CN114757510A - Bus dispatching method considering maximum passenger capacity limit under epidemic situation propagation - Google Patents

Bus dispatching method considering maximum passenger capacity limit under epidemic situation propagation Download PDF

Info

Publication number
CN114757510A
CN114757510A CN202210333485.0A CN202210333485A CN114757510A CN 114757510 A CN114757510 A CN 114757510A CN 202210333485 A CN202210333485 A CN 202210333485A CN 114757510 A CN114757510 A CN 114757510A
Authority
CN
China
Prior art keywords
bus
traveler
time
travel
route
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210333485.0A
Other languages
Chinese (zh)
Inventor
黄岩
李宗志
周备
张生瑞
王剑坡
廖国美
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Changan University
Original Assignee
Changan University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Changan University filed Critical Changan University
Priority to CN202210333485.0A priority Critical patent/CN114757510A/en
Publication of CN114757510A publication Critical patent/CN114757510A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Tourism & Hospitality (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Theoretical Computer Science (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Development Economics (AREA)
  • Operations Research (AREA)
  • Educational Administration (AREA)
  • Game Theory and Decision Science (AREA)
  • Quality & Reliability (AREA)
  • Primary Health Care (AREA)
  • Health & Medical Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Traffic Control Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a bus scheduling method considering maximum passenger capacity limit under epidemic spread, which can realize the aim of optimizing urban regional bus fleet scheduling and maximum passenger capacity limit on a bus under different epidemic infection rates, inoculation rates, crowd susceptibility degrees and travel demand levels so as to minimize passenger travel cost and minimize newly increased infection cure cost.

Description

Bus dispatching method considering maximum passenger capacity limit under epidemic situation propagation
Technical Field
The invention relates to the technical field of bus dispatching, in particular to a bus dispatching method considering maximum passenger capacity limitation under epidemic situation propagation.
Background
The urban bus is a main undertaker for commuting and traveling, the bus traveling is low in cost, large in passenger capacity and preferential in crossing signal passing, and the urban bus has obvious advantages in energy conservation, emission reduction and traffic jam relief compared with private bus traveling. But simultaneously, during the epidemic outbreak, the bus becomes potential epidemic situation transmission place because of the closed indoor environment of the bus, the gathering of a large number of susceptible people for a long time and a plurality of public contact surfaces which are possibly exposed, such as handrails, railings, seats and the like. For example, in 1 month of 2020, 68 people in Ningbo Zhejiang occupy the same bus, and 24 people are infected in the bus because there is a COVID-19 virus infected person on the bus. Since symptoms of an infected person often appear after a certain period of latency, it is difficult to take the necessary medical isolation measures before the infected person gets on the car. Thus, when the infester and susceptible population are on one vehicle at a time, the epidemic will spread without obvious signs.
In order to reduce the risk of spreading epidemic situations on buses, government transportation departments or public transport companies generally adopt measures of reducing the frequency of bus departure, limiting the maximum passenger capacity on the buses, closing bus lines or parts of bus stations, implementing strict in-vehicle environment disinfection, not using non-air circulation air conditioners in the buses, keeping window opening ventilation, requiring temperature measurement of passengers getting on the buses, wearing masks and the like. However, these restrictions, while potentially alleviating the spread of the epidemic, also cause a significant reduction in urban public transportation service capabilities. From the fairness perspective, completely closing the public transportation service will bring a great negative impact on the travel of residents. On the one hand, the public transport is the main trip mode of the old and the low income group, especially the low income group is engaged in the relevant industry of physical labor mostly, often does not possess the condition of working at home during the epidemic situation, and closing public transport service will influence their trip efficiency greatly. On the other hand, closing the bus line, reducing the departure frequency and setting the maximum number of passengers on the bus directly leads to the reduction of the bus running capacity, and further causes the great reduction of the passenger ticket income of the bus, which not only increases the burden of subsidizing the bus by government finance, but also faces the situation of a bankrupt officer to the bus company without government financial support.
In order to improve the level of bus operation service, researchers have conducted numerous studies such as bus lane setting, signal prioritization, route optimization, departure frequency optimization, bus customization, etc. in the past decades from the viewpoints of reducing the total time of passengers' trips, the total cost of bus operation, the bus operation emission, minimizing the maximum number of buses, etc. In the result verification part of the scheme, a static bus distribution model is mostly adopted, and macroscopic bus operation indexes are considered. However, these models and methods often fail to take into account the issues of bus operation optimization and precautions that involve different infection rates, vaccination rates, demand levels, and personal characteristics of the traveling person in the spread of the epidemic.
Disclosure of Invention
Aiming at the defects in the prior art, the bus scheduling method considering the limitation of the maximum passenger capacity under the condition of epidemic situation propagation solves the problem that the bus operation optimization and prevention measures under different infection rates, inoculation rates, demand levels and personal characteristics of travelers cannot be considered in the conventional bus scheduling method.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a bus dispatching method considering maximum passenger capacity limit under epidemic situation propagation comprises the following steps:
S1, constructing and solving an optimal traveler travel path objective function in the research area to obtain travel tracks and travel states of travelers in the research area and the running state of the bus route;
s2, obtaining the number of passengers on the bus of each bus time of the bus line and the running length of the bus line passing through an epidemic situation outbreak point according to the travel track and the travel state of each traveler in the research area and the running state of the bus line;
s3, calculating a bus route sequencing index according to the number of people on the bus of each bus number of the bus route and the running length of the bus route passing through an epidemic situation outbreak point, and sequencing the bus routes to obtain a bus route sequencing result;
s4, according to the bus route sequencing result, adopting a genetic algorithm to construct a population, simulating individuals in the population for multiple times through Monte Carlo, constructing a virus propagation probability model, optimizing the population, and obtaining an urban bus dispatching scheme under epidemic situation propagation.
Further, in step S1, the objective function of the traveler' S travel optimal path is:
Figure BDA0003573774300000031
wherein K is a departure time selected by the traveler, K is a set of all departure times, OD is a starting and ending point of the traveler, OD is a set of starting and ending points, P is a travel route selected by the traveler, P is a set of travel routes,
Figure BDA0003573774300000032
In order to select the travel time of the travelers (k, od) on the travel route p, the travelers (k, od) start and end at the time of k,
Figure BDA0003573774300000033
when used for the shortest path among travelers (k, od),
Figure BDA0003573774300000034
is the state of whether the traveler (k, od) uses the travel route p.
The beneficial effects of the above further scheme are: the method realizes the selection of the travel mode and the route of each traveler in the road network according to the travel time of the shortest route, namely, each traveler can not change own route unilaterally to obtain any user balance state saving travel time, thereby loading the traveler into the road network more truly.
Further, the travel time of the traveler (k, od) who selects the travel route p
Figure BDA0003573774300000035
The method comprises the steps of road section passing time, intersection waiting time, middle station staying time and station waiting time;
the calculation formula of the road section passing time is as follows:
Figure BDA0003573774300000036
wherein the content of the first and second substances,
Figure BDA0003573774300000037
passing time of travelers (k, od) on the section a,/aIs the length of the section a, /)bIn order to be the length of the bus,
Figure BDA0003573774300000038
number of vehicles in line for travelers (k, od) on road section a, vaThe average running speed v of the bus on the road section aa，eQueuing the bus in the road section a to get out of speed;
The calculation formula of the intersection waiting time is as follows:
Figure BDA0003573774300000041
wherein the content of the first and second substances,
Figure BDA0003573774300000042
waiting time, T, for travelers (k, od) at the intersection j direction dj，d，gIs a green light time window in the direction d of the intersection j,
Figure BDA0003573774300000043
the arrival time of the traveler (k, od) at the intersection j in the direction d,
Figure BDA0003573774300000044
to time of arrival
Figure BDA0003573774300000045
In green light time window Tj，d，gIn the interior of said container body,
Figure BDA0003573774300000046
the green light is turned on for the time when the traveler (k, od) is nearest to the j direction d of the intersection;
the formula for calculating the residence time of the middle station is as follows:
Figure BDA0003573774300000047
wherein the content of the first and second substances,
Figure BDA0003573774300000048
for the stay time of the travelers (k, od) at the bus station u,
Figure BDA0003573774300000049
the number of passengers (k, od) getting on the bus at the bus station u, tATime of getting on for a single passenger, tBIs the time for a single passenger to get off,
Figure BDA00035737743000000410
the number of alighting persons (k, od) at the bus station u;
the calculation formula of the waiting time of the station is as follows:
Figure BDA00035737743000000411
wherein the content of the first and second substances,
Figure BDA00035737743000000412
for waiting time of travelers (k, od) at bus station u,
Figure BDA00035737743000000413
for the departure time of the travelers (k, od) at the bus station u,
Figure BDA00035737743000000414
is the arrival time of the traveler (k, od) at the bus stop u.
Further, the traveler travel optimal path objective function satisfies the following constraints:
the first constraint, maximum number of transfers for the traveler to select a path:
Figure BDA00035737743000000415
wherein P is a travel path selected by a traveler, P is a travel path set, and S uIs a set of bus stations, and is characterized in that,
Figure BDA00035737743000000416
whether a traveler (K, OD) selects the boarding state of a bus station u in a travel route p or not is judged, K is the departure time selected by the traveler, K is a set of all departure times, OD is the starting and ending point of the traveler, and OD is a set of the starting and ending point;
second constraint, maximum passenger capacity limit of bus route:
Figure BDA0003573774300000051
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003573774300000052
the number of passengers getting on the bus with the bus line r numbered v at the bus station u,
Figure BDA0003573774300000053
whether a traveler (k, od) is in the bus station u of the bus with the bus line r number v, alpha is the social distance limiting parameter on the bus, C is the design capacity of the bus, SrIs a collection of bus lines r, Sr,vSet of bus numbers for bus route r, SuIs a bus station set;
the third constraint, the maximum time for the traveler to wait for the bus:
Figure BDA0003573774300000054
wherein P is a travel route selected by a traveler, P is a travel route set,
Figure BDA0003573774300000055
whether or not a traveler (k, od) uses the state of the travel route p,
Figure BDA0003573774300000056
for waiting time of travelers (k, od) at bus station u,
Figure BDA0003573774300000057
whether or not a traveler (k, od) selects the boarding state of the bus station u on the travel route p,
Figure BDA0003573774300000058
maximum travel time acceptable for travelers (k, od);
the fourth constraint, total number of unliked passengers and total traffic demand in the study area minus the total number of passengers on board, and the total number of unliked passengers needs to be greater than or equal to 0:
Figure BDA0003573774300000059
Wherein QunmetQ is the total number of passengers not boarding the vehicle, Q is the total traffic demand in the study area,
Figure BDA00035737743000000510
the state of whether a traveler (K, OD) uses a travel path P is determined, P is the travel path selected by the traveler, P is a travel path set, K is the departure time selected by the traveler, K is all the departure time sets, OD is the starting and ending point of the traveler, and OD is the starting and ending point set;
and a fifth constraint, wherein the used travel path is the shortest path in the travel path set P:
Figure BDA00035737743000000511
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00035737743000000512
in order to select the travel time of the travelers (k, od) of the travel route p,
Figure BDA00035737743000000513
is used for the shortest path among travelers (k, od).
Further, the formula for calculating the bus route ranking index in step S3 is as follows:
Figure BDA0003573774300000061
wherein Rank is a bus route sorting index,
Figure BDA0003573774300000062
number of passengers on bus with r number v at bus station u, WinfectWeight of cost for the cure of infection, NrThe running length f of the bus route r passing through the outbreak point of epidemic situationr，oIs the state of whether the bus line r passes through an epidemic situation outbreak point or not, SrIs a collection of bus routes r.
The beneficial effects of the above further scheme are: the invention calculates the passing risk value W by simultaneously considering the running length of the bus line r passing through the epidemic situation outbreak point and the infection curing cost infect·Nr·fr，oThe more people are on the vehicle, the risk value Winfect·Nr·fr，oThe lower the bus route, the higher the bus route ranking index.
Further, the step S4 includes the following sub-steps:
s41, building a fleet scale objective function according to the bus line sequencing result, iteratively calculating the adjustable fleet scale, and building each different individual in the initial population in the genetic algorithm by adopting the fleet scale calculated each time until the maximum population number is reached;
s42, carrying out multiple simulation on individuals in the initial population reaching the maximum population number by adopting Monte Carlo, selecting the trip information of an infected person and an inoculated person from travelers in each simulation, inputting a virus propagation probability model, and calculating the virus propagation probability under each simulation;
s43, calculating the expected total travel cost of each individual in the initial population reaching the maximum population number according to all the simulated virus propagation probabilities;
s44, selecting individuals with the maximum elite population number to form a surrogate elite population according to the expected total travel cost of each individual in the initial population, and obtaining the urban public transport vehicle dispatching scheme with the given maximum passenger capacity limit under epidemic situation propagation.
The beneficial effects of the above further scheme are: according to the method, Monte Carlo simulation under the combination of different infectors, inoculators and susceptible people is carried out on each individual in the initial population, the risk of virus propagation in the public transport network under the corresponding combination is calculated, and the expected travel total cost of each individual is finally obtained. Based on this, the elite population of the current generation was selected from all individuals.
Further, in step S41, the bus route ranking result is a result of descending ranking according to the bus route ranking index, the bus routes that can increase the number of vehicles are found in the top 50% of the bus route ranking results by using the fleet scale objective function, the bus routes that can decrease the number of vehicles are found in the last 50% of the bus route ranking results by using the fleet scale objective function, and the decreased number of vehicles is equal to the increased number of vehicles;
the fleet size objective function is:
Figure BDA0003573774300000071
wherein vehrFor the number of vehicles to be adjusted, crThe turnaround time of the public transport line r, fmaxAt maximum departure frequency, nrThe current number of vehicles of the bus route r;
the beneficial effects of the above further scheme are: according to the result of the descending order of the bus route sorting indexes, the adjustable vehicle number of the bus routes is sequentially calculated, the adjustable vehicle number is at most 2, and if no adjustable vehicle number exists in the routes, the number is 0.
The fleet size objective function satisfies the following constraints:
the first constraint is that the bus headway time in the fleet scale of the bus route meets the following requirements:
hmin≤hr≤hmax
wherein h isminIs the minimum bus head time interval hmaxAt the maximum bus head time interval h rThe headway of the bus route r;
the second constraint, the total number of bus vehicles in the bus route does not exceed the total number of available vehicles:
N＝Nc+Nsor
Figure BDA0003573774300000072
nr＝T/hr
Wherein N is the total number of the buses in the bus route, NcFor operating the number of vehicles, NsNumber of vehicles in reserve, SrIs a collection of bus routes r, nrIs the current number of vehicles, h, of the bus route rrThe time interval of the bus line r is the headway time, and T is given time.
Further, the virus propagation probability model in step S42 is:
Figure BDA0003573774300000081
Figure BDA0003573774300000082
Figure BDA0003573774300000083
Figure BDA0003573774300000084
Figure BDA0003573774300000085
wherein the content of the first and second substances,
Figure BDA0003573774300000086
bus station for bus line r numbered v
Figure BDA0003573774300000087
The process to bus stop u leads to the expectation of newly increased numbers of infected persons,
Figure BDA0003573774300000088
bus station for bus line r numbered v
Figure BDA0003573774300000089
The amount of airborne disease in the process of arriving at bus station u,
Figure BDA00035737743000000810
bus station for bus line r numbered v
Figure BDA00035737743000000811
The amount of pathogens that spread by surface contact during transit to bus stop u,
Figure BDA00035737743000000812
bus station for bus line r numbered v
Figure BDA00035737743000000813
The number of susceptible people in the process of arriving at a bus station u, theta is a mask filtering coefficient, rho is individual respiration,
Figure BDA00035737743000000814
bus station for bus line r numbered v
Figure BDA00035737743000000815
The stay time in the process of arriving at the bus station u,
Figure BDA00035737743000000816
bus station for bus line r numbered v
Figure BDA00035737743000000817
The ventilation volume of the bus in the process of arriving at a bus station u, C is the designed capacity of the bus, V is the volume of the bus, P is the travel route selected by the traveler, P is the travel route set, K is the departure time selected by the traveler, K is all the departure time sets, OD is the starting and ending point of the traveler, OD is the starting and ending point set,
Figure BDA00035737743000000818
the condition of whether the traveler (k, od) is an infected person, the traveler (k, od) being the traveler starting at the time k and ending at the time k, gamma being the ratio of the exhaled viruses remaining on the surface, and sk，odIs a susceptibility parameter of a traveler (k, od),
Figure BDA0003573774300000091
bus with r number v for bus line at bus station
Figure BDA0003573774300000099
The point in time of the departure is,
Figure BDA0003573774300000092
the time point when the bus with the number v of the bus line r arrives at the bus station u, qk，odNumber of pathogens of pathogenic size exhaled by travelers (k, od)Amount, Sr，uIs a set of bus stations of a bus line r,
Figure BDA0003573774300000093
is the state of whether a traveler (k, od) is at a bus stop u of a bus with the bus line r number v,
Figure BDA0003573774300000094
is the state of whether the traveler (k, od) uses the travel route p.
The beneficial effects of the above further scheme are: the method considers two modes of air transmission and contact transmission in calculating the virus transmission risk, and truly simulates the process of transmitting the virus along with a carrier in the public transportation susceptible population.
Further, the formula for calculating the expected total travel cost of each individual in the initial population reaching the maximum population number in step S43 is as follows:
Figure BDA0003573774300000095
Figure BDA0003573774300000096
wherein TC is the expected total travel cost, K is the departure time selected by the traveler, K is the set of all the departure times, OD is the starting and ending point of the traveler, OD is the set of the starting and ending point,
Figure BDA0003573774300000097
VOT for shortest path utilization in travelers (k, od)k，odIs the travel time value, W, of the traveler (k, od)infectFor the weight of the cure cost of the disease, E () is the expectation function, NnewCOT is the cure cost of the disease for newly increased number of infected people.
Further, in step S44, the probability that the individuals with the largest elite population number are selected in the initial population is:
Figure BDA0003573774300000098
wherein, p (i) is the probability that the ith individual in the initial population is selected, and tc (i) is the total travel cost of the individual i in the initial population.
In conclusion, the beneficial effects of the invention are as follows: the bus dispatching method considering the maximum passenger capacity limit under epidemic spread can achieve the aim of optimizing urban regional bus fleet dispatching and on-board maximum passenger capacity limit under different epidemic infection rates, inoculation rates, population susceptibility degrees and travel demand levels so as to achieve the minimum passenger travel cost and the minimum newly-increased infection cure cost.
Drawings
FIG. 1 is a flow chart of a method of bus scheduling that considers maximum passenger capacity constraints under epidemic propagation;
FIG. 2 is a regional public transportation network diagram of an embodiment;
FIG. 3 is a 0 m on-board social distance limit, resulting in a bus ride map;
FIG. 4 is a 0.6 meter on-board social distance limit, resulting in a bus occupancy map;
FIG. 5 is a 1.0 meter on-board social distance limit, resulting in a bus available for riding;
fig. 6 shows the social distance limit on the bus of 1.4 m, and the obtained bus available map.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, a method for dispatching buses considering maximum passenger capacity limit under epidemic propagation includes the following steps:
s1, constructing and solving an optimal trip path objective function of travelers in the research area to obtain a trip track and a trip state of each traveler in the research area and an operation state of a bus route;
In step S1, the objective function of the traveler' S travel optimal path is:
Figure BDA0003573774300000101
wherein K is a departure time selected by the traveler, K is a set of all departure times, OD is a starting and ending point of the traveler, OD is a set of starting and ending points, P is a travel route selected by the traveler, P is a set of travel routes,
Figure BDA0003573774300000111
in order to select the travel time of the travelers (k, od) on the travel route p, the travelers (k, od) start and end at the time of k,
Figure BDA0003573774300000112
when used for the shortest path among travelers (k, od),
Figure BDA0003573774300000113
is the state of whether the traveler (k, od) uses the travel route p.
Travel time of the traveler (k, od) selecting the travel route p
Figure BDA0003573774300000114
The method comprises the steps of road section passing time, intersection waiting time, middle station staying time and station waiting time;
the calculation formula of the road section passing time is as follows:
Figure BDA0003573774300000115
wherein the content of the first and second substances,
Figure BDA0003573774300000116
passing time of travelers (k, od) on the section a,/aIs the length of the section a, /)bIn order to be the length of the bus,
Figure BDA0003573774300000117
number of vehicles in line for travelers (k, od) on road section a, vaThe average running speed v of the bus on the road section aa，eQueuing the bus in the road section a to get out of speed;
the calculation formula of the intersection waiting time is as follows:
Figure BDA0003573774300000118
wherein the content of the first and second substances,
Figure BDA0003573774300000119
waiting time, T, for travelers (k, od) at the intersection j direction d j，d，gIs a green light time window in the direction d of the intersection j,
Figure BDA00035737743000001110
the arrival time of travelers (k, od) in the direction d of the intersection j,
Figure BDA00035737743000001111
to the time of arrival
Figure BDA00035737743000001112
In green light time window Tj，d，gIn the interior of the container body,
Figure BDA00035737743000001113
the green light is turned on for the time of a traveler (k, od) in the j direction d of the intersection;
the formula for calculating the residence time of the middle station is as follows:
Figure BDA00035737743000001114
wherein the content of the first and second substances,
Figure BDA00035737743000001115
for the stay time of the travelers (k, od) at the bus station u,
Figure BDA00035737743000001116
the number of passengers (k, od) getting on the bus at the bus station u, tATime of getting on for a single passenger, tBIs the time for a single passenger to get off,
Figure BDA00035737743000001117
the number of alighting persons (k, od) at the bus station u;
the calculation formula of the waiting time of the station is as follows:
Figure BDA0003573774300000121
wherein the content of the first and second substances,
Figure BDA0003573774300000122
for waiting time of travelers (k, od) at bus station u,
Figure BDA0003573774300000123
for the departure time of the travelers (k, od) at the bus station u,
Figure BDA0003573774300000124
is the arrival time of the traveler (k, od) at the bus stop u.
The optimal path objective function for the traveler's trip meets the following constraints:
the first constraint, maximum number of transfers for the traveler to select a path:
Figure BDA0003573774300000125
wherein P is a travel path selected by a traveler, P is a travel path set, and SuIs a set of bus stations, and is provided with a bus station,
Figure BDA0003573774300000126
selecting the boarding state of a bus station u in a trip path p for a traveler (K, OD), wherein K is a departure time selected by the traveler, K is a set of all departure times, OD is a starting and ending point of the traveler, and OD is a set of the starting and ending point;
Second constraint, maximum passenger capacity limit of bus route:
Figure BDA0003573774300000127
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003573774300000128
the number of passengers getting on the bus with the bus line r numbered v at the bus station u,
Figure BDA0003573774300000129
whether a traveler (k, od) is in the bus station u of the bus with the bus line r number v, alpha is the social distance limiting parameter on the bus, C is the design capacity of the bus, SrIs a collection of bus lines r, Sr,vSet of bus numbers, S, for bus route ruIs a bus station set;
the third constraint, the maximum time for the traveler to wait for the bus:
Figure BDA00035737743000001210
wherein P is a travel route selected by a traveler, P is a travel route set,
Figure BDA00035737743000001211
whether or not a traveler (k, od) uses the state of the travel route p,
Figure BDA00035737743000001212
for traveling outWaiting time of the person (k, od) at the bus station u,
Figure BDA00035737743000001213
whether or not a traveler (k, od) selects the boarding state of the bus station u on the travel route p,
Figure BDA00035737743000001214
maximum travel time acceptable for travelers (k, od);
the fourth constraint, total number of unliked passengers and total traffic demand in the study area minus the total number of passengers on board, and the total number of unliked passengers needs to be greater than or equal to 0:
Figure BDA0003573774300000131
wherein Q isunmetThe total number of passengers who have not got on the bus, Q is the total traffic demand in the research area,
Figure BDA0003573774300000132
The state of whether a traveler (K, OD) uses a travel path P is determined, P is the travel path selected by the traveler, P is a travel path set, K is the departure time selected by the traveler, K is all the departure time sets, dd is the starting and ending point of the traveler, and OD is the starting and ending point set;
and a fifth constraint, wherein the used travel path is the shortest path in the travel path set P:
Figure BDA0003573774300000133
wherein the content of the first and second substances,
Figure BDA0003573774300000134
in order to select the travel time of the travelers (k, od) of the travel route p,
Figure BDA0003573774300000135
is time-consuming for the shortest path among travelers (k, od).
S2, obtaining the number of passengers on the bus of each bus number of the bus route and the running length of the bus route passing through an epidemic situation outbreak point according to the running track and the running state of each traveler in the research area and the running state of the bus route;
s3, calculating bus route sequencing indexes according to the number of people on the buses of each bus number of the bus route and the running length of the bus route passing through an epidemic situation outbreak point, and sequencing the bus routes to obtain bus route sequencing results;
in step S3, the formula for calculating the bus route ranking index is as follows:
Figure BDA0003573774300000136
wherein Rank is a bus route sorting index,
Figure BDA0003573774300000137
number of passengers on bus with r number v at bus station u, W infectWeight for the cost of cure of infection, NrIs the running length of the bus route r passing through the epidemic situation outbreak point, fr,oIs the state of whether the bus line r passes through an epidemic situation explosion point SrIs a collection of bus routes r.
S4, according to the bus route sequencing result, a population is constructed by adopting a genetic algorithm, individuals in the population are simulated for multiple times through Monte Carlo, a virus propagation probability model is constructed, the population is optimized, and the urban bus dispatching scheme with the given maximum passenger capacity limit under epidemic situation propagation is obtained.
The step S4 includes the following sub-steps:
s41, building a fleet scale objective function according to the bus line sequencing result, iteratively calculating the adjustable fleet scale, and building each different individual in the initial population in the genetic algorithm by adopting the fleet scale calculated each time until the maximum population number is reached;
when the initial population is generated, the method adopts the method of transmitting tn bus linesReal number encoding of vehicle frequency, i.e. individuals f in the initial populationp，fp＝{f1，f2，...，ftnIn which f1，f2，...，ftnNumber of vehicles, f, of bus route 1, 2pThe individuals are obtained according to the bus route sequencing result.
After calculating an adjustable fleet size using a fleet size objective function, and after adjusting fleet size, using the current fleet size to construct each different individual in the initial population, e.g., f p0＝{f1，f2H, then fp1＝{f1+veh1，f2-veh2In which fp0Is the 0 th generation, i.e. the initial population, fp1Is the 1 st generation population, f1Number of vehicles, f, for bus route 12Number of vehicles, veh, for bus route 21Adjustable number of vehicles, veh, for bus route 12An adjustable number of vehicles for the bus route 2.
Until the initial population reaches the maximum population number GsAt the same time, it is ensured that the individual buses in the initial population are different, that is
Figure BDA0003573774300000141
And is provided with
Figure BDA0003573774300000142
Wherein F is the initial population reaching the maximum population number,
Figure BDA0003573774300000143
g of initial population F to reach maximum population numbersAnd (4) individuals.
Step S41, the bus route sorting result is a result of descending sorting according to the bus route sorting index, the bus route capable of increasing the number of vehicles is found in the first 50% of the bus route sorting results by adopting the fleet scale objective function, the bus route capable of reducing the number of vehicles is found in the second 50% of the bus route sorting results by adopting the fleet scale objective function, and the reduced number of vehicles is equal to the increased number of vehicles;
the fleet size objective function is:
Figure BDA0003573774300000151
wherein, vehrTo the number of vehicles that can be adjusted, crThe turnaround time of the bus route r, fmaxAt maximum departure frequency, nrThe current number of vehicles of the bus route r;
The fleet size objective function satisfies the following constraints:
the first constraint is that the bus headway time interval in the fleet scale of the bus route meets the following requirements:
hmin≤hr≤hmax
wherein h isminIs the minimum bus head interval, hmaxIs the maximum bus head time interval hrThe headway of the bus route r;
and a second constraint, wherein the total number of the buses in the bus route is not more than the total number of the available vehicles:
N＝Nc+Nsor
Figure BDA0003573774300000152
nr＝T/hr
Wherein N is the total number of the buses in the bus route, NcTo run the number of vehicles, NsNumber of vehicles to be reserved, SrIs a set of bus routes r, nrIs the current number of vehicles, h, of the bus route rrThe time interval of the bus line r is the time interval of the bus, and T is given time.
S42, carrying out multiple simulation on individuals in the initial population reaching the maximum population number by adopting Monte Carlo, selecting the trip information of an infected person and an inoculated person from travelers in each simulation, inputting a virus propagation probability model, and calculating the virus propagation probability under each simulation;
for each individual F in the population FpnTo perform Monte CarloAnd (6) simulating. In each simulation, the expected total number of virus infectors in each OD traveler set is obtained according to infection rate, the residence density of the departure cell and the distance from the explosion point in the travel population, the virus infectors are randomly selected in each OD traveler set, and then the inoculators are randomly selected in the rest susceptible population according to the inoculation rate.
The virus propagation probability model in step S42 is:
Figure BDA0003573774300000161
Figure BDA0003573774300000162
Figure BDA0003573774300000163
Figure BDA0003573774300000164
Figure BDA0003573774300000165
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003573774300000166
bus station for bus line r numbered v
Figure BDA0003573774300000167
The process to bus stop u leads to the expectation of new numbers of infected people,
Figure BDA0003573774300000168
bus station for bus line r numbered v
Figure BDA0003573774300000169
The amount of airborne pathogens in the process of arriving at bus station u,
Figure BDA00035737743000001610
bus station for bus line r numbered v
Figure BDA00035737743000001611
The amount of pathogens that spread by surface contact during transit to bus stop u,
Figure BDA00035737743000001612
bus station for bus line r numbered v
Figure BDA00035737743000001613
The number of susceptible people in the process of arriving at a bus station u, theta is a mask filtering coefficient, rho is individual respiration,
Figure BDA00035737743000001614
bus station for bus line r numbered v
Figure BDA00035737743000001615
The stay time in the process of arriving at the bus station u,
Figure BDA00035737743000001616
bus station for bus line r numbered v
Figure BDA00035737743000001617
The ventilation volume of the bus in the process of arriving at a bus station u, C is the designed volume of the bus, V is the volume of the bus, P is the travel route selected by the traveler, P is the travel route set, K is the departure time selected by the traveler, K is the set of all departure times, OD is the starting and ending point of the traveler, OD is the set of the starting and ending point,
Figure BDA00035737743000001618
the state of whether the traveler (k, od) is an infected person or not, the traveler (k, od) is the traveler whose starting and ending points at the time of k are od, and gamma To exhale the proportion of virus remaining on the surface, sk,odIs a susceptibility parameter of a traveler (k, od),
Figure BDA00035737743000001619
bus with r number v for bus line at bus station
Figure BDA00035737743000001620
The point in time of the departure is,
Figure BDA00035737743000001621
the time point when the bus with the number v of the bus line r arrives at the bus station u, qk,odThe number of pathogenic pathogens exhaled by the travelers (k, od), Sr,uIs a set of bus stations of a bus line r,
Figure BDA00035737743000001622
is the state whether the traveler (K, od) is at the bus stop u of the bus with the bus line r number v,
Figure BDA0003573774300000171
is the state of whether the traveler (k, od) uses the travel route p.
S43, calculating the expected total travel cost of each individual in the initial population reaching the maximum population number according to all the simulated virus propagation probabilities;
in step S43, according to all the simulated virus propagation probabilities, the expected new cure cost and the expected new infectious population of the individual are obtained, and then the expected total travel cost of the individual in the initial population up to the maximum population is calculated.
The formula for calculating the expected total travel cost of each individual in the initial population up to the maximum population number in step S43 is as follows:
Figure BDA0003573774300000172
Figure BDA0003573774300000173
wherein TC is the expected total travel cost, K is the departure time selected by the traveler, K is the set of all the departure times, OD is the starting and ending point of the traveler, OD is the set of the starting and ending point,
Figure BDA0003573774300000174
VOT for shortest path utilization in travelers (k, od)k,odIs the travel time value, W, of the traveler (k, od)infectFor weighting the cure cost of a disease, E () is the expectation function, NnewCOT is the curing cost of the disease for newly infected people.
In the present embodiment, the expectation function is an existing expectation function.
S44, selecting individuals with the maximum elite population number to form a surrogate elite population according to the expected total travel cost of each individual in the initial population, and obtaining the urban public transport vehicle dispatching scheme with the given maximum passenger capacity limit under epidemic situation propagation. The scheme for dispatching the buses of each line by individuals in the elite population of the present generation.
Step S44 specifically includes: the maximum passenger capacity limit may be set in various situations, and under each maximum passenger capacity limit, the expected total travel cost of each individual in the initial population is obtained through the contents of steps S1 to S3, and the individual group cost surrogate elite population corresponding to the minimum expected total travel cost is selected. Further, the elite population of the present generation under each maximum passenger capacity limit condition, namely, the vehicle dispatching scheme under different maximum passenger capacity limit conditions can be obtained.
In step S44, the individual with the largest elite population number is selected to constitute the elite population of the belt using the roulette rule.
Step S44 the probability that the individuals with the maximum elite population number are selected in the initial population is:
Figure BDA0003573774300000181
wherein p (i) is the probability of the ith individual in the initial population being selected, and tc (i) is the total travel cost of the individual i in the initial population.
The invention is further illustrated with reference to the following figures and examples:
in this example, epidemic propagation and bus optimization cases in an actual network are considered, and a research area is shown in fig. 2. The epidemic situation outbreaks around the hospital node 12, considering setting 1 km and 2 km as high risk, medium risk and low risk area demarcation ranges, the infection rate varies from 1% to 30%, the inoculation rate varies from 0% to 90%, and the travelers have extra cost for taking the bus route passing through the node 12. In the area, 33 signalized intersections, 12 bus lines and bus speed limit of 40 kilometers per hour are counted, and in order to simplify the scene, a bus station is assumed to be arranged at a position close to the intersections. Travel demand is divided into three stages, peak, critical and peak, wherein approximately 50% of travelers' destinations or departure points pass through 12, 18, 19, 20 and 23 nodes.
The buses can adopt social distance limits of 0 meter, 0.6 meter, 1.0 meter and 1.4 meter, and the number of passengers under each limit is shown in figures 3-6.
FIG. 3 shows a 52 person nuclear load, wherein 26 persons have seats and 26 persons stand; in fig. 4 35 persons are nuclear-loaded, of which 16 have seats and 19 stand; in fig. 5 18 persons are nuclear-loaded, 11 of which have seats and 7 stand; in fig. 6, 11 persons are nuclear-loaded, 9 persons having seats and 2 persons standing.
Under the above-mentioned circumstances
A1, constructing the optimal travel path objective function of all bus travelers in the region according to travel demands, OD distribution, bus route layout, departure frequency, epidemic situation outbreak points, travel cost of each road section of a road network, bus boarding restrictions and other traffic control conditions of the current situation of the research region.
A2, obtaining the number of passengers on the bus of each bus time and the running length of the bus line passing through an epidemic situation outbreak point of the bus line according to the travel track and the travel state of each traveler in the research area and the running state of the bus line;
a3, calculating a bus route sequencing index according to the number of people on the bus of each bus number of the bus route and the running length of the bus route passing through an epidemic situation outbreak point and combining the regional epidemic situation curing cost weight (2.5 is taken in the embodiment), and sequencing the bus routes to obtain a bus route sequencing result;
a4, constructing a population by adopting a genetic algorithm according to the bus route sequencing result;
And A5, generating an initial population. Bus line fleet f according to the current schemep0Taking {12,10,10,6,10,10,12,10,8,10,12,6} as a reference, randomly selecting a bus line in the first 50% according to the sequencing result until a bus line capable of increasing the size of the fleet is found, randomly selecting a bus line in the last 50% until a bus line capable of reducing the size of the fleet is found, and ensuring that the increased fleet size is consistent with the reduced fleet size, so as to generate an initial population;
and A6, performing Monte Carlo simulation. For each bus route scheme individual F in the population Fpnepsilon.F, and carrying out Monte Carlo simulation. In each simulation, the expected total number of virus infectors in each OD traveler set is obtained according to infection rate, residence density of departure cells and distance from the explosion point in a trip group, and the trip density and the infection risk at the explosion point distance of different cells are shown in table 1.
TABLE 1
Figure BDA0003573774300000191
It is assumed that the relevant attributes in the virus propagation probability model and the total travel cost model obey beta distribution, and specific parameters are shown in table 2.
TABLE 2
Figure BDA0003573774300000201
Virus infected individuals were randomly selected under each OD panelist set, followed by random selection of vaccinees among the remaining susceptible population according to vaccination rate. Substituting travel information of an infected person and an inocuator into a virus propagation probability model by using the travel information as input data, and performing virus propagation The probability model calculates the virus propagation probability under the simulation of the invention, and the virus propagation probability model continuously executes Monte Carlo simulation until the maximum simulation times are reached 100 times. According to all the simulated results under the invention, the expected new cure cost and the expected new infectious population of the scheme are obtained. From this, the respective schemes F in the population F can be calculatedpnDesired total travel cost.
And A7, generating a new population. And selecting the individual group cost belt elite population with the maximum elite population quantity according to the expected total travel cost of each individual by using a roulette rule.
A8, convergence judgment. And judging whether the genetic algorithm reaches the maximum algebra or whether the calculation time exceeds a given threshold value. If so, the optimal scheme is found, and the result is output. Otherwise, steps A7-A8 continue to be performed until the algorithm converges.
The maximum passenger capacity limit is first divided into several groups as constants, such as 0 meter, 0.6 meter, 1.0 meter, 1.4 meter. Then, under each kind of maximum passenger carrying limit, the optimization of the bus dispatching is carried out. Thus, the vehicle dispatching optimization scheme under all the passenger carrying limit schemes is obtained. And finally, comparing the results of the vehicle dispatching schemes under the passenger carrying limits, namely the total cost, and obtaining the maximum passenger carrying capacity limit and the vehicle dispatching scheme with the minimum corresponding total cost. The maximum passenger capacity and vehicle dispatching in the scheme are the optimal scheme.
And A9, analyzing results. Because the infection rate, the inoculation rate, the travel demand and the combination types of the social distances on different vehicles are more, in order to reduce the calculation time, the four social distances on the vehicles can be respectively calculated under the other three mixed combinations. Three optimization scenarios are set, namely optimization of vehicle dispatch only (scenario 1), optimization of vehicle social distance only (scenario 2) and optimization of both (scenario 3). The optimization results of the present invention under different optimization scenarios are shown in table 3. It can be seen that after optimization, the total travel cost of the area under all circumstances is reduced.
TABLE 3
Figure BDA0003573774300000211
Based on step a9, vehicle dispatch and on-vehicle social distance schedules at different infection and vaccination rates can be evaluated as shown in table 4. It can be seen that the method of the present invention provides a solution to the measures to be taken at different infection and vaccination rates. It can be seen from the results that while regimen 1 reduces the overall cost by between 1.6% and 6.4% for all infection and vaccination rate combinations, the overall cost improvement for regimens 2 and 3 varies with the infection and vaccination rate combinations, with regimen 3 performing slightly better than regimen 2 overall. At an infection rate of 1%, the vaccination rate was below 20%, and regimen 2 and 3 were only effective when the social distance was below 0.6 m. When the infection rate is 5%, the inoculation rate is below 30%, the scheme 3 can achieve the reduction of the total cost by 24.7-52.6% at a social distance of 1.0 m, the inoculation rate is 40-50%, and the scheme 3 can reduce the total cost by 7.6-15.7% at a social distance of 0.6 m. When the infection rate is 10%, the inoculation rate is less than 20%, the scheme 3 can reduce 62.6-69.1% under the social distance of 1.4 m, and when the inoculation rate is 20-40%, the scheme 3 can reduce the total cost by 30.9-54.4% under the social distance of 1.0 m, the inoculation rate is 50-60%, and the scheme 2 can reduce the total cost by 5.6-16.1% under the social distance of 0.6 m. When the infection rate is 15%, the inoculation rate is below 30%, the scheme 3 can achieve 61.8% -75.4% of total cost reduction by using a social distance of 1.4 meters, and when the inoculation rate is 30% -40%, the scheme 3 can achieve 4.7-18.1% of total cost reduction by using a social distance of 0.6 meters. When the infection rate is 20% and the inoculation rate is 0-30%, the total cost reduction of 65.1-78.2% can be achieved by implementing the scheme 3 at a social distance of 1.4 meters, when the inoculation rate is 40-50%, the total cost income of 39.1-53.8% can be achieved by adopting the scheme 3 at a social distance of 1.0 meters, and the total cost reduction of 16.8% can be achieved by adopting the scheme 3 at a social distance of 0.6 meters at an inoculation rate of 50%. Thus, it can be seen that the total cost obtained by adopting different schemes will show significant differences under different combinations.
TABLE 4
Figure BDA0003573774300000221
Figure BDA0003573774300000231

Claims (10)

1. A bus dispatching method considering maximum passenger capacity limit under epidemic situation propagation is characterized by comprising the following steps:
s1, constructing and solving an optimal traveler travel path objective function in the research area to obtain travel tracks and travel states of travelers in the research area and the running state of the bus route;
s2, obtaining the number of passengers on the bus of each bus time of the bus line and the running length of the bus line passing through an epidemic situation outbreak point according to the travel track and the travel state of each traveler in the research area and the running state of the bus line;
s3, calculating a bus route sequencing index according to the number of people on the bus of each bus number of the bus route and the running length of the bus route passing through an epidemic situation outbreak point, and sequencing the bus routes to obtain a bus route sequencing result;
s4, according to the bus route sequencing result, adopting a genetic algorithm to construct a population, simulating individuals in the population for multiple times through Monte Carlo, constructing a virus propagation probability model, optimizing the population, and obtaining an urban bus dispatching scheme giving maximum passenger capacity limitation under epidemic situation propagation.
2. The method for dispatching buses under consideration of maximum passenger capacity restriction under epidemic propagation according to claim 1, wherein the objective function of the optimal path for travelers to travel in step S1 is as follows:
Figure FDA0003573774290000011
Wherein K is the departure time selected by the traveler, K is the set of all departure times, OD is the starting and ending point of the traveler, and OD isA starting point set and an ending point set, wherein P is a travel route selected by a traveler, P is a travel route set,
Figure FDA0003573774290000012
to select the travel time of a traveler (k, id) on a travel route p, the traveler (k, id) is the traveler whose starting point and ending point at time k are od,
Figure FDA0003573774290000013
when used for the shortest path among travelers (k, od),
Figure FDA0003573774290000014
is the state of whether the traveler (k, od) uses the travel route p.
3. The method according to claim 2, wherein the travel time of the travelers (k, od) who select the travel route p is selected as the bus dispatching method considering the maximum passenger capacity limit under epidemic propagation
Figure FDA0003573774290000021
The method comprises the steps of road section passing time, intersection waiting time, middle station staying time and station waiting time;
the calculation formula of the road section passing time is as follows:
Figure FDA0003573774290000022
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003573774290000023
passing time of travelers (k, od) on the section a,/aIs the length of the section a, /)bIn order to be the length of the bus,
Figure FDA0003573774290000024
number of vehicles in line for travelers (k, od) on road section a, vaThe average running speed v of the bus on the road section aa,eQueuing the bus in the road section a to get out of speed;
the calculation formula of the intersection waiting time is as follows:
Figure FDA0003573774290000025
Wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003573774290000026
waiting time, T, for travelers (k, od) in the direction d of the intersection jj,d,gIs a green light time window in the direction d of the intersection j,
Figure FDA0003573774290000027
the arrival time of travelers (k, od) in the direction d of the intersection j,
Figure FDA0003573774290000028
to the time of arrival
Figure FDA0003573774290000029
In green light time window Tj,d,gIn the interior of the container body,
Figure FDA00035737742900000210
the green light is turned on for the time when the traveler (k, od) is nearest to the j direction d of the intersection;
the formula for calculating the residence time of the middle station is as follows:
Figure FDA00035737742900000211
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA00035737742900000212
for the stay time of travelers (k, od) at bus station u,
Figure FDA00035737742900000213
the number of passengers (k, od) getting on the bus at the bus station u, tATime of getting on for a single passenger, tBIs the time for a single passenger to get off,
Figure FDA00035737742900000214
the number of alighting persons (k, od) at the bus station u;
the calculation formula of the waiting time of the station is as follows:
Figure FDA00035737742900000215
wherein the content of the first and second substances,
Figure FDA00035737742900000216
for waiting time of travelers (k, od) at bus station u,
Figure FDA00035737742900000217
for the departure time of the travelers (k, od) at the bus station u,
Figure FDA00035737742900000218
is the arrival time of the traveler (k, od) at the bus stop u.
4. The method according to claim 3, wherein the traveler travel optimal path objective function satisfies the following constraints:
the first constraint, maximum number of transfers for the traveler to select a path:
Figure FDA0003573774290000031
wherein P is a travel path selected by a traveler, P is a travel path set, and S uFor bus stationsThe collection of the data is carried out,
Figure FDA0003573774290000032
selecting the boarding state of a bus station u in a trip path p for a traveler (K, OD), wherein K is a departure time selected by the traveler, K is a set of all departure times, OD is a starting and ending point of the traveler, and OD is a set of the starting and ending point;
second constraint, maximum passenger capacity limit of bus route:
Figure FDA0003573774290000033
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003573774290000034
the number of passengers getting on the bus with the bus line r numbered v at the bus station u,
Figure FDA0003573774290000035
whether a traveler (k, od) is in the bus station u of the bus with the bus line r number v, alpha is the social distance limiting parameter on the bus, C is the design capacity of the bus, SrIs a set of bus lines r, Sr,vSet of bus numbers, S, for bus route ruIs a bus station set;
the third constraint, the maximum time for the traveler to wait for the bus:
Figure FDA0003573774290000036
wherein P is a travel route selected by a traveler, P is a travel route set,
Figure FDA0003573774290000037
whether or not a traveler (k, od) uses the state of the travel route p,
Figure FDA0003573774290000038
for waiting time of travelers (k, od) at bus station u,
Figure FDA0003573774290000039
whether or not a traveler (k, od) selects the boarding state of the bus station u on the travel route p,
Figure FDA00035737742900000310
maximum travel time acceptable for travelers (k, od);
the fourth constraint, total number of unliked passengers and total traffic demand in the study area minus the total number of passengers on board, and the total number of unliked passengers needs to be greater than or equal to 0:
Figure FDA0003573774290000041
Wherein QunmetQ is the total number of passengers not boarding the vehicle, Q is the total traffic demand in the study area,
Figure FDA0003573774290000042
the state of whether a traveler (K, OD) uses a travel route P is determined, P is the travel route selected by the traveler, P is a travel route set, K is the departure time selected by the traveler, K is all departure time sets, OD is the starting and ending point of the traveler, and OD is the starting and ending point set;
and a fifth constraint, wherein the used travel path is the shortest path in the travel path set P:
Figure FDA0003573774290000043
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003573774290000044
in order to select the travel time of the travelers (k, od) of the travel route p,
Figure FDA0003573774290000045
is used for the shortest path among travelers (k, od).
5. The method for dispatching buses with consideration of maximum passenger capacity limit under epidemic propagation as claimed in claim 1, wherein the formula for calculating the bus route ranking index in step S3 is as follows:
Figure FDA0003573774290000046
wherein Rank is a bus route sorting index,
Figure FDA0003573774290000047
number of passengers on bus with r number v at bus station u, WinfectWeight for the cost of cure of infection, NrThe running length f of the bus route r passing through the outbreak point of epidemic situationr,oIs the state of whether the bus line r passes through an epidemic situation explosion point SrIs a collection of bus routes r.
6. The method as claimed in claim 1, wherein the step S4 includes the following sub-steps:
S41, according to the bus line sequencing result, constructing a fleet scale objective function to iteratively calculate the adjustable fleet scale, and adopting the fleet scale calculated each time to construct each different individual in the initial population in the genetic algorithm until the maximum population number is reached;
s42, carrying out multiple simulation on individuals in the initial population reaching the maximum population number by adopting Monte Carlo, selecting the trip information of an infected person and an inoculated person from travelers in each simulation, inputting a virus propagation probability model, and calculating the virus propagation probability under each simulation;
s43, calculating the expected total travel cost of each individual in the initial population reaching the maximum population number according to all the simulated virus propagation probabilities;
and S44, selecting the individuals with the maximum elite population quantity to form the surrogate elite population according to the expected total travel cost of each individual in the initial population, and obtaining the urban bus dispatching scheme with the given maximum passenger capacity limit under epidemic situation propagation.
7. The method as claimed in claim 6, wherein the step S41 is implemented by arranging the bus route ranking results in descending order according to the bus route ranking index, finding bus routes capable of increasing the number of vehicles in the top 50% of the bus route ranking results by using the fleet scale objective function, finding bus routes capable of reducing the number of vehicles in the last 50% of the bus route ranking results by using the fleet scale objective function, wherein the reduced number of vehicles is equal to the increased number of vehicles;
The fleet size objective function is:
Figure FDA0003573774290000051
wherein vehrFor the number of vehicles to be adjusted, crThe turnaround time of the public transport line r, fmaxAt maximum departure frequency, nrThe current number of vehicles of the bus route r;
the fleet size objective function satisfies the following constraints:
the first constraint is that the bus headway time interval in the fleet scale of the bus route meets the following requirements:
hmin≤hr≤hmax
wherein h isminIs the minimum bus head interval, hmaxIs the maximum bus head time interval hrThe time interval of the bus line r is the time interval of the bus;
and a second constraint, wherein the total number of the buses in the bus route is not more than the total number of the available vehicles:
N＝Nc+Nsor
Figure FDA0003573774290000052
nr＝T/hr
Wherein N is the total number of the buses in the bus route, NcTo run the number of vehicles, NsNumber of vehicles to be reserved, SrIs a set of bus routes r, nrIs the current number of vehicles, h, of the bus route rrThe time interval of the bus line r is the time interval of the bus, and T is given time.
8. The method for dispatching buses with consideration of maximum passenger capacity limit under epidemic propagation as claimed in claim 6, wherein the probability model of virus propagation in step S42 is:
Figure FDA0003573774290000061
Figure FDA0003573774290000062
Figure FDA0003573774290000063
Figure FDA0003573774290000064
Figure FDA0003573774290000065
wherein the content of the first and second substances,
Figure FDA0003573774290000066
is a public transportBus station with r number v
Figure FDA0003573774290000067
The process to bus stop u leads to the expectation of newly increased numbers of infected persons,
Figure FDA0003573774290000068
Bus station for bus line r numbered v
Figure FDA0003573774290000069
The amount of airborne pathogens in the process of arriving at bus station u,
Figure FDA00035737742900000610
bus station for bus line r numbered v
Figure FDA00035737742900000611
The amount of pathogens that spread by surface contact during transit to bus stop u,
Figure FDA00035737742900000612
bus station for bus line r numbered v
Figure FDA00035737742900000613
The number of susceptible people in the process of arriving at a bus station u, theta is a mask filtering coefficient, rho is individual respiration,
Figure FDA00035737742900000614
bus station for bus line r numbered v
Figure FDA00035737742900000615
The stay time in the process of arriving at the bus station u,
Figure FDA00035737742900000616
bus station for bus line r numbered v
Figure FDA00035737742900000617
The ventilation volume of the bus in the process of arriving at a bus station u, C is the designed volume of the bus, V is the volume of the bus, P is the travel route selected by the traveler, P is the travel route set, K is the departure time selected by the traveler, K is the set of all departure times, OD is the starting and ending point of the traveler, OD is the set of the starting and ending point,
Figure FDA00035737742900000618
is the state of whether the speaker (k, od) is an infected person, the speaker (k, od) is a speaker whose starting and ending points at time k are od, gamma is the ratio of the exhaled virus remaining on the surface, and sk,odIs a susceptibility parameter of a traveler (k, od),
Figure FDA0003573774290000071
bus with r number v for bus line at bus station
Figure FDA0003573774290000072
The time point of departure is the point in time,
Figure FDA0003573774290000073
The time point when the bus with the number v of the bus line r arrives at the bus station u, qk,odThe number of pathogenic pathogens exhaled by the travelers (k, od), Sr,uIs a set of bus stations of a bus line r,
Figure FDA0003573774290000074
is the state of whether a traveler (k, od) is at a bus stop u of a bus with the bus line r number v,
Figure FDA0003573774290000075
is the state of whether the traveler (k, od) uses the travel route p.
9. The method as claimed in claim 6, wherein the formula for calculating the expected total travel cost of each individual in the initial population up to the maximum population number in step S43 is as follows:
Figure FDA0003573774290000076
Figure FDA0003573774290000077
wherein TC is the expected total travel cost, K is the departure time selected by the traveler, K is the set of all the departure times, OD is the starting and ending point of the traveler, OD is the set of the starting and ending point,
Figure FDA0003573774290000078
VOT for shortest path utilization in travelers (k, od)k,odIs the travel time value, W, of the traveler (k, od)infectFor the weight of the cure cost of the disease, E () is the expectation function, NnewCOT is the cure cost of the disease for newly increased number of infected people.
10. The method for dispatching buses with consideration of maximum passenger capacity limit under epidemic propagation as claimed in claim 6, wherein the probability that the individuals with the maximum elite population number in the step S44 are selected in the initial population is:
Figure FDA0003573774290000079
Wherein p (i) is the probability of the ith individual in the initial population being selected, and tc (i) is the total travel cost of the individual i in the initial population.
CN202210333485.0A 2022-03-30 2022-03-30 Bus dispatching method considering maximum passenger capacity limit under epidemic situation propagation Pending CN114757510A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210333485.0A CN114757510A (en) 2022-03-30 2022-03-30 Bus dispatching method considering maximum passenger capacity limit under epidemic situation propagation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210333485.0A CN114757510A (en) 2022-03-30 2022-03-30 Bus dispatching method considering maximum passenger capacity limit under epidemic situation propagation

Publications (1)

Publication Number Publication Date
CN114757510A true CN114757510A (en) 2022-07-15

Family

ID=82328594

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210333485.0A Pending CN114757510A (en) 2022-03-30 2022-03-30 Bus dispatching method considering maximum passenger capacity limit under epidemic situation propagation

Country Status (1)

Country Link
CN (1) CN114757510A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115271276A (en) * 2022-09-30 2022-11-01 广东工业大学 Combined macro-micro demand response type vehicle scheduling method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115271276A (en) * 2022-09-30 2022-11-01 广东工业大学 Combined macro-micro demand response type vehicle scheduling method
CN115271276B (en) * 2022-09-30 2023-02-03 广东工业大学 Combined macro-micro demand response type vehicle scheduling method

Similar Documents

Publication Publication Date Title
Fan et al. Optimal design of intersecting bimodal transit networks in a grid city
CN107564269B (en) A kind of half flexible bus dispatching method based on willingness to pay
CN109543934B (en) Method for evaluating comprehensive index of urban public transport network
CN107358357A (en) Urban track traffic transfer station evaluation method
CN110599760B (en) Travel behavior simulation method under multi-mode traffic network
CN112309119B (en) Urban traffic system capacity analysis optimization method
Yao et al. Simulation-based dynamic passenger flow assignment modelling for a schedule-based transit network
CN115527369B (en) Large passenger flow early warning and evacuation method under large-area delay condition of airport hub
Tsai et al. Joint optimization of temporal headway and differential fare for transit systems considering heterogeneous demand elasticity
CN114757510A (en) Bus dispatching method considering maximum passenger capacity limit under epidemic situation propagation
CN111724076A (en) Regional multi-type rail transit passenger flow dynamic distribution method under operation interruption condition
CN113538886A (en) Real-time response type customized bus hierarchical scheduling method
CN115713207A (en) Hybrid bus service combination optimization method
Karoń Travel demand and transportation supply modelling for agglomeration without transportation model
CN113592419B (en) Rail transit speed and time table optimization method considering passenger flow and energy conservation
RITH et al. Development and application of a travel mode choice model and policy implications for home-to-work commuters toward reduction of car trips in metro Manila
CN113096429B (en) Elastic bus area flexibility line generation method based on bus dispatching station distribution
CN106067078A (en) Bus berth distribution optimization method for double platforms
Limsawasd et al. Skip-Stop Strategy Patterns optimization to enhance mass transit operation under physical distancing policy due to COVID-19 pandemic outbreak
Si et al. Optimization model and algorithm for mixed traffic of urban road network with flow interference
Chen et al. Simulation and optimization of pedestrian regular evacuation in comprehensive rail transit hub–a case study in Beijing
Huang et al. Optimal headway-based bus dispatching strategy under the influence of epidemic outbreaks
CN111862637B (en) Traffic information control method, system, storage medium, computer device, and terminal
CN111754071A (en) Regional rail transit global security enhancing method
Yoo et al. Revising bus routes to improve access for the transport disadvantaged: A reinforcement learning approach

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination