle leaves the station
Figure BDA00037746304800001111
During this period, the passengers of the vehicles on the waiting line b are all detained. The expression (18) represents that the remaining passenger capacity in the current train is greater than or equal to the sum of the number of the remaining passengers staying at the stop and the number of the passengers staying at the stop on the waiting line b after the last train leaves the stop, but a part of the remaining passengers is
Figure BDA00037746304800001112
During which time successive stops are waiting for passengers of the vehicle on line b. The expression (19) represents that the remaining passenger capacity in the current secondary vehicle is more than or equal to the sum of the number of the remaining passengers in the left station and the number of the passengers in the non-left station of the vehicles on the waiting line b after the last vehicle leaves the station
Figure BDA00037746304800001113
The number of passengers waiting for the vehicle on the line b successively arrives at the station during this time.
And 4, taking departure intervals between adjacent train numbers of all lines as variables to be optimized, taking a simulation calculation result as a fitness value, and performing iteration of an optimization algorithm. Each iteration of the optimization algorithm needs to calculate the total waiting time of passengers obtained by a group of departure intervals, and the total waiting time is used as the fitness value of the iteration of the current round to participate in the selection transformation of the optimization algorithm. The optimization algorithm is heuristic algorithms such as a genetic algorithm, a simulated annealing algorithm, a particle swarm algorithm and the like. In this embodiment, it is preferable to use a genetic algorithm to select, cross, and mutate different chromosome solutions according to their fitness values.
And 5, judging whether an iteration termination condition is reached (which can be set according to the maximum iteration number and/or a convergence threshold): if the iteration termination condition is reached, generating an optimal schedule scheme, namely an departure interval scheme of each train number of all bus lines in a certain specific time period; otherwise, returning to the step 3, and carrying out new simulation calculation on the waiting time of the passengers at each station.
The invention also provides an electronic device, which comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor realizes the multi-line bus dynamic schedule oriented collaborative simulation optimization method in the embodiment.
A computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for optimizing multi-route bus dynamic schedule collaborative simulation according to the above embodiments.
The above embodiments are preferred embodiments of the present application, and those skilled in the art can make various changes or modifications without departing from the general concept of the present application, and such changes or modifications should fall within the scope of the claims of the present application.

Claims (10)

1. A collaborative simulation optimization method for a multi-line bus dynamic schedule is characterized by comprising the following steps:
s1, processing historical operation data of a plurality of bus lines, and extracting passenger flow distribution rules of all bus stops;
s2, considering the random travel time between stations and the full load rate of the vehicles, constructing a dynamic departure schedule optimization model taking the total waiting time of passengers at all stations as a target, wherein variables to be optimized comprise departure intervals between adjacent train numbers of all lines;
s3, carrying out simulation calculation on the total waiting time of passengers at all stations in the optimization model according to the passenger flow distribution rule of each station;
s4, taking departure intervals between adjacent train numbers of all lines as variables to be optimized, taking a simulation calculation result as a fitness value, and performing iteration of an optimization algorithm;
s5, judging whether an iteration termination condition is reached: if the iteration termination condition is reached, generating an optimal schedule scheme; otherwise, the step S3 is returned to.
2. The method of claim 1, wherein the historical operational data of the bus route comprises: the bus stop position information, the bus route information, the bus GPS track data and the passenger card swiping data.
3. The method of claim 1, wherein the total waiting time for all station passengers comprises: the waiting time of the passengers at all the non-transfer stations, the waiting time of the passengers at all the transfer stations and the waiting time of the non-transfer passengers.
4. The method of claim 1, wherein the dynamic departure schedule optimization model is:
Figure RE-FDA0003866071990000011
s.t
Figure RE-FDA0003866071990000012
Figure RE-FDA0003866071990000013
Figure RE-FDA0003866071990000014
Figure RE-FDA0003866071990000015
Figure RE-FDA0003866071990000016
Figure RE-FDA0003866071990000017
Figure RE-FDA0003866071990000021
Figure RE-FDA0003866071990000022
in the formula, l represents any bus line to be optimized in the departure schedule, r is the number of the bus lines, and a and b are any two of the bus lines; j represents any number of bus lines, m l Representing the total departure times of the line l in the optimization period; i denotes an arbitrary site, n l Represents the total number of unidirectional sites for line l; k ab Representing a set of transfer stations between two lines a, b,
Figure RE-FDA0003866071990000023
representing the total number of transfer stations between the two lines a, b, k representing any transfer station in the lines;
Figure RE-FDA0003866071990000024
indicates the time, f, at which the train number j of the line l arrives at any non-transfer station i li (t) represents the distribution rule of passengers arriving at the station i on the line l along with the time, namely the passenger flow distribution rule;
Figure RE-FDA0003866071990000025
representing the number of passengers staying at the stop after the train number j of the line l reaches any non-transfer stop i; NO indicates the number of vehicles arriving at transfer station kThe sequence of the method is as follows,
Figure RE-FDA0003866071990000026
Figure RE-FDA0003866071990000027
respectively the number of the passengers staying at the station, the number of the passengers not staying at the station, the extra waiting time of the passengers staying at the station and the normal waiting time of all the passengers,
Figure RE-FDA0003866071990000028
respectively waiting for the number of the passengers staying at the station, the number of the passengers not staying at the station, the extra waiting time of the passengers staying at the station and the normal waiting time of all the passengers at the transfer station k; logical variable delta ab =1 indicates that there is a transfer station between the lines a, b, otherwise δ ab ＝0；
Figure RE-FDA0003866071990000029
Representing the departure interval between the train number j and the train number j +1 of the line l as a variable to be optimized;
Figure RE-FDA00038660719900000210
representing the slack time for the train number j of line l to reach station i,
Figure RE-FDA00038660719900000211
definition of
Figure RE-FDA00038660719900000212
The value range of (a); s represents a bus stop set, L represents a bus route set, and J represents a route departure number set;
Figure RE-FDA00038660719900000213
indicating the first moment of the line l, C the optimization period,
Figure RE-FDA00038660719900000214
represents the maximum section passenger flow of the line l in a certain period of time, Q l,v Represents the nominal passenger capacity, mu, of the vehicle on the line l l Represents the planned vehicle load rate for route l;
Figure RE-FDA00038660719900000215
the interval is the departure interval between any train number of the line l; n is a radical of * Representing a positive integer.
5. The method of claim 4, wherein the simulation calculation method of step S3 is:
(1) And (3) calculating the waiting time of the passengers at the non-transfer stations:
calculating the number of passengers getting off when the vehicle arrives at any station i and the remaining passenger capacity in the vehicle after the passengers get off; updating and calculating the number of the passengers staying at the station, the residual passenger capacity in the bus and the waiting time of the passengers at the non-transfer station after the passengers get on the bus;
(2) Calculating the waiting time of passengers at the transfer station:
generating a vehicle arrival sequence schedule Order _ k at the transfer station k; calculating the average passenger transfer ratio of the vehicles on the lines a, b at the station k
Figure FDA0003774630470000031
And
Figure FDA0003774630470000032
updating and calculating the class of the vehicles arriving at the transfer station k according to the corresponding NO element in Order _ k
Figure FDA0003774630470000033
And
Figure FDA0003774630470000034
(3) And summing to calculate the waiting time of the passengers arriving at all stations of all the trains on all the lines.
6. The method according to claim 5, wherein the updating calculates the number of passengers at the station of the non-transfer after the passengers get on the bus, the remaining passenger capacity in the bus and the waiting time of the passengers, and the updating is calculated according to the following three conditions:
when in use
Figure FDA0003774630470000035
The method comprises the following steps:
Figure FDA0003774630470000036
when in use
Figure FDA0003774630470000037
The method comprises the following steps:
Figure FDA0003774630470000038
when in use
Figure FDA0003774630470000039
The method comprises the following steps:
Figure FDA00037746304700000310
in the formula (I), the compound is shown in the specification,
Figure FDA00037746304700000311
representing the remaining passenger capacity of the train number j of the line l after the passengers disembark and disembark at the non-transfer station i,
Figure FDA00037746304700000312
representing the extra waiting time of the dead passengers on route i at the non-transfer station i,
Figure FDA00037746304700000313
the number j of the lines l is shown when all passengers at the non-transfer station i normally waitAnd (3) removing the solvent.
7. The method of claim 5, wherein the updating calculates at transfer site k
Figure FDA00037746304700000314
Figure FDA00037746304700000315
And
Figure FDA00037746304700000316
specifically, calculation updating is carried out according to two types of a line a and a line b:
(1) J-th vehicle of route a from initial station
Figure FDA00037746304700000317
Arrival at site k:
when in use
Figure FDA0003774630470000041
The method comprises the following steps:
Figure FDA0003774630470000042
when in use
Figure FDA0003774630470000043
When the method is used:
Figure FDA0003774630470000044
when in use
Figure FDA0003774630470000045
The method comprises the following steps:
Figure FDA0003774630470000046
updating:
Figure FDA0003774630470000047
(2) J-th vehicle of route b from initial station
Figure FDA0003774630470000048
Arrival at site k:
when in use
Figure FDA0003774630470000049
The method comprises the following steps:
Figure FDA00037746304700000410
when in use
Figure FDA00037746304700000411
The method comprises the following steps:
Figure FDA00037746304700000412
when in use
Figure FDA00037746304700000413
When the method is used:
Figure FDA00037746304700000414
updating:
Figure FDA0003774630470000051
8. the method of claim 5, wherein the optimization algorithm is a genetic algorithm, a simulated annealing algorithm, or a particle swarm algorithm.
9. An electronic device comprising a memory and a processor, the memory having stored therein a computer program, wherein the computer program, when executed by the processor, causes the processor to carry out the method according to any one of claims 1 to 8.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1 to 8.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115641722A (en) * 2022-12-22 2023-01-24 吉林大学 Regular bus trip service system and method based on dynamic waiting time

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115641722A (en) * 2022-12-22 2023-01-24 吉林大学 Regular bus trip service system and method based on dynamic waiting time
CN115641722B (en) * 2022-12-22 2023-04-28 吉林大学 Class travel service system and method based on dynamic waiting time

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