CN116562581A

CN116562581A - Rail transit connection bus route and scheduling optimization method based on shared bicycle travel influence

Info

Publication number: CN116562581A
Application number: CN202310577764.6A
Authority: CN
Inventors: 蔡晶; 李卓奇; 张然; 曾大囌; 王晓静; 赵蕊; 郭凤香
Original assignee: Kunming University of Science and Technology
Current assignee: Kunming University of Science and Technology
Priority date: 2023-05-22
Filing date: 2023-05-22
Publication date: 2023-08-08

Abstract

An optimization method for connecting bus route and dispatch based on shared bicycle travel influence belongs to the public transportation planning management field, firstly selects a rail transportation station to be optimized, delimits an optimization area, extracts information of all bus stops and shared bicycle stop points in a research area, corresponds the shared bicycle stop points selected by the same district travel with the bus stops with the shortest distance, acquires the current operation mode of the selected rail transportation station and the traffic data of the passenger from the residence to the nearest public backcross station and the nearest shared bicycle stop point, constructs an objective function model for minimizing the total cost of the passenger and operation connected with the shared bicycle in a connecting bus, determines a logic model of the selection probability, determines constraint conditions of the logic model, solves the model to obtain a planning path, reduces the total cost of the passenger taking the bus by optimizing the connecting bus route and the dispatching moment, and increases the selection probability of the passenger to connect the bus.

Description

Rail transit connection bus route and scheduling optimization method based on shared bicycle travel influence

Technical Field

The invention relates to the technical field of public transportation planning management, in particular to an optimization method for connecting and dispatching a public transportation path based on track traffic with shared bicycle travel influence.

Background

As an important ring in urban transportation systems, rail transit is popular among the public because it has the advantages of being fast, on time, safe and unaffected by road traffic conditions. However, the rail transit is used as the large artery of urban traffic, has long construction period and high cost, and takes economic and environmental factors into consideration during design planning, so that partial rail transit stations are arranged beside an open road and have a certain distance from a residential area or a living area, and passengers cannot arrive at home in one stop. At the moment, the rail transit connection bus serving as the urban traffic capillary vessel can be used as a carrier of the last kilometer to go deep into the community, and reach places where the rail transit cannot cover. The bus is connected to connect the residence, office building, business area and rail transit station, which is convenient for citizens to travel and increases the service range of rail transit. In addition, the shared bicycle and the shared electric bicycle which are rapidly developed in recent years gradually become important transportation means for citizens to go out for connection.

The shared bicycle has the characteristics of low use threshold, convenient use, low cost and the like, so the shared bicycle has the irreplaceable advantage in short-distance travel. As the lowest carbon and environment-friendly traffic mode, the input amount of the shared bicycle is increased in all big plains such as Beijing, shanghai, shenzhen, chengdu and Kunming, and the shared bicycle is visible everywhere. The shared bicycle becomes the first choice for citizens to commute and connect with rail transit, and accordingly small impact and influence are brought to the passenger flow of urban connection buses.

The shared bicycle and the connected bus are both used as ideal traffic modes of connected rail traffic, and a certain competition relationship exists. In recent years, the travel amount of the shared bicycle is larger and larger, and the traffic flow of the connected buses is drastically reduced. The loss of the bus passenger flow means the decrease of income, so that in order to balance the balance and reduce the operation cost, the bus company can reduce the shifts of the connected buses and increase the departure interval. But for middle-aged and elderly people, citizens who can not ride in the riding, passengers carrying large pieces of luggage and in bad weather, the bus connection is the best way of connecting the bus for traveling, and traveling services are required to be provided for the groups well. Therefore, the influence of the shared bicycle is considered, reasonable path and scheduling planning are carried out on the rail transit connected buses, the passenger flow of the connected buses can be improved, and the method has very important practical significance on the travel problem of last kilometers of urban residents.

Disclosure of Invention

The invention aims to optimize the track traffic connection bus route and the departure schedule under the influence of the shared bicycle, increase the passenger flow sharing rate of the connection bus, achieve balanced operation of the connection bus and the shared bicycle, and provide better travel service for citizens.

In order to achieve the above object, the technical scheme of the present invention is as follows:

a track traffic connection bus route and scheduling optimization method based on shared bicycle travel influence comprises the following steps:

step one: selecting a rail transit station to be optimized, and defining an optimized area;

step two: extracting information of all bus stops and shared bicycle stop points in a research area, acquiring the current operation mode of a selected rail transit station, acquiring traffic behavior data of passengers from a residence to the nearest bus stop and the nearest shared bicycle stop point, investigating the passenger flow of each stop, and acquiring traffic data in connection behavior;

step three: constructing an objective function model for minimizing the total cost of passengers and operation connected by the bus and the shared bicycle;

step four: constructing a Logit model of trip mode selection probability;

step five: constructing constraint conditions to meet reality and realize reasonable solving results;

Step six: and solving a model to obtain an optimized result of the connection bus route and the dispatching under the influence of the travel of the shared bicycle.

Further, the basic assumption condition of the present model is: each connected bus line runs according to the obtained average running speed, and delay possibly caused by intersection signal lamps, traffic jams and the like in the process, namely, arrival of a quasi-point is not considered; the time of the shared bicycle riding passengers in the model reaching the rail transit station obeys uniform distribution; passengers connected with buses in the model arrive at the bus station to wait according to the schedule, and the waiting time at the bus station is not considered; the passenger flow of each station in the model is the connection passenger flow to the rail transit station, and the passenger flow between stations is not considered; the passengers of the model take the nearest next train after arriving at the rail transit station, and the situation that the passengers cannot get on the train is not considered; the basic parameters in the model are obtained according to field investigation or actual experience.

Further, the information of the bus stop and the shared bicycle stop in the second step comprises positions of the bus stop and the shared bicycle stop, distance between the shared bicycle stop and the rail transit stop, and distance between the bus stop and the shared bicycle stop and the nearest district.

Further, the operation mode in the second step includes, but is not limited to, the arrival time of the bidirectional train at the early peak.

Further, the traffic behavior data in the second step includes a walking distance and a walking speed of the passenger, and an average transfer time of the passenger from the bus stop and the stop point to the rail transit station.

Further, the passenger flow of the station in the second step refers to the passenger flow of the public transport and the shared bicycle transfer connection rail traffic which are taken at the early peak of working days.

Further, the traffic data of the connection behavior comprises connection bus average running speed, stop time, single-car average riding speed, single-car supply quantity of each stop point, capacity, passenger time cost of bus travel, passenger time cost of shared bicycle travel, bus travel fare and unit fare parameters of shared bicycle travel.

Further, in the second step, the longitude and latitude of each bus stop, the shared bicycle stop and the rail transit station are extracted by using a map platform, and then the distance between the bus stop and the distance between the stop and the rail transit station are calculated according to the longitude and latitude, wherein the map platform is an open map platform, and comprises, but is not limited to, a hundred-degree map, a high-altitude map and the like.

Further, in the second step, the study was conducted with the two hours with the highest amount of travel in the morning as the early peak.

Further, step two, finding the nearest bus station from each cell, corresponding the cell to the bus station, and calculating the average walking distance from each bus station to the corresponding cellThe method comprises the steps of carrying out a first treatment on the surface of the Similarly, find the nearest stop point from each cell, calculate the average walking distance of each cell and the corresponding stop point +.>And the shared bicycle stop points selected by the travel of the same district are corresponding to the bus stops.

Further, in the second step, the bus passenger flow is obtained through bus IC card swiping and code scanning data, riding data of the shared bicycle is identified through mobile phone signaling and shared bicycle data, and the passenger flow of riding connection is obtained. And adding the two, wherein the added result is the total regional rail transit junction passenger flow.

Further, the third specific step is as follows:

step one, obtaining the walking arrival time of the passengers traveling according to the parameter data obtained in the step twoDistance between stations->Shared bicycle connection riding distance +.>Number of stop stations along bus l>And station stop time Train arrival time, stop x bicycle supply +.>And travel demand of the leased bicycle at the point +.>；

(II) according to the distance between stationsAnd sharing bicycle connection riding distance +.>Can obtain the connection maleTraffic time->And sharing bicycle riding time->According to the number of along-way stop stations of the connected bus l +.>And site stop time->Can obtain the whole stop time +.>The method comprises the steps of carrying out a first treatment on the surface of the The transfer waiting time of the connected buses and the rail transit can be obtained according to the arrival time of the train, and is expressed as +.>Wherein->For the arrival time of the nth train, < >>Indicating the arrival time of the kth bus of the connected bus l,/>The average time from getting off to walking to waiting at the platform layer of the rail transit station is given to passengers; the time that the passenger waits for the bus to be connected at the bus stop can be expressed as +.>Wherein->Indicating the departure number of buses within one hour by connecting buses, wherein the departure time of buses can be adjusted>Calculating to obtain; transfer of shared bicycle travel passengersWaiting time can be expressed as half of the time interval of two adjacent shifts of the rail transit train, namely +.>；

(III) according to the parking point x bicycle supply amountAnd travel demand of the leased bicycle at the point +.>Defining a borrowing and returning difference penalty cost expressed as +. >Wherein->A return difference penalty coefficient;

(IV), dividing the situations of returning the riding vehicle into three categories:

the method comprises the steps of carrying out a first treatment on the surface of the Wherein->Respectively representing the proportion of the time length of the three conditions of the vehicle returning to the empty space to the total time length of the early peak in the early peak time window, and simultaneously defining the residual vehicle ∈ ->And 5, representing the condition of less residual parking spaces,

for the time cost caused by insufficient capacity of the parking point, the method is also divided into three cases, and when the residual parking space is more, the time for the rider to return the vehicle is as followsThe method comprises the steps of carrying out a first treatment on the surface of the When the remaining parking space is less, the rider returns to the bicycle at the same time>The method comprises the steps of carrying out a first treatment on the surface of the When there is no remainderWhen in parking space, if the rider selects the parking point nearest to the current position to return the vehicle, the vehicle returning time is +.>，/>Representing the distance of the rider from the nearest stop point at the current position, sharing the average bicycle return time；

And fifthly, the passenger cost of the bus is as follows:

（1）

including passenger walking arrival time, vehicle travel time, stop time, waiting time, transfer time, and fare cost,

in the formula (1):representing the cost per unit time of the passenger->Representing bus fare, & lt & gt>Representing the passenger flow of a bus stop i +.>Decision variables for 0-1: />

；

Sixth, the passenger cost of the shared bicycle is:

（2）

Including passenger walk-to-stop time, ride time, transfer waiting time, capacity penalty cost, borrow-and-unbalance penalty cost, fare cost,

in the formula (2):representing the penalty coefficient by still imbalance +.>Representing shared bicycle fare,/->Representing the travel passenger flow of the shared bicycle stop point x;

seventhly, the operation cost of the bus connection is as follows:

（3）；

in the formula (3):representing the unit cost of the bus operator of the connection +.>The departure number of the bus line l per hour is represented;

eighth, the operation cost of the shared bicycle is；

And (ninth), the objective function model constructed by the invention is as follows:

further, in the fourth step, when the problem of distributing the multiple traffic modes is solved, a logic model needs to be built to calculate the traffic mode selection probability, which is expressed as:

（5）

in formula (5):the passenger selection probability of two connection modes of bus connection and bicycle sharing are respectively represented, and the passenger selection probability is +.>Passenger costs for both connection modes are indicated separately,/->Representing the utility coefficient.

The calculation result of the model is determined by the passenger costs of two traffic modes together, and the passenger travel costs of two travel modes of each station in the current situation of the investigation region are calculated through the model in the third step And calculating, and then respectively calculating the passenger selection probabilities of two travel modes of each station according to the Logit model. The station with the connection bus selection probability far smaller than the shared bicycle selection probability is regarded as the station with the connection bus passenger flow minimum, and passengers mainly travel by the shared bicycle without considering the station when the subsequent connection bus route is optimized. And establishing an objective function of the lower model by taking the maximum sum of the bus trip selection probabilities as an objective.

Further, the constraint in step five is as follows:

（6）

in formula (6): constraint (1) is a capacity constraint in whichFor the vehicle capacity of line l>Is the plane of line lAverage departure frequency; constraint (2) is departure frequency constraint, < ->For interval time of two adjacent shifts, calculating to obtain +.>，/>Respectively representing the minimum departure frequency and the maximum departure frequency allowed; constraint (3) ensures that path i, j is serviced by only one bus route; constraint (4) ensures that the number of lines entered and issued by each site is the same; constraint (5) is a typical Miller-Tucker-Zemlin (MTZ) constraint, which aims to eliminate sub-loops that may occur in TSP problems; constraint (6) is a junction bus route length constraint, < ->Respectively representing the minimum length and the maximum length of the connected bus route; constraint (7) is the number constraint of the connected bus stops, < - >Respectively representing the minimum stop number and the maximum stop number of the connected buses; the constraint (8) is a passenger transfer waiting time constraint, so that the transfer waiting time of the passengers of the connected buses is ensured not to exceed the interval between two adjacent trains; constraint (9) is a shared bicycle stop capacity limit, ensuring that the number of passengers renting a bicycle at stop x does not exceed the number of vehicles supplied at stop, +.>The number of parked vehicles for the stop; constraint 10 is a decision variable constraint, when path i, j is selected, +.>=1, otherwise 0.

In the sixth step, a genetic algorithm is used for solving the model, the decision variable is encoded, and a real string encoding mode is adopted for the connection bus route selection.

Further, the sixth specific step is as follows:

and (one) importing coordinate points: and importing the acquired longitude and latitude information of the bus stop and the bicycle stop into Matlab software.

(II) basic parameter setting: inputting data of all basic parameters in an objective function model, and setting population size sizepop, maximum iteration number maxgen, code gap and crossover probability of a genetic algorithmProbability of mutation->。

Initializing a population: numbering each bus station, numbering the rail transit connection station as 0, numbering the rest bus stations from 1 to n, randomly generating a random array of 1 to n, and automatically generating at least the number of lines of the internal connection buses in the area according to the station scale in the research area and the reasonable limit of the number of the connection line stop stations And up to the number of lines>Aiming at the number of bus lines with different connections +.>Respectively generate->The number of the stations is divided into a plurality of groups by 1 random number, and the first number of each group is added with 0, so that the connection bus driving path can be represented.

And (IV) constraint checking: after obtaining initial solutions of the corresponding population sizes, inputting the constraint conditions established in the step five, wherein the solutions after meeting the constraint conditions are feasible initial solutions.

And (fifth) starting iteration: and inputting initial iteration times gen=0, carrying out iteration when gen < maxgen is satisfied, and ending the iteration when the maximum iteration times are reached when gen < maxgen is not satisfied.

And (six) calculating the fitness: and calculating the objective function value aiming at the initial solution, comparing the objective function values under different amounts of connected bus routes, and reserving an optimal scheme, wherein the fitness of the solution is the reciprocal of the objective function value.

(seventh) selecting: selecting a population by adopting a roulette mode, and leaving optimal individuals to become a offspring population according to the set grooves.

(eight) crossover: the crossover (Simulated binary crossover) is performed using analog binary, and the population is updated after the crossover operation.

(nine) mutation: the current population is updated using polynomial variation (polynomial mutation) for the real coded portion.

And (ten) after completing one iteration process, the iteration number gen+1 returns to (fifth) to perform the next iteration.

And (eleven) after the maximum iteration times are reached, obtaining connection bus paths traversing all current bus stops, selecting the stops as decision variables by taking the sum of bus selection probabilities as the maximum objective function, and obtaining a result again through a genetic algorithm.

(twelve) initializing population: the station selection uses a 0-1 binary code, "1" indicating that the station is docked, and "0" indicating that the station is not docked. According to the result of the first genetic algorithm, chromosomes with the same length are initialized.

Thirteenth constraint checking: the number of bus stops after stop selection should satisfy the constraint that the number of "1" s in the chromosome should be within the constraint range.

(fourteen) start iteration: the iterative process and steps are identical to the first genetic algorithm.

(fifteen) calculating an objective function and a fitness: and selecting stop stops by taking the maximum sum of bus selection probabilities as a target, wherein the adaptability is the target function value.

Sixthly, selecting multi-point crossover for crossover operator of binary code, and selecting multi-point mutation for mutation operator.

Seventeen, obtaining the optimal path of the connected bus after stopping and selecting and the departure schedule after the maximum iteration times are reached.

Further, the described apparatus comprises: and an acquisition module. The acquisition module comprises a site acquisition module, a passenger flow acquisition module and a parameter acquisition module.

Further, the acquisition module is used for acquiring the information of the stop points of all buses and shared bicycles in the research area in the second step, the passenger flow information of the internal connection track traffic in the research period and the basic parameter information of the traveling of passengers.

Further, the station acquisition module is used for acquiring information of all bus stations and shared bicycle stop points in the research area, wherein the information comprises positions of the bus stations and the shared bicycle stop points, distance between the bus stations, distance between the shared bicycle stop points and a rail transit station, and distance between the bus stations and the shared bicycle stop points and a nearest district.

Further, the passenger flow acquisition module is used for identifying and acquiring the passenger flow of the shared bicycle transfer rail traffic of each station taking the connection bus and each stop riding in the study period.

Further, the parameter acquisition module is used for acquiring average speeds of bus connection, bicycle sharing and walking; fare, transfer spending time, walking to stop distance, unit travel cost of passengers and unit operation cost of connecting buses and sharing bicycles; basic parameter information such as bicycle stop point capacity, supply quantity and the like is shared.

Furthermore, a calculation module is provided for importing the information and parameters acquired by the acquisition module into the model established in the third step to the sixth step, and performing calculation and iteration operation through a genetic algorithm until the iteration is finished.

Further, an output module is provided for determining the optimal path of the connected bus and the final departure schedule obtained by iterative calculation in the calculation module, and outputting the circuit diagram and the schedule in an intuitively visible manner.

Further, the apparatus includes a memory, a processor, and a computer code program executable with the mathematical model and the genetic algorithm.

Further, the memory is used for storing the computer program. The processor is configured to read the computer program in the memory and execute the steps three-six of the claims.

Further, a computer program is stored, and after the program is read by the processor, the contents of the steps three to six of the claims are executed.

Compared with the prior art, the invention has the beneficial effects that: 1. the joint optimization method of the connection bus route and the dispatching under the influence of the shared bicycle is considered, the total cost of taking the bus by the passengers is reduced through the optimization of the connection bus route and the dispatching moment, the selection probability of the connection bus by the passengers is increased, the passenger flow is increased, and the use tendency of the connection bus and the shared bicycle is balanced; 2. when the use cost of shared bicycle passengers is analyzed, the problem of 'difficulty in borrowing a vehicle' caused by untimely allocation of the vehicle in the peak period and the problem of 'difficulty in returning the vehicle' caused by insufficient capacity of a parking lot in reality are considered, so that the method has practical significance; 3. the joint optimization of the path and the dispatch is carried out on the connected buses, so that the waiting time and the time of the passengers can be effectively reduced, the time cost of the passengers is reduced, an optimization thought is provided for bus companies, the advantages of buses can be brought into play, vicious circle in competition with shared bicycles is changed, and better travel service is provided for citizens.

Drawings

FIG. 1 is a schematic flow diagram of a track traffic connection bus route and scheduling optimization method based on shared bicycle travel influence;

FIG. 2 is a flowchart of a genetic algorithm for model solving in accordance with the present invention;

FIG. 3 is a schematic diagram of an optimal path of a connection bus after optimization by a genetic algorithm in the embodiment of the invention;

FIG. 4 is a departure schedule optimized by a genetic algorithm according to an embodiment of the present invention;

FIG. 5 is a schematic block diagram of an embodiment of the present invention;

fig. 6 is a schematic diagram of a device structure according to an embodiment of the present invention.

Description of the embodiments

The flow of the present invention is further illustrated by the following description in conjunction with the accompanying drawings, and the following examples are intended to describe the invention more fully and in detail, and are also within the scope of the invention.

Referring to fig. 1-2, the invention relates to a track traffic connection bus route and scheduling optimization method based on shared bicycle travel influence, which comprises the following steps:

step four: constructing a Logit model of trip mode selection probability;

Step two is refined again for ease of understanding. The method comprises the following specific steps:

and (I) extracting information of all bus stops and shared bicycle stop points in the research area. The information comprises the positions of bus stops and shared bicycle stop points, the distance between the bus stops, the distance between the shared bicycle stop points and the rail transit stops, and the like. Finding the nearest bus station from each district, corresponding the district to the bus station, and calculating the average walking distance from each bus station to the corresponding districtThe method comprises the steps of carrying out a first treatment on the surface of the Similarly, finding the nearest stop point from each cell, and calculating each cell and pairAverage walking distance of stop point +.>And the shared bicycle stop points selected by the travel of the same district are corresponding to the bus stops. The steps include the following: content 1, adopting a map platform to extract longitude and latitude information of all connection bus stations and shared bicycle stop points in an area; content 2, calculating the distance between bus stations according to longitude and latitude information >And the distance of the shared bicycle stop x from the rail transit transfer stop y +.>。

And (II) investigating basic information of the plugging process in the embodiment. The steps include the following: content 1, investigating the current situation of a study area, namely passenger flow of the transit transfer rail transit taken by each bus station; content 2, investigating the current situation of a research area, namely riding the passenger flow of shared bicycle transfer rail traffic; and 3, investigating the arrival time of the rail transit train.

The content 1 adopts the bus IC card to punch cards and the bus code to scan code data to obtain the number of passengers at each station, and the bus connection is short, so that the bus connection mainly bears the demand of transfer passenger flow, and the number of passengers on the bus can be defaulted to be the passenger flow of transfer rail transit. The content 2 obtains the passenger flow of the vehicle connection at the entrance and exit of the rail transit station after the vehicles are borrowed from the parking points of the shared bicycles through the mobile phone signaling data; and 3, acquiring train departure intervals through a rail transit official platform, and calculating a train arrival schedule of the research station.

And thirdly, investigating basic physical parameters such as bus running speed, bicycle riding speed, passenger walking speed, walking to-station distance, transfer time, bus stop time, shared bicycle supply quantity, stop point capacity and the like. In the step, the speed data is the average speed approved by the main stream, and can be obtained from relevant documents, reports and other places, the corresponding information of each district, the shared bicycle stop point and the bus station in the walking-to-station distance is obtained through a map platform, and the rest information is obtained through field investigation.

And fourthly, analyzing the cost per unit time of the passenger trip and the operation cost per unit operation of the operator according to the average collection level of local residents and related literature research surveys.

The third step comprises the following contents:

content 1, inter-station distance obtained according to subdivision step (one) in step twoAnd sharing bicycle connection riding distance +.>The travel time of the connected bus 1 can be obtained>And sharing bicycle riding time->。

Content 2, the transfer waiting time of the connected buses and the rail transit can be obtained according to the arrival time of the train obtained in the subdivision step (II) in the step (II), and the transfer waiting time is expressed asWherein->For the arrival time of the nth train,indicating the arrival time of the kth bus of the connected bus, which is one of decision variables of the model, < +.>The average time from getting off to walking to waiting at the platform layer of the rail transit station is given to passengers; the time that the passenger waits for the bus to be connected at the bus stop can be expressed as +.>Wherein->Indicating the departure number of buses within one hour by connecting buses, wherein the departure time of buses can be adjusted>Calculating to obtain; the transfer waiting time of the passengers on the shared bicycle can be expressed as half of the time interval of two adjacent shifts of the rail transit train, namely。

Content 3, obtaining the walking arrival time of the passengers according to the parameter data obtained in the subdivision step (three) in the step two 。

Content 4, according to the number of the along-way stop stations of the connected busAnd in step two, subdividing the station stop time obtained in step (three)>Can obtain the whole stop time +.>。

Content 5, parking Point x bicycle supply amount obtained according to subdivision step (three) in step twoAnd travel demand of the leased bicycle at the point +.>A return difference penalty cost is defined, expressed as +.>Wherein->Is a borrowing and returning difference punishment coefficient.

This is because of the cost of passengers due to the "hard to borrow" phenomenon, such as mismatching of bicycle supply and demand for lending, and improper deployment of the vehicle, which may occur during peak hours for shared bicycle stops.

When the shared bicycle is actually used and goes out, particularly in the early peak period, the phenomenon that travelers who connect the rail transit get back together and get back to the bicycle together often occurs, so that the parking station is not enough in capacity and the phenomenon of difficult returning to the bicycle occurs. Therefore, the invention classifies the situations of returning the riding vehicle into three categories:

the method comprises the steps of carrying out a first treatment on the surface of the Wherein->Respectively representing the proportion of the time length of the three conditions of the vehicle returning to the empty space to the total time length of the early peak in the early peak time window, and simultaneously defining the residual vehicle ∈ ->And 5, representing the condition of less residual parking spaces.

For the time cost caused by insufficient capacity of the parking point, the method is also divided into three cases, and when the residual parking space is more, the time for the rider to return the vehicle is as followsThe method comprises the steps of carrying out a first treatment on the surface of the When the remaining parking space is less, the rider returns to the bicycle at the same time>The method comprises the steps of carrying out a first treatment on the surface of the When there is no parking space left, assuming that the rider selects the nearest parking point to the current position to return the vehicle, the returning time is +.>，/>Representing the distance of the rider from the nearest stop point at the current position, sharing the average bicycle return time。

Content 7, the passenger cost of the docked bus can be expressed as:（1）

(1) Including passenger walking arrival time, vehicle travel time, stop time, waiting time, transfer time, and fare costs.

Wherein the method comprises the steps ofRepresenting the cost per unit time of the passenger->Indicating departure frequency of connected buses, +.>Representing a bus fare, in the embodiment a ticket system for paying for getting on bus is connected, irrespective of the distance taken,/for bus fare>Representing the passenger flow of a bus stop i +.>Decision variables for 0-1:

；

content 8, the passenger cost of the shared bicycle can be expressed as:

（2）

Wherein the method comprises the steps ofRepresenting the penalty coefficient by still imbalance +.>Representing shared bicycle fare,/->The travel passenger flow of the shared bicycle stop x is indicated. The shared bicycle fare comprises a plurality of forms of sleeve tickets such as secondary tickets, monthly tickets, annual tickets and the like, and the fare of different operators is also different. Thus in an embodiment, the shared bicycle fare is unified as the average price of each shared bicycle operator monthly ticket.

Content 9, the operation cost of the connected bus can be expressed as:

（3）；

wherein the method comprises the steps ofRepresenting the unit cost of the bus operator of the connection +.>Representing the number of departure per hour of the bus line l, the size of which is determined by the decision variable +.>And (5) determining.

Content 10, the operating costs of the shared bicycle may be expressed as。

Content 11, the objective function model constructed by the embodiment is:

in the fourth step, when the problem of distributing the multiple traffic modes is solved, a Logit model needs to be constructed to calculate the traffic mode selection probability, and the calculation is expressed as follows:（5）

wherein the method comprises the steps ofThe passenger selection probability of two connection modes of bus connection and bicycle sharing are respectively represented, and the passenger selection probability is +.>Passenger costs for both connection modes are indicated separately,/->Representing the utility coefficient.

The calculation result of the model is determined by the passenger costs of two traffic modes together, and the passenger travel costs of two travel modes of each site in the current situation of the investigation region are checked through the models in the content 7 and the content 8 in the third step And calculating, and then respectively calculating the passenger selection probabilities of two travel modes of each station according to the Logit model. />

The selection probability can not only represent the passenger flow volume of selecting the traffic mode, but also represent the dependence degree of the residential community residents on the traffic mode. For example, the probability of selecting a bus trip in a certain cell is larger, which means that the passenger flow of selecting the bus trip is more than that of sharing a bicycle, and the bus trip cost is lower. In this case, even if the total amount of passenger flow in the cell is smaller than that in other cells, the dependence on buses is higher, and the bus stops corresponding to the cells should be considered when designing the line. For example, a cell is closer to a rail transit station, the probability of selecting a bus trip is smaller, the passenger flow of selecting the bus trip is smaller, and the dependence degree of residents of the cell on the bus is lower. The travel cost of passengers at other stations can be reduced by considering that the station is not stopped when the line is designed. Therefore, the objective function of the lower model is established with the maximum sum of the bus trip selection probabilities as the objective. Therefore, not only the passenger flow of the connected bus is considered, the income of an operator is increased, but also the passenger flow is not large, and the method has more practical significance than the simple method which aims at the maximum passenger flow for the stations with higher bus dependence degree.

Constructing constraint conditions according to the established model, enabling the solved to meet the actual requirements, wherein the constraint conditions in the fifth step are as follows:

（6）

in formula (6): constraint (1) is a capacity constraint in whichFor the vehicle capacity of line l>The average departure frequency of line l; constraint (2) is departure frequency constraint, < ->For interval time of two adjacent shifts, calculating to obtain +.>，/>Respectively representing the minimum departure frequency and the maximum departure frequency allowed; constraint (3) ensures that path i, j is serviced by only one bus route; constraint (4) ensures that the number of lines entered and issued by each site is the same; constraint (5) is a typical Miller-Tucker-Zemlin (MTZ) constraint, which aims to eliminate sub-loops that may occur in TSP problems; constraint (6) is a junction bus route length constraint, < ->Respectively representing the minimum length and the maximum length of the connected bus route; constraint (7) is the number constraint of the connected bus stops, < ->Respectively representing the minimum stop number and the maximum stop number of the connected buses; the constraint (8) is a passenger transfer waiting time constraint, so that the transfer waiting time of the passengers of the connected buses is ensured not to exceed the interval between two adjacent trains; constraint (9) is a shared bicycle stop capacity limit, ensuring that the number of passengers renting a bicycle at stop x does not exceed the number of vehicles supplied at stop, +. >The number of parked vehicles for the stop; constraint 10 is a decision variable constraint, when path i, j is selected, +.>=1, otherwise 0.

Further, the sixth specific step is as follows:

Initializing a population: numbering each bus station, numbering the rail transit connection station as 0, numbering the rest bus stations from 1 to n, randomly generating a random array of 1 to n, and automatically generating at least the number of lines of the internal connection buses in the area according to the station scale in the research area and the reasonable limit of the number of the connection line stop stationsAnd up to the number of lines>Aiming at the number of bus lines with different connections +.>Respectively generate->The number of the stations is divided into a plurality of groups by 1 random number, and the first number of each group is added with 0, so that the connection bus driving path can be represented.

(eight) crossover: the invention uses an analog binary crossover (Simulated binary crossover) that is more suitable for real number coding, which has better effect on local search in real number coded chromosomes. After the crossover operation, the current population is updated.

(nine) mutation: similar to the crossover operation, the current population is updated for real number encoding using better performing polynomial variation (polynomial mutation).

And (eleven) after the maximum iteration times are reached, the connection bus route traversing all the current bus stops is obtained, but the passenger flow of part of stops is lower, the bus selection probability is not high, and the trip cost of passengers and the operation cost of bus companies can be reduced by considering the stop-skipping operation. Therefore, the maximum sum of bus selection probabilities is required to be used as an objective function, the station selection is used as a decision variable, and the result is obtained through a genetic algorithm again.

Sixteenth genetic operator and iterative process are similar to (7) -10, but for binary coded crossover operator selection multi-point crossover, mutation operator selection multi-point mutation.

Example 1: referring to fig. 3-4, the embodiment of the invention selects sheep intestine village stations of the Kunming rail transit line 2 as the study object, and defines the study area by taking red cloud road, longquan road, lin road and Beijing road as the boundary. The area is provided with 23 public buses and 34 shared bicycle parking areas, and longitude and latitude information is extracted through a Goldmap open platform to calculate the distance.

Through investigation, the values of partial indexes in the model are as follows: average walking speedMean running speed of connected buses>Bus average stop time->Average time for passengers to get off and walk to rail transit station +.>Bus fare connection->Cost per unit time of passengerBus operation cost->Average riding speed, +.>。

According to the limit of the site scale and the stop number in the research area, the minimum number of buses connected in the area is regulatedAnd maximum number->. For different numbers of lines in the area +.>Respectively generate->-1 random number groups the number of stations into several groups. The total number of the stop stations of each connected bus is 7-12 in constraint conditions, and the stop stations are in the range of about 23 bus stations >=2, 3. Generating a random number "10" using the randi function, the 10 th number will be dyedThe color bodies are cut off and divided into two connected bus paths.

In the embodiment, the length of the current solution is calculated according to the distance between stations, and then the arrival time of the reasonable rail transit station of the connected bus is calculated according to the time when the Kunming subway No. 2 train arrives at the sheep intestine village station in the range of 7:00-9:00. And judging whether to get off or not according to the transfer passenger flow data obtained through investigation by the following rules. After the departure time is obtained, the average waiting time of the passengers is calculated, and finally the objective function value can be obtained.

In an embodiment, the maximum passenger capacity of the docked bus is 70 people. Scene 1, if more than 70 passengers take the first bus transfer rail transit, the passengers must go from the first bus to the next bus; scene 2, if the number of passengers of the first car is less than 70, the first car is not sent, and only the second car is sent; and 3, if the number of passengers in the first trip plus the number of passengers in the next trip exceeds 70, starting the first trip, and judging whether to start after the second trip is continuously added with the number of passengers in the next trip.

And finally calculating to obtain the optimal path of the connected bus and the departure schedule thereof.

Referring to fig. 5-6, an apparatus includes an acquisition module. The method is used for acquiring information of stop points of all buses and shared bicycles in a study area, passenger flow information of internal connection track traffic in a study period and basic parameter information of passenger travel.

The acquisition module comprises a site acquisition module, a passenger flow acquisition module and a parameter acquisition module.

The station acquisition module is used for acquiring information of all bus stations and shared bicycle stop points in the research area, wherein the information comprises positions of the bus stations and the shared bicycle stop points, distance between the bus stations, distance between the shared bicycle stop points and a rail transit station, and distance between the bus stations and the shared bicycle stop points and a nearest district.

The passenger flow acquisition module is used for identifying and acquiring the passenger flow of the shared bicycle transfer rail traffic of each station taking the connection bus and each stop riding in the study period.

The parameter acquisition module is used for acquiring the average speed of connecting buses, sharing bicycles and walking; fare, transfer spending time, walking to stop distance, unit travel cost of passengers and unit operation cost of connecting buses and sharing bicycles; basic parameter information such as bicycle stop point capacity, supply quantity and the like is shared.

The calculation module is used for importing the information and the parameters acquired by the acquisition module into the model established in the third step and the sixth step, and performing calculation and iteration operation through a genetic algorithm until the iteration is finished.

The system comprises a calculation module, a calculation module and a bus transfer module, wherein the calculation module is used for calculating the optimal path of the connected bus and the final departure schedule of the bus, and outputting the route diagram and the schedule in an intuitive and visible mode.

The apparatus further comprises a computer code program comprising a memory, a processor and executable mathematical models and genetic algorithms as described above.

The memory is used for storing the computer program. The processor is configured to read the computer program in the memory and execute the steps three-six of the claims.

To accomplish the above technical requirements, the memory is connected to the processor through a data bus, but may also be connected to the processor through an address bus or a control bus. When the computer runs, the computer program stored in the memory can be read by the processor through the computer instruction, and the contents of the step three-step six are executed to optimize the connected buses in the investigation region. In embodiments in which the computer program is written using Matlab software, the program code may be written using other software or languages such as Python.

Among them, computer storage media include volatile storage media and nonvolatile storage media. Volatile storage media are storage media which can not continuously store data content after a computer is powered off, and mainly comprise a random access memory (Random Access Memory, RAM), a dynamic random access memory (SDRAM) and a Static Random Access Memory (SRAM); the nonvolatile storage medium is a storage medium capable of storing data content for a long period of time even after power off of calculation, and mainly includes a Read Only Memory (ROM), a one-time programmable Memory (One Time Programable ROM, OTPROM), a rewritable Memory (Erasable Programmable ROM, EPROM), an electrically erasable Memory (Electrically Erasable Programmable ROM, EEPROM), an optical disk, a floppy disk, and a mechanical hard disk.

The above description is merely an example of the present invention, but the scope of the present invention is not limited thereto, and modifications and variations may be made thereto by those skilled in the art, and those modifications and variations are intended to be included within the scope of the present invention.

Claims

1. The track traffic connection bus route and scheduling optimization method based on the shared bicycle travel influence is characterized by comprising the following steps of:

step four: constructing a Logit model of trip mode selection probability;

2. The track traffic connection bus route and dispatch optimization method based on shared bicycle travel influence as claimed in claim 1, wherein the method is characterized by comprising the following steps: the information of the bus stop and the shared bicycle stop in the second step comprises the positions of the bus stop and the shared bicycle stop, the distance between the shared bicycle stop and the rail transit station, and the distance between the bus stop and the shared bicycle stop and the nearest district; the operation mode in the second step comprises, but is not limited to, the arrival time of the early-peak bidirectional train; the traffic behavior data in the second step comprises the walking distance and the walking speed of the passengers and the average transfer time of the passengers from the bus stops and the stop points to the rail transit stops; the passenger flow of the station in the second step refers to the passenger flow of the public transport and the shared bicycle transfer connection track traffic which are taken at the early peak of working days; the traffic data of the connection behavior comprise connection bus average running speed, stop time, single-car average riding speed, single-car supply quantity and capacity of each stop point, bus travel passenger time cost, shared bicycle travel passenger time cost, bus travel fare and shared bicycle travel unit fare parameters.

3. The track traffic connection bus route and dispatch optimization method based on shared bicycle travel influence according to claim 1 or 2, wherein the method is characterized in that: calculating the distance between each bus station and the distance between each stop point and each rail transit station, wherein the distance is calculated according to longitude and latitude, and the longitude and latitude data are derived from a map platform; the operation mode is that the early peak time is selected as two hours with highest travel amount in the morning in one day; selecting an entrance and exit with the largest passenger residence people flow as a travel starting point, and measuring the travel starting point to the nearest bus stopAnd stop point->Is>，/>The method comprises the steps of carrying out a first treatment on the surface of the The bus traffic is obtained by the bus IC card swiping and the code scanning data of the bus, the riding data of the shared bicycle is identified by the mobile phone signaling and the shared bicycle data, the riding traffic is obtained, and the sum of the two traffic is the total regional rail traffic docking traffic.

4. The track traffic connection bus route and dispatch optimization method based on shared bicycle travel influence as claimed in claim 1, wherein the method is characterized by comprising the following steps:

the third specific step is as follows:

step one, obtaining the walking arrival time of the passengers traveling according to the parameter data obtained in the step two Distance between stationsShared bicycle connection riding distance +.>Number of stop stations along bus l>And site stop time->Train arrival time, stop x bicycle supply +.>And travel demand of the leased bicycle at the point +.>；

(II) according to the distance between stationsAnd sharing bicycle connection riding distance +.>Can obtain the running time of the connected busAnd sharing bicycle riding time->According to the number of along-way stop stations of the connected bus l +.>And site stop time->Can obtain the whole stop time +.>The method comprises the steps of carrying out a first treatment on the surface of the The transfer waiting time of the connected buses and the rail transit can be obtained according to the arrival time of the train, and is expressed as +.>Wherein->For the arrival time of the nth train, < >>Indicating the arrival time of the kth bus of the connected bus l,/>The average time from getting off to walking to waiting at the platform layer of the rail transit station is given to passengers; the time that the passenger waits for the bus to be connected at the bus stop can be expressed as +.>Wherein->Indicating the departure number of buses within one hour by connecting buses, wherein the departure time of buses can be adjusted>Calculating to obtain; the transfer waiting time of the passengers on the shared bicycle can be expressed as half of the time interval of two adjacent shifts of the rail transit train, namely +.>；

(III) according to the parking point x bicycle supply amount And travel demand of the leased bicycle at the point +.>Defining a borrowing and returning difference penalty cost expressed as +.>Wherein->A return difference penalty coefficient;

for the time cost caused by insufficient capacity of the parking point, the method is also divided into three cases, and when the residual parking space is more, the time for the rider to return the vehicle is as followsThe method comprises the steps of carrying out a first treatment on the surface of the When the remaining parking space is less, the rider returns to the bicycle at the same time>The method comprises the steps of carrying out a first treatment on the surface of the When there is no parking space left, assuming that the rider selects the nearest parking point to the current position to return the vehicle, the returning time is +.>，/>Representing the distance of the rider from the nearest stop point at the current position, sharing the average bicycle return time；

And fifthly, the passenger cost of the bus is as follows:

（1）

(1) Including passenger walking arrival time, vehicle travel time, stop time, waiting time, transfer time, and fare cost,

in the formula (1):representing the cost per unit time of the passenger- >Representing bus fare, & lt & gt>Representing the passenger flow volume at the bus stop i,decision variables for 0-1:

；

sixth, the passenger cost of the shared bicycle is:

（2）

seventhly, the operation cost of the bus connection is as follows:

；

eighth, the operation cost of the shared bicycle is；

5. the track traffic connection bus route and dispatch optimization method based on shared bicycle travel influence as claimed in claim 1 or 4, wherein the method comprises the following steps: the Logit model in the fourth step is（5）

6. The track traffic connection bus route and dispatch optimization method based on shared bicycle travel influence as claimed in claim 1, wherein the method is characterized by comprising the following steps: the constraint conditions of the fifth step are as follows:

（6）

in formula (6): constraint (1) is a capacity constraint in whichFor the vehicle capacity of line l>The average departure frequency of line l; constraint (2) is departure frequency constraint, < ->For interval time of two adjacent shifts, calculating to obtain +.>，/>Respectively representing the minimum departure frequency and the maximum departure frequency allowed; constraint (3) ensures that path i, j is serviced by only one bus route; constraint (4) ensures that the number of lines entered and issued by each site is the same; constraint (5) is a typical Miller-Tucker-Zemlin (MTZ) constraint, which aims to eliminate sub-loops that may occur in TSP problems; the constraint (6) is a junction bus path length constraint,respectively representing the minimum length and the maximum length of the connected bus route; constraint (7) is the number constraint of the connected bus stops, < ->Respectively representing the minimum stop number and the maximum stop number of the connected buses; the constraint (8) is a passenger transfer waiting time constraint, so that the transfer waiting time of the passengers of the connected buses is ensured not to exceed the interval between two adjacent trains; constraint (9) is a shared bicycle stop capacity limit, ensuring that the number of passengers renting a bicycle at stop x does not exceed the number of vehicles supplied at stop, +. >The number of parked vehicles for the stop; constraint 10 is a decision variable constraint, when path i, j is selected, +.>=1, otherwise 0.

7. The track traffic connection bus route and dispatch optimization method based on shared bicycle travel influence as claimed in claim 1, wherein the method is characterized by comprising the following steps: the specific steps of the step six are as follows:

and (one) importing coordinate points: importing the acquired longitude and latitude information of the bus stop and the bicycle stop into Matlab software;

(II) basic parameter setting: inputting data of all basic parameters in an objective function model, and setting population size sizepop, maximum iteration number maxgen, code gap and crossover probability of a genetic algorithmProbability of mutation->；

Initializing a population: numbering each bus station, numbering the rail transit connection station as 0, numbering the rest bus stations from 1 to n, randomly generating a random array of 1 to n, and automatically generating at least the number of lines of the internal connection buses in the area according to the station scale in the research area and the reasonable limit of the number of the connection line stop stationsAnd up to the number of linesAiming at the number of bus lines with different connections +.>Respectively generate->The number of the stations is divided into a plurality of groups by 1 random number, and the first number of each group is added with 0, so that the connection bus driving path can be represented;

And (IV) constraint checking: after obtaining initial solutions of the corresponding population sizes, inputting established constraint conditions, wherein the solutions after meeting the constraint conditions are feasible initial solutions;

and (fifth) starting iteration: inputting initial iteration times gen=0, carrying out iteration when gen < maxgen is met, and ending the iteration when the maximum iteration times are not met;

and (six) calculating the fitness: calculating objective function values aiming at the initial solutions, comparing the objective function values under different amounts of connected bus lines, and reserving an optimal scheme, wherein the fitness of the solutions is the reciprocal of the objective function values;

(seventh) selecting: selecting a population by adopting a roulette mode, and leaving optimal individuals to become a child population according to the set grooves;

(eight) crossover: crossing (Simulated binary crossover) with analog binary, and updating the population after the crossing operation;

(nine) mutation: updating the current population with polynomial variation (polynomial mutation) for the real coded portion;

(ten) after finishing one iteration process, the iteration number gen+1 returns to (fifth) to perform the next iteration;

(eleven) after the maximum iteration times are reached, obtaining connection bus paths traversing all current bus stops, taking the sum of bus selection probabilities as the maximum objective function, selecting the stops as decision variables, and obtaining a result again through a genetic algorithm;

(twelve) initializing population: the station selection uses a 0-1 binary code, "1" indicating that the station is docked, and "0" indicating that the station is not docked. Initializing chromosomes with the same length according to the result of the first genetic algorithm;

thirteenth constraint checking: the number of bus stops at the selected stops should meet the constraint, namely the number of 1's in the chromosome is within the constraint range;

(fourteen) start iteration: the iterative process and steps are the same as the first genetic algorithm;

(fifteen) calculating an objective function and a fitness: selecting stop stops by taking the maximum sum of bus selection probabilities as a target, wherein the adaptability is the target function value;

sixthly, selecting multi-point crossover for crossover operators of binary codes and selecting multi-point mutation for mutation operators;

8. Track traffic connection bus route and dispatch optimizing device based on shared bicycle trip influence, characterized in that, the device that describes includes:

the acquisition module is used for acquiring the information of the stop points of all buses and shared bicycles in the study area in the first step and the second step, the passenger flow information of the internal connection track traffic in the study period and the basic parameter information of the traveling of passengers;

The calculation module is used for calculating the information acquired by the acquisition module, and the calculation method comprises the following steps: thirdly, performing calculation and iterative operation on the model related to the step six through a genetic algorithm;

the output module is used for determining the optimal path of the connected bus and the final departure schedule obtained by iterative calculation in the calculation module, and outputting the circuit diagram and the schedule in an intuitive and visible mode.

9. The track traffic junction bus route and schedule optimizing device based on the shared bicycle trip effect according to claim 8, further comprising a memory, a processor and a computer code program capable of executing the mathematical model and the genetic algorithm, wherein the memory is used for storing the computer program, and the processor is used for reading the computer program in the memory and executing the contents in the third step and the sixth step.

10. The computer storage medium for optimizing the track traffic connection bus route and the dispatching based on the shared bicycle travel influence is characterized by storing a computer program, and executing the contents of the step three-step six after the program is read by a processor.