CN115587657A - Station determining and route optimizing method for night customized bus - Google Patents
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Abstract
The invention discloses a method for determining stations and optimizing lines of a night customized bus, which comprises the following steps: (1) Surveying, determining and recording the position of each destination and the number of passengers; (2) Adopting a DBSCAN algorithm, taking the maximum acceptable walking distance of passengers after getting off the bus as a neighborhood radius parameter, and taking the minimum clustering passenger number as a density threshold value to perform clustering analysis on destination scattered points to obtain a plurality of discrete points and clusters; (3) The discrete points are set as independent stations, and other clusters adopt a K-means algorithm considering the influence of the passenger number weight on the total walking distance to calculate a clustering center; (4) And adjusting the determined stations, and constructing a multi-route dynamic planning model which aims at minimizing the sum of the reserved cost, the running cost and the time cost of the passengers of the customized bus and takes the departure and arrival time windows, the passenger capacity and the like of the passengers as constraints, so as to determine a final route. The invention fully considers the service level of the customized bus, provides a multi-line dynamic planning algorithm which gives consideration to the travel time value cost of passengers and the bus operation cost, and provides reference for a large-scale junction passenger flow evacuation scheme at night.
Description
Technical Field
The invention belongs to the field of planning and management of public transportation and transportation, and particularly relates to a station determining and route optimizing method for customizing buses at night.
Background
The conventional urban traffic network is an integrated network which takes rail transit as a backbone and conventional public transport as a main body and is developed in a coordinated way in various traffic modes, and can better dredge the daytime passenger flow. Compared with a traffic network perfect in the daytime, the pressure of relieving night passenger flow is concentrated on taxies and network taxi appointments, and how to make a large-scale night passenger flow relieving method is a key research problem.
The customized public transport with flexible, fast and economical route has extremely high applicability to the evacuation of passengers aiming at the travel characteristics of large passenger flow, concentrated arrival time and dispersed destinations of the railway at night. However, the current research on the customized public transportation mainly focuses on the aspect of the operation mode of the commuting type customized public transportation, theoretical support for evacuation of large mass traffic arriving in a centralized mode is lacked, the consideration on the total walking distance of passengers in station setting is deficient, and the research on the aspect of balancing the time value cost of the passengers in line optimization is immature. Therefore, how to reasonably and scientifically determine the station, plan the route and ensure the intensification of the travel time of the passengers is still the problem to be solved in the prior art.
Disclosure of Invention
The invention provides a stop determining and route optimizing method for a night customized bus, which can effectively meet the outbound traffic demand of night hub passengers and aims at solving the technical problems that the existing customized bus research lacks theoretical support for the evacuation of intensively arriving large passenger flows, lacks the consideration of the total walking distance of passengers in stop setting, and is immature in research on the balance of time value cost of the passengers in route optimization. The main concerned factors of the invention are passenger destination source position, passenger drop amount distribution and road network conditions, which intuitively show the distribution of the passenger trip starting and ending points and are the basis for determining the site layout. Under the background, the service level of the customized bus is fully considered, based on actual passenger demands, the customized bus stop clustering is carried out by adopting a DBSCAN algorithm and an improved K-means algorithm, and a multi-route dynamic planning algorithm considering both the travel time value cost of passengers and the bus operation cost is provided.
The specific scheme is as follows:
a method for determining stations and optimizing routes of customized buses at night comprises the following steps:
step S1: determining destinations where passengers leave the station in the hub, marking scattered positions of all the destinations on a map, and recording and counting corresponding positions and the number of passengers falling from each destination;
step S2: considering the requirements of special passengers on door-to-door service and the relative positions of destinations, adopting a DBSCAN algorithm which is not influenced by the distribution shape of clustering points as a basic algorithm, carrying out clustering analysis on the destination discrete points, dividing the destination discrete points into a plurality of discrete points and various clusters, and setting the discrete points as independent stations;
and step S3: determining a parameter K value, namely the positions of K clusters and an initial centroid point, according to each cluster class obtained by a DBSCAN algorithm by using a K-means algorithm as a basis, modifying judgment logic of distance clustering in the algorithm, further obtaining a clustering center of each cluster class, and determining the position of each cluster class website;
and step S4: marking all the set site positions, and finely adjusting unreasonable site positions according to actual conditions; after adjustment is finished, based on the established stop positions, a heuristic insertion algorithm is adopted, an initial solution of the route is obtained in a mode of inserting the destination into the route set, then a genetic algorithm is adopted to optimize the initial solution, and the obtained routes and all stops are marked on a map to obtain a final night customized bus route optimization scheme.
Preferably, the specific process of step S1 is:
step S11: marking the scattered positions of all destinations on a map by investigating the positions of all passenger destinations in a hub;
step S12: and recording and counting the positions of all destinations and the number of passengers falling, and sorting the positions and the number of the passengers falling into the destinations to be used as a data set.
Preferably, the specific process of step S2 is:
step S21: taking the maximum acceptable walking distance after passengers get off the bus as a neighborhood radius parameter, taking the maximum acceptable walking distance as a neighborhood radius parameter epsilon in a DBSCAN algorithm, taking the minimum number of clustered passengers as a density threshold parameter Minpts in the DBSCAN algorithm, and dividing destination scattered points into a plurality of discrete points and clusters;
step S22: each discrete point is set as a separate site.
Preferably, the specific process of step S3 is:
step S31: determining the value of a parameter K in a K-means algorithm according to each cluster type obtained by the DBSCAN algorithm, namely K clusters; then, calculating the average value of the position coordinates of each cluster station as the positions of K initial centroid points of a K-means algorithm;
step S32: the judgment logic for changing the distance clustering in the K-means algorithm is to consider the influence of the passenger number weight on the total walking distance so as to minimize the sum D of the walking distances of all the passengers at night, and is represented as:
in the formula, a h The distance from each point in the cluster to the cluster center; n is h The number of passengers at each point in the cluster is clustered; h is the number of cluster destinations; x is the number of h ,x z Respectively, the abscissa, y, of each point and cluster center in the cluster h ,y z Respectively are the vertical coordinates of each point in the cluster and the cluster center;
step S33: and combining the cluster center of each cluster as a station of each cluster with the discrete point station in the step S22 into a set to be used as a final station set.
Preferably, the specific process of step S4 is:
step S41: with the minimum sum of the customized bus operation cost and the passenger trip cost as a target, establishing a line dynamic planning model:
customized bus operation cost C s ：
C s ＝C b N+C f L b (4-1)
Cost per unit time value V of the passenger:
passenger total time value cost C generated by customized bus detour p ：
C p ＝V×ΔT (4-3)
An objective function:
in the formula, C b Is an inherent cost; c f Fuel cost per mileage; l is b The running mileage of the vehicle is taken; n is the number of vehicles; z is the annual income of passengers;
the system is used for calculating the reward obtained by using the riding time for overtime work; t is the average value of the effective working time in one year; the delta T is the custom bus detour delay time;
the path length of the k-th vehicle between i and j is obtained; delta T j The delay time for the vehicle to reach station j; w j The total number of people getting off at station j; k is a vehicle number, K = {1,2, \8230;, K }; i, j is the number of the vehicle parking station, I, j = {1,2, \8230 =, { I }; k and I are respectively the maximum value of the number of vehicles and the maximum value of the number of stations;
is a decision variable;
step S42: establishing constraint conditions according to four aspects of departure time constraint, arrival time constraint, passenger capacity constraint and detour distance constraint of the vehicle:
customizing bus departure time T d ：
T α +θ≤T d ≤T α +T m (4-5)
Customizing bus arrival time T bi ：
T ti +θ≤T bi ≤T ti +ΔT (4-6)
Passenger capacity constraint:
and (4) constraint of detour distance:
in the formula, theta is the average time required for passengers to walk to reach the customized bus departure point; t is m The maximum waiting time that can be tolerated by the passenger; t is α Train arrival time; t is ti The time required for the taxi to directly reach the station i comprises the waiting time of the passenger;
the total number of passengers of the kth vehicle;
the maximum passenger capacity of the kth vehicle; omega is a nonlinear coefficient;
the length of the route of the Kth vehicle between the stations i, j is obtained;
the length of the route of the Kth vehicle between the stations r, j is shown;
the length of the route of the Kth vehicle between the stations i and r;
step S43: adopting a heuristic insertion algorithm, obtaining an initial solution of the path by inserting the destination into the path set, and then optimizing the initial solution by adopting a genetic algorithm;
step S44: and marking the obtained route and each stop on a map to obtain a final customized bus route optimization scheme.
A stop determining and route optimizing system for customizing buses at night comprises:
a data set sorting module: determining destinations where passengers leave the station in the hub, marking scattered positions of all the destinations on a map, recording and counting corresponding positions of the destinations and the number of passengers falling from each destination, and arranging the positions to be used as a data set;
a cluster analysis module: considering the requirements of special passengers on door-to-door service and the relative positions of destinations, adopting a DBSCAN algorithm which is not influenced by the distribution shape of clustering points as a basic algorithm, carrying out clustering analysis on destination discrete points, dividing the destination discrete points into a plurality of discrete points and clusters, and setting the discrete points as independent stations;
a site determination module: determining a parameter K value, namely the positions of K clusters and an initial centroid point, according to each cluster class obtained by a DBSCAN algorithm by using a K-means algorithm as a basis, modifying judgment logic of distance clustering in the algorithm, further obtaining a clustering center of each cluster class, and determining the position of each cluster class station;
a line optimization module: marking all the set site positions, and finely adjusting unreasonable site positions according to actual conditions; after adjustment is finished, based on the established station positions, a heuristic insertion algorithm is adopted, an initial solution of the paths is obtained in a mode of inserting the destinations into the path set, then a genetic algorithm is adopted to optimize the initial solution, and the obtained lines and stations are marked on a map to obtain a final night customized bus line optimization scheme.
A stop determining and route optimizing system terminal for a night customized bus comprises a memory, a processor and at least one instruction or at least one section of computer program which is stored on the memory and can be loaded and run on the processor, and is characterized in that the processor loads and runs the at least one instruction or the at least one section of computer program to realize the stop determining and route optimizing method for the night customized bus.
A non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method for stop determination and route optimization for a night time bus order.
Compared with the prior art, the station determining and route optimizing method for customizing the bus at night has the following beneficial effects:
1) The invention effectively utilizes the demand data to determine the number of the stations, furthest reduces the number of the stations on the basis of meeting the requirement of convenience of passengers in traveling, avoids waste and saves the construction cost;
2) The method is based on two clustering algorithms to analyze special multidimensional data consisting of the scattered point position and the passenger drop of the destination, effectively distinguishes density difference of demand distribution, is reasonable and simple and convenient to calculate, and ensures the scientificity and accuracy of follow-up work.
3) The invention combines DBSCAN algorithm and improved K-means algorithm to carry out customized bus stop clustering; the DBCSCAN algorithm is not affected by scatter shapes and can automatically classify data sets, but does not find centroid points. The K-means algorithm can solve the clustering center, but is greatly influenced by the selection of the initial K value and the initial centroid point. Based on the advantages and the disadvantages of the two algorithms, the invention firstly utilizes the DBCSCAN algorithm to cluster the scattered points of the known destination and divide the scattered points into the discrete points and the clusters, thereby ensuring the accessibility of the discrete points, simultaneously providing reasonable K value and selection of initial centroid points for the next solution of the K-means algorithm clustering center, and greatly improving the accuracy and the rationality of the clustering effect.
4) The method is different from the judgment logic of the distance formula in the traditional K-means algorithm, and modifies the judgment logic into consideration of the influence of the passenger number weight on the total walking distance, so that the sum of all walking distances of passengers at night is minimum and is used as the iteration premise of solving the clustering center, and the time value of the passengers is guaranteed.
5) The model objective function of the invention ensures that the sum of the travel time cost of the passengers is minimum, so that the planning result is influenced by the weight of the number of passengers getting off at different stations. The time value cost of passengers plays a leading role, and the path selection is changed from the shortest path selection scheme to a selection scheme considering the weight of the number of passengers.
6) The invention can select the station site based on the starting and ending point positions of the passengers, simultaneously ensures the accessibility of each discrete point, and gives consideration to the time value cost and the bus operation cost of the passengers, so that the walking distance of the passengers is shortened, and the bus route is optimized. The system has important reference significance for solving the problem of passenger evacuation in railway stations, airports and the like which are lack of matched traffic service systems and have large-batch passenger arrival at night.
Drawings
Fig. 1 is a flow chart of a method for determining stations and optimizing routes for a night-time customized bus according to the present invention.
Fig. 2 is a distribution diagram of each discrete point and each cluster class after clustering by the DBSCAN algorithm.
FIG. 3 is a diagram of the clustering center after the improved K-means algorithm clustering.
Detailed Description
The present invention is further described below in conjunction with the drawings and the embodiments, it is to be understood that the embodiments described below are intended to facilitate the understanding of the present invention and do not have any limiting effect thereon.
Referring to fig. 1, the steps of a specific implementation process and key technologies used by the method are shown, and a method for determining stations and optimizing routes of a customized bus at night comprises the following steps:
step S1: and determining destinations where passengers leave the station in the hub, marking scattered positions of all the destinations on a map, and recording and counting corresponding positions and the number of passengers falling from each destination.
The specific process of the step S1 comprises the following steps:
step S11: marking scattered positions of all destinations on a map by investigating the positions of all passenger destinations in a hub;
step S12: and recording and counting the positions of all destinations and the number of passengers falling, and sorting the data set serving as an algorithm.
Step S2: the method fully considers the requirements of special passengers (such as passengers with large luggage quantity and inconvenient walking) on door-to-door service and the relative positions of destinations, adopts a DBSCAN algorithm which is not influenced by the distribution shape of clustering points as a basic algorithm, performs clustering analysis on the destination discrete points, divides the destination discrete points into a plurality of discrete points and clusters, and sets the discrete points as independent stations. As shown in fig. 2, fig. 2 is a distribution diagram of discrete points and clusters after clustering by the DBSCAN algorithm, that is, clusters known destination discrete points and divides the known destination discrete points into discrete points and clusters.
The specific process of step S2 is:
step S21: dividing destination scattered points into a plurality of discrete points and each cluster type by taking the maximum acceptable walking distance of passengers after getting off the bus as a neighborhood radius parameter epsilon in the DBSCAN algorithm and taking the minimum clustering passenger number as a density threshold parameter Minpts in the DBSCAN algorithm;
step S22: each discrete point is set as a separate station.
And step S3: a K-means algorithm is adopted as a basis, a parameter K value (namely K clusters) and the position of an initial centroid point are determined according to each cluster type solved by a DBSCAN algorithm, then the judgment logic of distance clustering in the algorithm is modified, the clustering center of each cluster type is further solved, and the position of each cluster type station is determined. As shown in fig. 3, fig. 3 is a cluster center diagram after the improved K-means algorithm clustering, that is, a final station diagram obtained by determining a parameter K value and a position of an initial centroid point according to each cluster type obtained by the DBSCAN algorithm and then clustering.
The specific process of the step S3 is as follows:
step S31: and determining the value of the parameter K in the K-means algorithm according to each cluster type obtained by the DBSCAN algorithm, namely K clusters. Then, calculating the average value of the position coordinates of each cluster station as the positions of K initial centroid points of a K-means algorithm;
step S32: the judgment logic for changing the distance clustering in the K-means algorithm is to consider the influence of the passenger number weight on the total walking distance so as to minimize the sum D of the walking distances of all the passengers at night, and is represented as:
in the formula, a h The distance from each point in the cluster to the cluster center; n is a radical of an alkyl radical h The number of passengers at each point in the cluster is clustered; h is the number of cluster destinations; x is the number of h ,x z The abscissa, y, of each point and cluster center in the cluster h ,y z Respectively are the vertical coordinates of each point in the cluster and the cluster center.
Step S33: and combining the cluster center of each cluster as a station of each cluster with the discrete point station in the step S22 into a set to be used as a final station set.
And step S4: marking all the set site positions, and finely adjusting unreasonable site positions according to actual conditions. After the adjustment is finished, based on the established site position, a heuristic insertion algorithm is adopted, an initial solution of the path is obtained in a mode of inserting the destination into the path set, and then a genetic algorithm is adopted to optimize the initial solution.
The specific process of the step S4 is as follows:
step S41: aiming at minimizing the sum of the customized bus operation cost and the passenger trip cost, establishing a line dynamic planning model: customized bus operation cost C s ：
C s ＝C b N+C f L b (4-1)
Cost per unit time value V of the passenger:
total passenger time value cost C due to custom bus detour p ：
C p ＝V×ΔT (4-3)
An objective function:
in the formula, C b Is an inherent cost; c f Fuel cost per mileage; l is b The running mileage of the vehicle is taken; n is the number of vehicles; z is the annual income of passengers;
the system is used for calculating the reward obtained by using the riding time for overtime work; t is the average value of the effective working time in one year. The delta T is the custom bus detour delay time;
the path length of the k-th vehicle between i and j is obtained; delta T j The delay time for the vehicle to reach station j; w j The total number of people getting off at the station j; k is a vehicle number, K = {1,2, \8230;, K }; i, j is the vehicle stop station number, I, j = {1,2, \8230;, I }; k and I are respectively the maximum value of the number of vehicles and the maximum value of the number of stations;
are decision variables.
Step S42: the method comprises the following steps of establishing constraint conditions according to four aspects of departure time constraint, arrival time constraint, passenger capacity constraint and detour distance constraint of a vehicle:
customizing bus departure time T d ：
T α +θ≤T d ≤T α +T m (4-5)
Customizing bus arrival time T bi ：
T ti +θ≤T bi ≤T ti +ΔT (4-6)
And (3) passenger capacity constraint:
and (3) restricting the detour distance:
in the formula, theta is the average time required for passengers to walk to reach a customized bus departure point; t is m The maximum waiting time that can be tolerated by the passenger; t is a unit of α Train arrival time; t is ti The time required for a taxi to directly reach the station i (including the waiting time of passengers);
the total number of passengers of the kth vehicle;
the maximum passenger capacity of the kth vehicle; omega is a nonlinear coefficient;
the length of the route of the Kth vehicle between the stations i, j is obtained;
the length of the route of the Kth vehicle between the stations r, j is shown;
the length of the route of the Kth vehicle between the stations i and r;
step S43: and (3) obtaining an initial solution of the path by inserting the destination into the path set by adopting a heuristic insertion algorithm, and then optimizing the initial solution by adopting a genetic algorithm.
Step S44: and marking the obtained route and each stop on a map to obtain a final customized bus route optimization scheme.
Compared with the prior art, the method for determining the station and optimizing the route of the night customized bus has the following beneficial effects:
1) According to the invention, the number of stations is determined by effectively utilizing the demand data, so that the number of stations is reduced to the greatest extent on the basis of meeting the requirement of convenience in passenger trip, waste is avoided, and the construction cost is saved;
2) The method is based on two clustering algorithms to analyze special multidimensional data consisting of the scattered point position of the destination and the passenger drop volume, effectively distinguishes density difference of demand distribution, is reasonable and simple and convenient to calculate, and ensures the scientificity and accuracy of follow-up work.
3) The invention adopts DBSCAN algorithm and improved K-means algorithm to carry out customized bus stop clustering; the DBCSCAN algorithm is not affected by scatter shapes and can automatically classify data sets, but does not find centroid points. The K-means algorithm can solve the clustering center, but is greatly influenced by the selection of the initial K value and the initial centroid point. Based on the advantages and the disadvantages of the two algorithms, the invention firstly utilizes the DBCSCAN algorithm to cluster the scattered points of the known destination and divide the scattered points into the discrete points and the clusters, thereby ensuring the accessibility of the discrete points, simultaneously providing reasonable K value and selection of initial centroid points for the next solution of the K-means algorithm clustering center, and greatly improving the accuracy and the rationality of the clustering effect.
4) The method is different from the judgment logic of the distance formula in the traditional K-means algorithm, and modifies the judgment logic into consideration of the influence of the passenger number weight on the total walking distance, so that the sum of all walking distances of passengers at night is minimum and is used as the iteration premise of solving the clustering center, and the time value of the passengers is guaranteed.
5) The model objective function ensures that the sum of travel time cost of passengers is minimum, so that the planning result is influenced by the weight of the number of passengers getting off at different stations. The time value cost of passengers plays a leading role, and the path selection is changed from the shortest path selection scheme to a selection scheme considering the weight of the number of passengers.
6) The invention can select the station based on the starting and ending point positions of the passengers, simultaneously ensures the accessibility of each discrete point, and gives consideration to the time value cost and the bus operation cost of the passengers during traveling, so that the walking distance of the passengers is shortened, and the bus route is optimized. The system has important reference significance for solving the problem of passenger evacuation in railway stations, airports and the like which are lack of matched traffic service systems and have large-batch passenger arrival at night.
It will be apparent to those skilled in the art that various changes and modifications can be made in the embodiments of the invention without departing from the inventive concept of the present application, and these embodiments are intended to be covered by the claims of the present application.
Claims (8)
1. A method for determining stations and optimizing routes of buses customized at night is characterized by comprising the following steps:
step S1: determining destinations where passengers leave the station in the hub, marking scattered positions of all the destinations on a map, and recording and counting corresponding positions and the number of passengers falling from each destination;
step S2: considering the requirements of special passengers on door-to-door service and the relative positions of destinations, adopting a DBSCAN algorithm which is not influenced by the distribution shape of clustering points as a basic algorithm, carrying out clustering analysis on destination discrete points, dividing the destination discrete points into a plurality of discrete points and clusters, and setting the discrete points as independent stations;
and step S3: determining a parameter K value, namely the positions of K clusters and an initial centroid point, according to each cluster class obtained by a DBSCAN algorithm by using a K-means algorithm as a basis, modifying judgment logic of distance clustering in the algorithm, further obtaining a clustering center of each cluster class, and determining the position of each cluster class website;
and step S4: marking all the set site positions, and finely adjusting unreasonable site positions according to actual conditions; after adjustment is finished, based on the established station positions, a heuristic insertion algorithm is adopted, an initial solution of the paths is obtained in a mode of inserting the destinations into the path set, then a genetic algorithm is adopted to optimize the initial solution, and the obtained lines and stations are marked on a map to obtain a final night customized bus line optimization scheme.
2. The method for determining stops and optimizing routes for customized night buses of claim 1,
the specific process of the step S1 is as follows:
step S11: marking scattered positions of all destinations on a map by investigating the positions of all passenger destinations in a hub;
step S12: and recording and counting the positions of all destinations and the number of passengers falling, and sorting the positions and the number of the passengers falling into the destinations to be used as a data set.
3. The method for station determination and route optimization for the customized buses at night according to any one of claims 1-2, wherein the specific process of the step S2 is as follows:
step S21: dividing destination scattered points into a plurality of discrete points and each cluster type by taking the maximum acceptable walking distance of passengers after getting off the bus as a neighborhood radius parameter epsilon in the DBSCAN algorithm and taking the minimum clustering passenger number as a density threshold parameter Minpts in the DBSCAN algorithm;
step S22: each discrete point is set as a separate site.
4. The method for determining stations and optimizing routes for customized buses at night according to claim 3, wherein the specific process of the step S3 is as follows:
step S31: determining the value of a parameter K in a K-means algorithm according to each cluster type obtained by the DBSCAN algorithm, namely K clusters; then, calculating the average value of the position coordinates of each cluster station as the positions of K initial centroid points of a K-means algorithm;
step S32: the judgment logic for changing the distance clustering in the K-means algorithm is to consider the influence of the passenger number weight on the total walking distance so as to minimize the sum D of the walking distances of all the passengers at night, and is represented as:
in the formula, a h The distance from each point in the cluster to the cluster center; n is h The number of passengers at each point in the cluster is clustered; h is the number of cluster destinations; x is the number of h ,x z Respectively, the abscissa, y, of each point and cluster center in the cluster h ,y z Respectively are the vertical coordinates of each point in the cluster and the cluster center;
step S33: and combining the cluster center of each cluster as a station of each cluster with the discrete point station in the step S22 into a set to be used as a final station set.
5. The method for station determination and route optimization for customized buses at night according to claim 4, wherein the specific process of the step S4 is as follows:
step S41: aiming at minimizing the sum of the customized bus operation cost and the passenger trip cost, establishing a line dynamic planning model:
customized bus operation cost C s ：
C s ＝C b N+C f L b (4-1)
Cost per unit time value V of the passenger:
total passenger time value cost C due to custom bus detour p ：
C p ＝V×ΔT (4-3)
An objective function:
in the formula, C b Is an inherent cost; c f Fuel cost per mileage; l is b The running mileage of the vehicle is taken; n is a carThe number of vehicles; z is the annual income of passengers;
the system is used for calculating the reward obtained by using the riding time for overtime work; t is the average value of the effective working time in one year; the delta T is the bypass delay time of the customized bus;
the path length of the k-th vehicle between i and j is obtained; delta T j The delay time for the vehicle to reach the station j; w j The total number of people getting off at the station j; k is a vehicle number, K = {1,2, \8230;, K }; i, j is the vehicle stop station number, I, j = {1,2, \8230;, I }; k and I are respectively the maximum value of the number of vehicles and the maximum value of the number of stations;
is a decision variable;
step S42: establishing constraint conditions according to four aspects of departure time constraint, arrival time constraint, passenger capacity constraint and detour distance constraint of the vehicle:
customizing bus departure time T d ：
T α +θ≤T d ≤T α +T m (4-5)
Customizing bus arrival time T bi ：
T ti +θ≤T bi ≤T ti +ΔT (4-6)
And (3) passenger capacity constraint:
and (3) restricting the detour distance:
in the formula (I), the compound is shown in the specification,theta is the average time required for the passenger to walk to reach the custom bus departure point; t is a unit of m The maximum waiting time that can be tolerated by the passenger; t is a unit of α Train arrival time; t is ti The time required for the taxi to directly reach the station i comprises the time for the passenger to wait for the taxi;
the total number of passengers of the kth vehicle;
the maximum passenger capacity of the kth vehicle; omega is a nonlinear coefficient;
the length of the route of the Kth vehicle between the stations i, j is obtained;
the length of the route of the Kth vehicle between the stations r and j is set;
the length of the route of the Kth vehicle between the stations i and r;
step S43: adopting a heuristic insertion algorithm, obtaining an initial solution of the path by inserting the destination into the path set, and then optimizing the initial solution by adopting a genetic algorithm;
step S44: marking the obtained route and each stop on a map to obtain a final customized bus route optimization scheme.
6. A stop determination and route optimization system for night customized buses is characterized by comprising:
a data set sorting module: determining destinations where passengers leave the station in the hub, marking scattered positions of all the destinations on a map, recording and counting corresponding positions of the destinations and the number of passengers falling from each destination, and arranging the positions to be used as a data set;
a cluster analysis module: considering the requirements of special passengers on door-to-door service and the relative positions of destinations, adopting a DBSCAN algorithm which is not influenced by the distribution shape of clustering points as a basic algorithm, carrying out clustering analysis on the destination discrete points, dividing the destination discrete points into a plurality of discrete points and various clusters, and setting the discrete points as independent stations;
a site determination module: determining a parameter K value, namely the positions of K clusters and an initial centroid point, according to each cluster class obtained by a DBSCAN algorithm by using a K-means algorithm as a basis, modifying judgment logic of distance clustering in the algorithm, further obtaining a clustering center of each cluster class, and determining the position of each cluster class station;
a line optimization module: marking all the set site positions, and finely adjusting unreasonable site positions according to actual conditions; after adjustment is finished, based on the established station positions, a heuristic insertion algorithm is adopted, an initial solution of the paths is obtained in a mode of inserting the destinations into the path set, then a genetic algorithm is adopted to optimize the initial solution, and the obtained lines and stations are marked on a map to obtain a final night customized bus line optimization scheme.
7. A stop determining and route optimizing system terminal for night customized buses, comprising a memory, a processor and at least one instruction or at least one computer program, wherein the at least one instruction or the at least one computer program is stored in the memory and can be loaded and run on the processor, and the system terminal is characterized in that the processor loads and runs the at least one instruction or the at least one computer program to realize the stop determining and route optimizing method for the night customized buses according to any one of claims 1 to 5.
8. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a method for determining stations and optimizing routes for night time bus customizations according to any one of claims 1 to 5.
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