CN115169696A - Intelligent dispatching method for subway connection bus under manual and automatic driving mixed running - Google Patents

Intelligent dispatching method for subway connection bus under manual and automatic driving mixed running Download PDF

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CN115169696A
CN115169696A CN202210798186.4A CN202210798186A CN115169696A CN 115169696 A CN115169696 A CN 115169696A CN 202210798186 A CN202210798186 A CN 202210798186A CN 115169696 A CN115169696 A CN 115169696A
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丁川
宋英杰
于滨
鲁光泉
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Abstract

The invention discloses an intelligent dispatching method for subway connection buses under manual and automatic driving mixed running, which comprises the steps of selecting a research area and constructing a traffic network topological graph; constructing a bus running constraint condition under a mixed running environment of manual and automatic driving buses; constructing a connection bus intelligent scheduling model; and designing an optimization algorithm for the model and solving to obtain an intelligent scheduling scheme, wherein the scheme comprises a connection bus route, vehicle distribution, departure time and departure type. The invention fully considers the characteristic that the modular automatic driving vehicles are flexible and can be grouped, effectively reduces the travel time of passengers and the operation cost of a bus company by intelligently scheduling the connection buses, and improves the service level of a connection bus system. Meanwhile, the intelligent dispatching method has higher universality, is not only suitable for intelligent dispatching of the connected buses in mixed running environments with different proportions, but also suitable for intelligent dispatching of the connected buses in completely manual driving and completely automatic driving environments.

Description

Intelligent dispatching method for subway connection bus under manual and automatic driving mixed running
Technical Field
The invention relates to the technical field of urban intelligent public transport, in particular to an intelligent subway connection bus dispatching method under manual and automatic driving mixed running.
Background
Urban rail transit plays an increasingly important role in modern urban public transportation systems. However, the coverage rate of the rail transit is greatly limited due to the high cost and the long period of the rail transit construction, and on the contrary, the conventional public transit system has the characteristics of high wire network coverage rate, flexible line layout and the like, so that whether the conventional public transit and the rail transit can be efficiently connected or not is very important. At present, most of researches on the buses for connection are individually optimized for line design and scheduling in a full-manual driving environment, and the consideration of dynamic interaction among a bus schedule, bus capacity, line design and vehicle distribution is lacked. Meanwhile, the phenomenon of uneven time-space distribution of traffic travel often occurs in urban areas, for example, the passenger flow is rapidly increased in peak hours, so that the waiting time of passengers is too long, the buses are abnormally crowded, the passenger flow is less in off-peak hours, so that the passenger volume of the buses is lower, and a large amount of social resources are wasted. The public transport company generally adopts different departure frequencies in different time periods in the face of the situation, but the capacity of the vehicle is fixed, so that the operation cost of the company and the waiting time of passengers cannot be completely balanced. The rapid development of new generation information technology and artificial intelligence and the deep integration in the traffic field promote the generation of the concept of automatically driving the bus. The autonomous bus exhibits great advantages in aspects of improving the safety of bus operation, eliminating driver cost, reducing energy consumption, improving the flexibility of scheduling of vehicles, and the like, and in addition, the autonomous bus has the greatest characteristic that the capacity of the autonomous bus can be changed according to the change of the demand of passengers, namely, the capacity of the autonomous bus is dynamically changed by assembling or disassembling unit cars according to the change of the demand, and the function can be realized by a modular autonomous bus.
Therefore, how to realize intelligent dispatching of subway connection buses under manual and automatic driving mixed running is a problem which needs to be solved urgently by technical personnel in the field.
Disclosure of Invention
In view of the above, the invention provides an intelligent dispatching method for subway connection buses under manual and automatic driving mixed running, which is used for dynamically and jointly optimizing connection bus route design, vehicle distribution, departure time and departure type under the existing quantity of manual and automatic driving buses in a planned area, so that the operation cost of a bus company can be reduced, the trip cost of passengers can be reduced, and the trip comfort level can be improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
an intelligent dispatching method for a subway connection bus under manual and automatic driving mixed running comprises the following specific steps:
step 1: selecting a research area, and constructing a traffic network topological graph of the research area;
step 2: constructing a running constraint condition of the transfer bus under the mixed running environment of manual and automatic bus driving according to the traffic network topological graph;
and step 3: constructing a connection bus intelligent scheduling model according to the operation constraint conditions of the connection bus;
and 4, step 4: and constructing an integral optimization algorithm for the intelligent dispatching model of the connected bus, and solving the integral optimization algorithm to obtain an intelligent dispatching scheme, wherein the scheme comprises a connected bus line, vehicle distribution, departure time and departure type.
Preferably, the specific implementation process of step 1 includes:
step 11: selecting a connected subway station to be researched, wherein the passenger flow volume in and out of the subway station meets the condition of opening a connected bus;
step 12: selecting a reasonable connection range of the subway station according to the road distribution of the area near the subway station and the actual situation of the land type;
step 13: selecting candidate road sections meeting the operation conditions of the connected buses according to the actual road distribution, and setting intersections of the candidate road sections as bus stops;
step 14: taking a building unit as a travel demand point, and investigating and counting the number and positions of the demand points in the research area;
step 15: according to GIS data of the regional population survey, the population of each demand point can be obtained; the travel demand of each demand point is in direct proportion to the population of the demand point;
step 16: collecting the passenger flow volume of the connected subway station entering and exiting the station at each time interval, and proportionally distributing the passenger flow volume of the entering and exiting station according to the population of each demand point to obtain the travel demand volume of each demand point on the assumption that the number of the residents taking the subway to the subway station is in direct proportion to the population;
and step 17: and constructing a network topological graph according to the determined attributes of the subway station, the candidate road sections, the bus stations and the demand points. The demand point attributes include demand point number, location, and trip demand.
Preferably, the implementation process body of step 2 comprises:
step 21: will study period T 0 Discretized into J +1 evenly distributed time nodes, the set of discretized time nodes being denoted as T = [0,1,2, …, J = [0,1,2, … ]]Then the unit interval time is Δ T = T 0 /J；
Step 22: presetting three decision variables of an intelligent dispatching model of the plug-in bus;
setting the first decision variable to X ijh (ii) a When X is present ijh If =1, the road section ij is taken as a connection road section of the connection bus line h; otherwise X ijh =0, which means that the road section ij is not a connection road section of the connection bus line h; the road section selected by the connection bus line is the connection road section, and the connection road section forms the connection bus line.
The second decision variable is
Figure BDA0003732968720000031
Representing that a vehicle of type v serving a transfer bus route h departs at time t; otherwise
Figure BDA0003732968720000032
Representing services for dockingThe bus with the type v of the bus line h does not depart at t;
the third decision variable is
Figure BDA0003732968720000033
And
Figure BDA0003732968720000034
respectively representing the number of manually driven buses and modular autonomous buses distributed to the connection bus line h;
step 23: the method for setting the operation constraint conditions of the bus to be plugged comprises the following steps:
the collection of bus stops on the bus line h of plugging into is as follows:
Figure BDA0003732968720000035
wherein L is a set of connected bus lines, e h Representing the terminal station of the connecting bus line h;
the bus line of plugging into has continuity, and is represented as:
Figure BDA0003732968720000036
wherein s is h Representing the starting station of the connecting bus line h;
no loop is generated midway in the bus line connection:
Figure BDA0003732968720000037
Figure BDA0003732968720000038
and (3) restraining the length of the bus line to be plugged:
Figure BDA0003732968720000039
wherein,
Figure BDA0003732968720000041
the distance between adjacent bus stops i and j on the connection bus line h is represented, and the unit is kilometers; d min And D max Respectively representing the minimum length and the maximum length of a connection bus line, wherein the unit is kilometer;
the nonlinear coefficient of the bus line of plugging does not exceed the maximum value specified in the specification:
Figure BDA0003732968720000042
wherein D(s) h ,e h ) Representing the spatial linear distance between the origin and destination points of the connection bus line;
the total number of the vehicles of the manual driving buses and the modularized automatic driving vehicles dispatched by each connection bus line is equal to the number of the vehicles distributed to the line:
Figure BDA0003732968720000043
Figure BDA0003732968720000044
the total number of vehicles of the scheduled manually driven buses and the modular autonomous vehicles is equal to the number of vehicles owned by the research area:
Figure BDA0003732968720000045
Figure BDA0003732968720000046
each transfer bus route sends one type of bus at most at each time point:
Figure BDA0003732968720000047
each ported bus route has at least one type of vehicle service:
Figure BDA0003732968720000048
departure time of the vehicle r at the first stop of the transfer bus route h:
Figure BDA0003732968720000049
wherein, b h Is a bus departure sequence set on a connection bus line h;
the time for the vehicle r to leave the station i is the sum of the time for the vehicle r to reach the station i and the time for the vehicle r to stay at the station i:
Figure BDA00037329687200000410
the residence time of the vehicle r at the station i is the number of people who take the vehicle r at the station i multiplied by the average boarding time:
Figure BDA00037329687200000411
the number of people who take the vehicle r at the station i on the connection bus line h:
Figure BDA0003732968720000051
wherein,
Figure BDA0003732968720000052
is a function of the arrival rate of the demand point k.
The time for the 0 th vehicle to leave the starting station on the transfer bus route h is the starting time of the study:
Figure BDA0003732968720000053
the time for the vehicle r to reach the i +1 station should be the sum of the time for the vehicle to leave the station i and the time to travel between the two stations:
Figure BDA0003732968720000054
the interval between two adjacent vehicles on the connection bus line h to reach the starting station is not less than the minimum value required by the bus system:
Figure BDA0003732968720000055
the time when the vehicle r leaves the station i on the transfer bus line h is earlier than the time when the vehicle r-1:
Figure BDA0003732968720000056
preferably, the specific implementation process of step 3 includes:
the benefits of an operator and a passenger are comprehensively considered, the constructed objective function is to minimize the sum of the operation cost, the passenger trip cost and the punishment cost of the unworked passenger of different types of buses of the whole bus system, and the passenger trip cost comprises the walking cost, the waiting cost and the on-board cost;
compared with manual driving of buses, the automatic driving of buses reduces or eliminates the cost of taking services on the buses by eliminating the requirements of drivers, so that the operation cost of each bus is reduced, and can be represented by a fixed cost parameter gamma (gamma is more than 0 and less than 1); on the other hand, because v modularized automatic driving vehicles are connected to form a type v automatic driving bus, the speed and the acceleration of the type v automatic driving buses are consistent, the resistance of the whole bus is reduced, the energy consumption of the whole bus can be reduced, and the characteristic is reflected by an economic scale parameter eta (0 < eta < 1);
the unit time operation cost of the bus:
Figure BDA0003732968720000057
wherein, when v =0, the operation cost of manually driving the public transport is shown, and v = m>0 represents the operation cost of connecting m modular autonomous buses together to form an autonomous bus with larger capacity;
Figure BDA0003732968720000058
represents a fixed cost of vehicle operation;
Figure BDA0003732968720000059
respectively representing the marginal cost of manually driving the public transport and automatically driving the public transport;
Figure BDA00037329687200000510
and
Figure BDA0003732968720000061
respectively representing the maximum capacity of the manually driven bus and the modular automatic driving bus; gamma represents a fixed cost parameter; η represents an economic scale parameter;
bus operating cost:
Figure BDA0003732968720000062
wherein,
Figure BDA0003732968720000063
the bus driving time between adjacent stations i and i +1 on the connection bus line h is expressed in minutes;
the set of bus stops where a passenger in demand point k can take a bus is represented as:
Figure BDA0003732968720000064
wherein,
Figure BDA0003732968720000065
the distance from a demand point k to a bus stop i is represented, and the unit is kilometers; k * The maximum service radius of the bus stop is represented, and the unit is kilometer; k represents the set of all demand points;
the demand point set served by the bus station i is represented as:
Figure BDA0003732968720000066
the waiting time of the passenger is expressed as:
Figure BDA0003732968720000067
the number of the buses r operating on the transfer bus line h on the transfer road section ij is represented as follows:
Figure BDA0003732968720000068
the discomfort factor for a passenger in vehicle r over the docking path ij is expressed as:
Figure BDA0003732968720000069
wherein,
Figure BDA00037329687200000610
and
Figure BDA00037329687200000611
respectively representing the seat number and the maximum capacity of the r-th bus on the connecting bus line h; α and β are the respective parameters;
the boarding time is expressed as:
Figure BDA00037329687200000612
wherein, σ is a parameter to be calibrated, and for simple calculation, σ =1 is taken;
passenger walking time:
Figure BDA0003732968720000071
wherein,
Figure BDA0003732968720000072
representing the average walking time from the demand point k to the bus stop i in minutes;
number of non-served passengers:
Figure BDA0003732968720000073
set of served demand points:
Figure BDA0003732968720000074
the joint optimization model for connecting the bus is as follows:
minM b +ω 1 M w +ω 2 M t +ω 3 M f +ω 4 M p (32)
s.t.
Eqs.(1)-(20)
wherein, ω is 1 、ω 2 、ω 3 Average monetary value coefficients of time of waiting for the passenger, time of getting on the bus and walking time respectively; omega 4 A penalty fee for not being served passengers. The product of the average monetary value coefficient and time is the cost, omega 1 M w Indicates the waiting cost, omega 2 M t Indicating the cost on board, omega 3 M f Representing the walking cost, the product of the penalty cost of the unworked passenger and the number of the unworked passengers is the penalty cost of the unworked passenger.
Preferably, the solving process of the intelligent scheduling model is mainly implemented by two stages, and the specific implementation process of step 4 includes:
step 41: in the first stage, an improved genetic algorithm is used for solving the joint optimization problem of the departure time and the departure type of the transfer bus, and as the departure time and the departure time mainly affect the operation cost of the bus, the waiting cost of passengers and the on-board cost of the bus, an objective function for defining the joint optimization problem of the departure time and the departure type of the transfer bus is as follows:
Z 1 ＝M b +ω 1 M w +ω 2 M t (33)
fitness function of
fitness 1 ＝1/Z 1 (34)
Step 42: the optimal solution obtained by the objective function of the joint optimization problem of the departure time and the departure type of the transfer bus is used as the input of the second stage, the improved genetic algorithm is used for solving the problems of the running line design and the vehicle distribution of the transfer bus, the problems directly affect the running cost of the bus, the walking cost of passengers and the punishment cost of the unworked passengers, and the running cost of the bus is obtained in the first stage, so that the line design and the vehicle distribution objective function Z are firstly calculated 2 The overall objective function constructed according to the optimal solution of the joint optimization problem of the departure time and the departure type of the transfer bus, the walking cost of the passengers and the punishment cost of the unworked passengers is as follows:
Z 2 ＝ω 4 M f +ω 5 M p (35)
Z＝Z 1 +Z 2 (36)
the line design and vehicle distribution fitness function is:
Figure BDA0003732968720000081
step 43: and solving an intelligent dispatching model of the transfer bus by adopting an improved dual genetic algorithm, and outputting an intelligent dispatching scheme comprising transfer bus lines, vehicle distribution, departure time and departure type.
Preferably, the specific calculation process of the improved dual genetic algorithm is as follows:
step 431: setting the population scale of the problems of the design of the connection bus line and the distribution of the vehicles as M, the maximum iteration number N and the cross probability as P c The mutation probability is P m Initialization counter gene =0; the population scale of the joint optimization problem of the departure time and the departure type of the plug-in bus is M1, the maximum iteration number is N1, and the cross probability is P c1 The mutation probability is P m1 Initialization counter gene1=0;
step 432: carrying out chromosome coding on the design of the connected bus line and the problem of vehicle distribution, the connection bus departure time and the departure type joint optimization problem by using an integer sequence coding method, and carrying out chromosome coding on the problem of vehicle distribution by using a {0,1} binary coding method;
step 433: if the gene is smaller than N, generating a child population which is connected with the problems of bus route design and vehicle distribution; otherwise, ending the circulation, and outputting the connection bus routes, the number of buses distributed by each connection bus route, the departure time and the departure type;
step 434: if the gene1 is smaller than N1, generating a child population of the bus dispatching; otherwise go to step 437;
step 435: executing selection operation by adopting an elite strategy and a roulette method; at a cross probability of P c1 Under the control of (3), performing a cross operation by adopting a two-point cross method; at a mutation probability of P m1 Performing mutation operation by using a basic bit mutation method under the control of (1); repairing chromosomes which do not meet the constraint;
step 436: calculating the chromosome fitness of the joint optimization problem of the departure time and the departure type of the transfer bus according to a formula (34), and enabling gene1= gene1+1, and returning to the step 434;
step 437: calculating chromosome fitness of the design of the connection bus line and the vehicle distribution problem according to a formula (37);
step 438: executing selection operation by adopting an elite strategy and a roulette method; at a cross probability of P c Performing a cross operation by adopting a two-point cross method under the control of (3); at mutation probability of P m Performing mutation operation by using a basic bit mutation method under the control of (1); repairing chromosomes which do not meet the constraint;
step 439: let gene = gene +1 and return to said step 433.
According to the technical scheme, compared with the prior art, the invention discloses and provides an intelligent dispatching method for subway connection buses under manual and automatic driving mixed running, which comprises the steps of selecting a research area and constructing a traffic network topological graph of the area; constructing a connection bus running constraint condition under the mixed running environment of manual and automatic driving buses; constructing a connection bus intelligent scheduling model; and designing an optimization algorithm for the model and solving to obtain an intelligent scheduling scheme, wherein the scheme comprises a connection bus route, vehicle distribution, departure time and departure type. The invention fully considers the characteristic that the modularized automatic driving vehicles can be grouped flexibly, and performs combined optimization on the line design of the plug-in buses, vehicle distribution, departure time and departure type to play a more important role in the subway plug-in, thereby realizing the advantage complementation of various transportation modes of the conventional buses and the subways, effectively reducing the travel time of passengers and the operation cost of a bus company and improving the service level of a plug-in bus system. Meanwhile, the method has higher universality, is not only suitable for joint optimization of the buses under mixed running environments with different proportions, but also suitable for joint optimization of the buses under environments of complete manual driving and complete automatic driving.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic flow diagram of an embodiment of the present invention;
FIG. 2 is a schematic diagram of intelligent subway connection bus dispatching in a manual and automatic driving mixed environment according to the present invention;
fig. 3 is a schematic diagram of a network structure for studying regional topology according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention discloses an intelligent dispatching method for subway connection buses under manual and automatic driving mixed running, wherein the optimization process is shown in figure 1, the characteristic that modular automatic driving buses can be changed into automatic driving buses with variable capacity through grouping is considered, a certain number of buses are given, and dynamic joint optimization is carried out on the design, vehicle distribution, departure time and departure types of a plurality of connection bus lines in the service range of a subway station so as to reduce the operation cost of a bus company and improve the service quality (reduce the travel time of passengers and improve the comfort level of travel). The method has higher universality, is not only suitable for the joint optimization of the connected buses under the mixed running environment of different proportions, but also suitable for the joint optimization of the connected buses under the environment of full-manual driving and full-automatic driving.
An intelligent dispatching method for subway connection buses under manual and automatic driving mixed running is characterized in that a subway connection bus intelligent dispatching schematic diagram is shown in an attached figure 2, a simulation network is composed of 11 road sections, 9 demand points, 9 bus stops and 1 subway stop, and as shown in an attached figure 3, 1-9 reference circle points are markedIndicating a bus stop and the reference 10 dot indicates a subway stop. A docked bus can only travel along segments of a network, which consists of 4 manned buses and 12 modular autonomous buses. Duration of study T 0 =20min, the travel demand of the network is 660 persons/hour.
All passengers are set to get on the bus at a bus stop near a demand point and get off the bus at a subway stop, and the time for getting on the bus is 2.5 seconds for each passenger. A plurality of modularization automatic driving buses can be connected together to form the automatic driving buses with different capacities, and at most 5 modularization automatic driving buses can be connected together to form the automatic driving bus with the maximum capacity on the assumption, and the manual driving buses can only operate independently. The departure intervals of the vehicles on the connected bus line are uneven, and the maximum departure interval and the minimum departure interval of the buses are respectively 8 minutes and 2 minutes. The fixed operation cost of the manual driving bus is $ 5.16, the marginal operation cost is $ 0.37, and the automatic driving bus can save 63% of the fixed operation cost due to no manual driving cost, and the marginal operation cost is $ 0.37. The economy scale parameter of the assembled modular autonomous vehicle is 0.8, and the average value coefficient of the waiting time of the passengers is omega 1 $ 0.3 per minute, passenger's on-board time-averaged time-worth coefficient of ω 2 $ 0.15 per minute, the average time value coefficient of the walking time of the passenger is ω 3 $ 0.4 per minute, the penalty cost of an unserviced passenger is ω 4 $ 10.
For the problems of the design of the connection bus lines and the distribution of vehicles, the population scale of the genetic algorithm is set to be 50, and the maximum iteration number is 80. For the joint optimization problem of the departure time and the departure type of the transfer bus, the population scale of the genetic algorithm is set to be 50, and the maximum iteration time is 30 times. To ensure the diversity of the previous genes in the iteration and prevent premature convergence, the mutation rate was set to 0.4. In order to prevent the iteration later period from damaging the elite strategy, the mutation probability is set to be 0.1.
Consider the following two scenarios: firstly, optimizing the design of a transfer bus route and the problem of vehicle distribution, and optimizing the departure time and the departure type of the transfer bus according to the optimization result; and the second scenario is to perform dynamic joint optimization on the design of the transfer bus route, vehicle distribution, departure time and departure type. The simulation results are shown in table 1.
TABLE 1 simulation results
Figure BDA0003732968720000111
The invention considers the characteristic that the modularized automatic driving bus can be changed into the automatic driving bus with variable capacity through grouping, a certain number of buses are given, and the design, the vehicle distribution, the departure time and the departure type of a plurality of connected bus lines in the service range of the subway station are dynamically and jointly optimized. Compared with the design of a connection bus line and the independent optimization of the departure scheduling problem, the walking cost of passengers is increased by 1.99%, the waiting cost of the passengers is reduced by 10.46%, the boarding cost (boarding time and boarding comfort) of the passengers is reduced by 8.98%, the running cost of the vehicles is reduced by 11.58%, and the total cost of the system can be effectively reduced by 6.35% by the method.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (6)

1. An intelligent dispatching method for a subway connection bus under manual and automatic driving mixed running is characterized by comprising the following steps:
step 1: determining an optimized area, and constructing a traffic network topological graph of the optimized area;
step 2: constructing a bus running constraint condition under a mixed running environment of manual and automatic driving buses according to a traffic network topological graph;
and 3, step 3: constructing a connection bus intelligent scheduling model according to the bus operation constraint conditions;
and 4, step 4: and constructing an integral optimization algorithm for the intelligent dispatching model of the plug-in bus, and solving the integral optimization algorithm to obtain an intelligent dispatching scheme.
2. The intelligent dispatching method for the subway connection bus under the mixed operation of manual operation and automatic driving as claimed in claim 1, wherein the concrete implementation process of step 1 comprises:
step 11: selecting a subway station with the passenger flow meeting the condition of opening the connected buses;
step 12: determining a connection range according to the actual road distribution and land type of the area where the subway station is located;
step 13: selecting candidate road sections meeting the operation conditions of the connected buses according to the actual road distribution in the connection range, and setting intersections of the candidate road sections as bus stops;
step 14: determining the number and the positions of travel demand points in the connection range;
step 15: acquiring the population of each demand point, wherein the travel demand of each demand point is in direct proportion to the population of the demand point;
step 16: collecting the passenger flow volume of the connected subway station entering and exiting the station at each time interval, and distributing the passenger flow volume of the entering and exiting station according to the proportion of the population of each demand point to obtain the travel demand volume of each demand point;
and step 17: and constructing a network topological graph according to the determined attributes of the subway station, the candidate road sections, the bus stations and the demand points.
3. The intelligent dispatching method for the subway connection bus under the condition of manual and automatic driving mixed traveling is characterized in that the concrete implementation process of the step 2 comprises the following steps:
step 21: determining an optimization period T 0 Discretized into J +1 evenly distributed time nodes, the set of discretized time nodes being denoted as T = [0,1,2, …, J = [0,1,2, … ]]Then the unit interval time is Δ T = T 0 /J；
Step 22: presetting three decision variables of an intelligent dispatching model of a plug-in bus;
the first decision variable is X ijh (ii) a When X is ijh When =1, the road section ij is taken as a connection road section of the connection bus line h; x ijh =0, indicating that the section ij is not a docked section of the docked bus route h;
the second decision variable is
Figure FDA0003732968710000021
Representing that a vehicle of type v serving a transfer bus route h departs at time t; otherwise
Figure FDA0003732968710000022
A vehicle of type v that is serving a transfer bus route h is not dispatched at time t;
the third decision variable is
Figure FDA0003732968710000023
And
Figure FDA0003732968710000024
respectively representing the number of manually driven buses and modular autonomous buses distributed to the connection bus line h;
step 23: the method for setting the operation constraint conditions of the bus to be plugged comprises the following steps:
the collection of bus stations on the connection bus line h is as follows:
Figure FDA0003732968710000025
wherein L is a set of connected bus lines, e h A terminal station representing a connection bus route h;
the bus line of plugging into has continuity, and is represented as:
Figure FDA0003732968710000026
wherein s is h Indicating a starting station of a connecting bus line h;
the connection of the bus line does not generate a loop midway:
Figure FDA0003732968710000027
Figure FDA0003732968710000028
and (3) restraining the length of the connection bus line:
Figure FDA0003732968710000029
wherein,
Figure FDA00037329687100000210
the distance between adjacent bus stops i and j on the connection bus line h is represented, and the unit is kilometers; d min And D max Respectively representing the minimum length and the maximum length of a connection bus line, wherein the unit is kilometer;
the nonlinear coefficient of the bus line not to be plugged does not exceed the preset maximum value:
Figure FDA00037329687100000211
wherein D(s) h ,e h ) Representing the spatial linear distance between the origin and destination points of the connection bus line;
the total number of the vehicles of the manual driving buses and the modularized automatic driving vehicles dispatched by each connection bus line is equal to the number of the vehicles distributed to the line:
Figure FDA0003732968710000031
Figure FDA0003732968710000032
the total number of vehicles of the scheduled manually driven buses and the modular autonomous vehicles is equal to the number of vehicles owned by the optimized area:
Figure FDA0003732968710000033
Figure FDA0003732968710000034
each transfer bus route sends one type of bus at most at each time point:
Figure FDA0003732968710000035
each ported bus route has at least one type of vehicle service:
Figure FDA0003732968710000036
departure time of the vehicle r at the first stop of the transfer bus route h:
Figure FDA0003732968710000037
wherein, b h Is a bus departure sequence set on a connecting bus line h;
the time for the vehicle r to leave the station i is the sum of the time for the vehicle r to arrive at the station i and the time for the vehicle r to stay at the station i:
Figure FDA0003732968710000038
the residence time of the vehicle r at the station i is the number of people who take the vehicle r at the station i multiplied by the average boarding time:
Figure FDA0003732968710000039
the number of people who take the vehicle r at the station i on the connection bus line h:
Figure FDA00037329687100000310
wherein,
Figure FDA00037329687100000311
is the arrival rate function of the demand point k;
the time when the 0 th vehicle leaves the starting station on the transfer bus line h is the starting time:
Figure FDA00037329687100000312
the time when the vehicle r arrives at the i +1 station is the sum of the time when the vehicle leaves the station i and the time when the vehicle travels between the two stations:
Figure FDA0003732968710000041
the interval between two adjacent vehicles on the transfer bus line h and the initial station is not less than the preset minimum value:
Figure FDA0003732968710000042
the time when the vehicle r leaves the station i on the transfer bus line h is earlier than the time when the vehicle r-1:
Figure FDA0003732968710000043
4. the intelligent dispatching method for the subway connection buses under the mixed operation of manual operation and automatic driving as claimed in claim 2, characterized in that an objective function of an intelligent dispatching model for the connection buses is constructed to minimize the sum of the operation cost, the passenger trip cost and the punishment cost of the un-served passengers of different types of buses of the bus system, wherein the passenger trip cost comprises the walking cost, the waiting cost and the on-board cost; the specific implementation process of the step 3 comprises the following steps:
the unit time operation cost of the bus:
Figure FDA0003732968710000044
wherein, v =0 represents the operation cost of manually driving the public transport, and v = m > 0 represents the operation cost of the automatic driving public transport with larger capacity formed by connecting m modularized automatic driving vehicles;
Figure FDA0003732968710000045
represents a fixed cost of vehicle operation;
Figure FDA0003732968710000046
respectively representing the marginal cost of manually driving the public transport and automatically driving the public transport;
Figure FDA0003732968710000047
and
Figure FDA0003732968710000048
respectively representing the maximum capacity of the manually driven bus and the modular automatic driving bus; gamma represents a fixed cost parameter; η represents an economic scale parameter;
bus operating cost:
Figure FDA0003732968710000049
wherein,
Figure FDA00037329687100000410
the bus driving time between adjacent stops i and i +1 on the connection bus line h is represented by minutes;
the set of bus stops where a passenger in demand point k can take a bus is represented as:
Figure FDA00037329687100000411
wherein,
Figure FDA00037329687100000412
representing the distance from the demand point k to the bus stop i in kilometers; k * Represents the maximum service radius of the bus stop, and the unit is kilometer; k represents the set of all demand points;
the demand point set served by the bus station i is represented as:
Figure FDA0003732968710000051
the waiting time of the passenger is expressed as:
Figure FDA0003732968710000052
the number of the buses r operating on the transfer bus line h on the transfer road section ij is represented as follows:
Figure FDA0003732968710000053
the discomfort factor for a passenger in vehicle r over the docking section ij is expressed as:
Figure FDA0003732968710000054
wherein,
Figure FDA0003732968710000055
and
Figure FDA0003732968710000056
respectively representing the seat number and the maximum capacity of the r-th bus on the connecting bus line h; α and β are the respective parameters;
the boarding time is expressed as:
Figure FDA0003732968710000057
wherein, σ is a parameter to be calibrated, and for simple calculation, let σ =1;
the walking time of the passengers is as follows:
Figure FDA0003732968710000058
wherein,
Figure FDA0003732968710000059
representing the average walking time from the demand point k to the bus stop i in minutes;
the number of non-serviced passengers is:
Figure FDA00037329687100000510
set of served demand points:
Figure FDA00037329687100000511
the intelligent dispatching model of the plug-in bus is as follows:
minM b +ω 1 M w +ω 2 M t +ω 3 M f +ω 4 M p (32)
s.t.
Eqs.(1)-(20)
wherein, ω is 1 、ω 2 、ω 3 Average monetary value coefficients for passenger waiting time, on-board time and walking time, respectively; omega 4 A penalty fee for not being served passengers.
5. The intelligent dispatching method for subway connection buses under manual and automatic driving mixed traveling according to claim 3, characterized in that the concrete implementation process of step 4 comprises:
step 41: constructing an objective function of a joint optimization problem of the departure time and the departure type of the transfer bus according to the operation cost of the bus, the waiting cost of passengers and the on-board cost, wherein the objective function is expressed as follows:
Z 1 ＝M b +ω 1 M w +ω 2 M t (33)
the fitness function of the joint optimization problem of the departure time and the departure type of the plug-in bus is
Figure FDA0003732968710000061
Step 42: constructing a receiving bus route design and vehicle distribution objective function according to the optimal solution of the joint optimization problem of the receiving bus departure time and the departure type, the walking cost of passengers and the punishment cost of unworked passengers, and expressing as follows:
Z 2 ＝ω 3 M f +ω 4 M p (35)
Z＝Z 1 +Z 2 (36)
the design of the connection bus line and the vehicle distribution fitness function are as follows:
Figure FDA0003732968710000062
step 43: and solving the intelligent dispatching model of the plug-in bus by adopting an improved dual genetic algorithm, and outputting an intelligent dispatching scheme.
6. The intelligent dispatching method for subway connection buses under manual and automatic driving mixed traveling according to claim 4, characterized in that the specific calculation process of the improved dual genetic algorithm is as follows:
step 431: setting the population scale of the problems of the design of the connection bus line and the distribution of the vehicles as M, the maximum iteration number N and the cross probability as P c The mutation probability is P m Initialization counter gene =0; the population scale of the joint optimization problem of the departure time and the departure type of the plug-in bus is M1, the maximum iteration number is N1, and the cross probability is P c1 The mutation probability is P m1 Initialization counter gene1=0;
step 432: carrying out chromosome coding on the design of the connected bus line and the problem of vehicle distribution, the connection bus departure time and the departure type joint optimization problem by using an integer sequence coding method, and carrying out chromosome coding on the problem of vehicle distribution by using a {0,1} binary coding method;
step 433: if the gene is smaller than N, generating a child population which is connected with the problems of bus route design and vehicle distribution; otherwise, ending the circulation, and outputting the connection bus routes, the number of buses distributed by each connection bus route, the departure time and the departure type;
step 434: if the gene1 is smaller than N1, generating a child population of the bus scheduling; otherwise go to step 437;
step 435: executing selection operation by adopting an elite strategy and a roulette method; at a cross probability of P c1 Under the control of (3), performing a cross operation by adopting a two-point cross method; at a mutation probability of P m1 Performing mutation operation by using a basic bit mutation method under the control of (1); repairing chromosomes which do not meet the constraint;
step 436: calculating the chromosome fitness of the joint optimization problem of the departure time and the departure type of the transfer bus according to the fitness function of the joint optimization problem of the departure time and the departure type of the transfer bus, and enabling gene1= gene1+1, and returning to the step 434;
step 437: calculating chromosome fitness of the design of the connection bus line and the vehicle distribution problem according to the design of the connection bus line and the vehicle distribution fitness function;
step 438: executing selection operation by adopting an elite strategy and a roulette method; at a cross probability of P c Under the control of (3), performing a cross operation by adopting a two-point cross method; at mutation probability of P m Performing mutation operation by using a basic bit mutation method under the control of (1); repairing chromosomes which do not meet the constraint;
step 439: let gene = gene +1 and return to said step 433.
CN202210798186.4A 2022-07-06 2022-07-06 Intelligent dispatching method for subway connection bus under manual and automatic driving mixed running Pending CN115169696A (en)

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Publication number Priority date Publication date Assignee Title
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115798211A (en) * 2022-11-21 2023-03-14 长安大学 Control method, system, equipment and medium for preventing internet public transport from being disconnected and mixed
CN115798211B (en) * 2022-11-21 2023-09-22 长安大学 Control method, system, equipment and medium for preventing network bus from being separated and mixed

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