CN113469451A

CN113469451A - Customized bus route generation method based on heuristic algorithm

Info

Publication number: CN113469451A
Application number: CN202110812117.XA
Authority: CN
Inventors: 孙威峰; 王瑞利
Original assignee: Hangzhou Shuzhimeng Technology Co ltd
Current assignee: Hangzhou Shuzhimeng Technology Co ltd
Priority date: 2021-07-19
Filing date: 2021-07-19
Publication date: 2021-10-01
Anticipated expiration: 2041-07-19
Also published as: CN113469451B

Abstract

The invention relates to a customized bus route generation method based on heuristic algorithm, which solves the problems of the prior art and has the technical scheme that: the method comprises the following steps of firstly, reducing the calculation amount by means of OD polymerization; sampling a set number of missed ODs and generating a corresponding trip scheme to complete initialization of a new line; step three, taking out the current missed OD and disturbing the traversal sequence, trying to splice all nearby new lines for each OD to obtain an optimal insertion mode, inserting the OD into the new line with the minimum cost, and after traversing all the ODs, finishing the generation of the new line; step four: line elimination and line optimization are carried out according to OD selection, and the line elimination and the line optimization are carried out in a combined mode in the process; and step five, judging whether the current line set meets the requirements, if not, repeatedly executing the step two to the step four, and obtaining the final line set through iteration.

Description

Customized bus route generation method based on heuristic algorithm

Technical Field

The invention relates to a customized bus route generation method, in particular to a customized bus route generation method based on a heuristic algorithm.

Background

The customized bus route generation method belongs to the class of Vehicle Routing Problems (VRP), but the restriction and implementation are more complicated. The problem is similar to drop car sharing and take-out delivery, and reference is made to the description of the problem in the mei-gang intelligent delivery system: "the path planning problem specific to the rider is not a simple route planning, not the question of which path to walk from a to b. The scene is that a rider has a plurality of delivery tasks, and the delivery tasks have various constraints on how to select the optimal delivery sequence to complete all the tasks. This is an NP-hard problem, with there being 11 million possible sequences when there are 5 orders, 10 task points. During the peak period, the rider usually bears more than 5 sheets, even sometimes one rider receives more than ten sheets at the same time, and the feasible taking and sending sequence becomes an astronomical number. The difference is that the drop car can only accommodate 3-4 people due to vehicle limitation; the split order of take-out delivery is even in extreme cases a dozen or so orders. But also allows for the direct delivery of a single order in a piecemeal situation, where the capacity of the bus is sufficient to be tens or hundreds of people, and the mei-qu assessment 10 single solution space 2.38 x 10 x 15 is an astronomical figure, which is only the minimum size of the bus piecemeal and, unlike drop/mei order starting and ending points, the bus piecemeal also needs to match the appropriate boarding and disembarking bus stops for the user.

At present, the methods for solving the vehicle path problem are very numerous and can be basically divided into 2 categories, namely an accurate algorithm and a heuristic algorithm. The precise algorithm is an algorithm capable of solving the optimal solution of the logistics system, and mathematical programming technologies such as linear programming, integer programming and nonlinear programming are mainly used for describing the quantitative relation of the logistics system so as to solve the optimal decision. However, because a strict mathematical method is introduced, the calculation amount generally increases exponentially with the increase of the problem scale, so that the problem of exponential explosion cannot be avoided, and the algorithms can only effectively solve medium and small-scale deterministic VRPs. Since the vehicle path optimization problem is an NP problem, and efficient and accurate algorithms are unlikely to exist (unless P = NP), finding an approximate algorithm is necessary and realistic, and for this reason experts mainly spend effort on constructing high quality heuristic algorithms. The heuristic algorithm is an improved search algorithm in the state space that evaluates each searched position to get the best position from which to search to the target. In heuristic search, the valuation of the location is important, and different valuations can have different effects. The existing method is directly used for solving, if the exploration is fully inspired, the speed is too slow, and because the exploration frequency of a solution space is too large, the dependence on navigation is too heavy (the navigation speed of a vehicle between two points is slow), if the exploration is not fully inspired, the line quality is very poor, so that the practical applicability is very poor. In summary, the disadvantages of the prior art are: the line generation speed is slow, the line quality has problems, the line has problems such as turning around, the satisfaction degree of the user is not considered much, and the actual riding will probably be low.

Disclosure of Invention

Aiming at the problems of low line generation speed, line quality, turning around and the like, low consideration on the satisfaction degree of users and possibly low actual riding willingness in the background technology, the invention provides a customized bus line generation method based on a heuristic algorithm.

The technical scheme adopted by the invention for solving the technical problems is as follows: a method for generating customized bus routes based on heuristic algorithm comprises the following steps after obtaining city OD data,

step one, reducing the calculated amount in an OD polymerization mode;

sampling a set number of missed ODs and generating a corresponding trip scheme to complete initialization of a new line;

step three, taking out the ODs which are not connected with each other at present, disordering the traversal sequence, trying to splice all the nearby new lines for each OD to obtain the optimal insertion mode, inserting the OD into the new line with the minimum cost, and after traversing all the ODs, finishing the generation of the new line;

step four: line elimination and line optimization are carried out according to OD selection, and the line elimination and the line optimization are carried out in a combined mode in the process;

and step five, judging whether the current line set meets the requirements, if not, repeatedly executing the step two to the step four, and obtaining the final line set through iteration.

In the invention, "O" is from English ORIGIN, which refers to a starting place of a trip, and "D" is from English DESTINATION, which refers to a DESTINATION of the trip, and the urban OD data belongs to a conventional calling method in the technical field, which refers to clear, and can be obtained by the prior art. And further optimizing the initial line result to improve the line quality. The satisfaction degree of the users is quantized, the number of the passengers in the line is directly influenced, and the line result gives consideration to passenger experience and bus operation. By utilizing the technical scheme provided by the invention, customized public transportation service can be provided based on travel demands submitted by scenes such as enterprises, schools, airports, high-speed rail stations and the like. Based on city OD data, the potential demand is identified, the large-flow trip difficulty is realized, and the customized bus trip demand is extracted.

Preferably, in the first step, the OD polymerization is carried out in two steps,

the method comprises a polymerization step one, wherein the same OD is combined in a Geohash combination demand mode, O and D are converted into Geohash, the Geohash at the starting point and the ending point are combined in the same demand mode, the OD with higher frequency is combined and compressed into a weighted OD, and the subsequent calculated amount is reduced;

and step two, calculating all walk reachable sites with the right OD, counting the frequency of the sites, and obtaining a site set based on a greedy point selection mode.

Preferably, in the second aggregation step, the greedy point selection mode includes a greedy point selection step one, wherein a station with the highest frequency is selected, if the frequency of the station is the same, the station with the small walking total distance is selected, and the corresponding frequency is subtracted from other stations which are corresponding to the finally selected station and can reach the original OD; and a greedy point selection step II, repeating the greedy point selection step I until the frequency of the rest sites is 0.

Preferably, in the second aggregation step, after the initial site set is set, an extended site set is required, and all sites and homologous sites around each site in the initial site set are supplemented to the initial site set to form the site set used in the second step.

Preferably, in the second step, a set number of ODs are sampled and acquired based on the weighted values of the weighted ODs, reachable stations near O and D are randomly selected, and a new line generated initially includes only two stations.

Preferably, in the third step, the insertion cost is an increased cost of the line, wherein the time-consuming cost is converted into a fee through a driver salary, and the mileage cost is converted into a fee through a vehicle fuel consumption.

Preferably, in the fourth step, the line elimination sub-step includes: combining the new routes into all the generated public transportation routes, matching all OD requirements with all the public transportation routes, selecting the public transportation route with the minimum total time consumption by each OD, and performing weight conversion according to OD satisfaction; and counting the number of people, the consumed time, the mileage, the cost, the income and the benefit index of the line, and comprehensively screening the line based on the multiple dimensions.

Preferably, the step of performing weight conversion on the OD satisfaction degree comprises calculating the satisfaction degree of time consumption of customized bus trip based on the time consumption of taxi trip and the time consumption of bus subway trip of the OD.

Preferably, in the fourth step, the line optimization substep includes deleting stations without people getting on or off the train, deleting stations with low profit, sequentially adjusting each in-line station based on a single-point movement and 2-opt method, and adjusting inter-line stations based on a 2-opt and cross-exchange method.

The substantial effects of the invention are as follows: the customized bus route design model is constructed and the route scheme is output on the basis of considering both the travel cost of passengers and the customized bus operation cost, so that reference is provided for the actual planning and operation of the customized bus. Compared with the research of the existing customized bus route design problem, the time and space requirements of passengers are considered in the discovery, a heuristic algorithm is designed, the coverage rate of the customized bus requirements is high, the average attendance rate is high, the satisfaction degree of the passengers can be effectively improved, and the operation cost of the customized bus is reduced.

Drawings

FIG. 1 is a schematic flow chart of the present invention;

FIG. 2 is a schematic diagram of a circuit generation according to the present invention;

fig. 3 is a schematic diagram of a circuit generation according to the present invention.

Detailed Description

The technical solution of the present invention will be further specifically described below by way of specific examples.

Example 1:

a customized bus route generation method based on heuristic algorithm (see attached figure 1) comprises the following steps after obtaining city OD data,

step one, reducing the calculated amount in an OD polymerization mode;

In the first step, the OD polymerization is divided into two steps,

and step two, calculating all walk reachable sites with the right OD, counting the frequency of the sites, and obtaining a site set based on a greedy point selection mode. In the aggregation step II, the greedy point selection mode comprises a greedy point selection step I, wherein a station with the highest frequency is selected, if the frequency of the station is the same, the station with the small walking total distance is selected, and the corresponding frequency is subtracted from other stations which correspond to the finally selected station and can reach the original OD; and a greedy point selection step II, repeating the greedy point selection step I until the frequency of the rest sites is 0. In the second aggregation step, after the initial site set is set, the site set needs to be expanded, and all sites and sites with the same name adjacent to each site in the initial site set are supplemented to the initial site set to form the site set used in the second step. The weighted OD in the embodiment refers to the OD with the weighted weight value, generally refers to the number of people, and can also be set manually; the station frequency mainly refers to an accumulated value of the authorized OD, and can be generally understood as the number of times that a person goes out of and enters the station position in a certain set time period; the total walking distance refers to the walking distance between the stop point and the real destination or starting point.

More specifically, firstly, the same OD is merged, O and D are converted into Geohash7 through the merging requirement of the Geohash, and the Geohash at the starting point and the ending point are merged with the same requirement. Higher frequency ODs can be combined and compressed into one weighted (number of people) OD, reducing the amount of subsequent calculations. Then, screening an initial station set, greatly reducing a search space based on frequent greedy station selection, calculating all walk-reachable stations with rights OD, specifically, getting-on stations and getting-off stations, setting the distance to be within 800 meters, counting the station frequency, and obtaining the station set based on a greedy station selection mode. Greedy point selection: and taking the station with the highest frequency, taking the station with smaller walking total distance when the frequency of the station is the same, and subtracting the corresponding frequency from other stations which can be reached by the original OD corresponding to the station. The above process is repeated until the remaining sites are all 0 in frequency.

When the route is actually generated, the above-mentioned station set has a problem of a large number of turns, because only one station on one side of the route is included, and the other side of the route is not opposite to the traveling station, so that the station set needs to be expanded. Expanding the site set: all stations in the neighborhood around each station. According to the general case, stations within a straight distance of 200 meters are set, as well as stations of the same name, because the stations of the same name are generally opposite. The station set obtained by the greedy point selection mode is small, the solution space can be greatly reduced, the line generation is greatly accelerated, and the problem of large-scale turning is avoided by combining the station set expansion method.

In the second step, a set number of ODs are sampled and obtained based on the weighted values of the weighted ODs, reachable stations near O and D are randomly selected, and the generated new line is a line including only two stations in an initial state.

More specifically, a small number of weighted ODs are sampled based on the weighted values of the weighted ODs, the number of the weighted ODs can be set to 10, reachable sites near O and D are randomly selected, the selected reachable sites both belong to a site set, and the initial state of the new line is a line including only two sites. In the sampling process, other factors can be comprehensively considered besides the number of OD weight numbers, for example, the line index finally obtained according to some OD in the previous iteration is higher; in the early iteration, the selected probability of not having a suitable line OD is higher than the connected OD.

In the third step, the current ODs which are not connected with each other are taken out, the traversal sequence is disturbed, for each OD, all the new lines nearby are tried to be spliced, the optimal insertion mode is obtained, the insertion cost is the cost of the lines, the time-consuming cost is converted into the cost through the driver pay, the mileage cost is converted into the cost through the automobile oil consumption, the OD is inserted into the new line with the minimum cost, and after all the ODs are traversed, the new line is generated.

In the fourth step, the sub-step of line elimination comprises: combining the new routes into all the generated public transportation routes, matching all OD requirements with all the public transportation routes, selecting the public transportation route with the minimum total time consumption by each OD, and performing weight conversion according to OD satisfaction; and counting the number of people, the consumed time, the mileage, the cost, the income and the benefit index of the line, and comprehensively screening the line based on the multiple dimensions. The step of performing weight conversion on the OD satisfaction degree comprises calculating the satisfaction degree of time consumption of bus trip through customization based on the time consumption of the OD taxi trip and the time consumption of the bus subway trip. In the fourth step, the line optimization substep comprises deleting stations without people getting on or off the train, deleting stations with low benefit, sequentially adjusting the stations in each line based on a single-point movement and 2-opt method, and adjusting the stations between lines based on a 2-opt and cross-exchange method. In this embodiment, the number of passengers is converted based on the satisfaction, for example, the number of passengers is 2 OD, the satisfaction is 80%, and the number of passengers is actually 1.6. The revenue, i.e., the cumulative effective transport distance, described in this embodiment: the OD distance accumulation of all people on the line and the benefit index, namely the income/cost, are better when the numerical value is higher, and the minimum total consumed time selected by each OD means that the total consumed time of walking and the total consumed time of buses is the minimum. The final route set in this embodiment may include information such as a station where the route passes, a route related index, arrival time of the vehicle at each station, and arrival and departure times of each user. And step five, judging whether the current line set meets the requirements, if not, repeatedly executing the step two to the step four, and obtaining a final line set through iteration, wherein the attached drawing 2 is a schematic diagram from multiple points to a single point, and is multi-O single D or single-O multi D, and the attached drawing 3 is a schematic diagram for multi-O multi D in the method. And in the fifth step, judging whether the current route set meets the requirements or not, wherein the judgment mainly comprises judging whether the total travel time of the target service population is reduced or not, and whether the benefit index of the vehicle reaches the standard or not, wherein the benefit index is related to the number of people, time consumption, mileage, cost and income of the statistical route.

The customized bus route design model is constructed and the route scheme is output on the basis of considering both the travel cost of passengers and the operation cost of the customized buses, and reference is provided for actual planning and operation of the customized buses. Compared with the research of the existing customized bus route design problem, the time and space requirements of passengers are considered in the discovery, a heuristic algorithm is designed, the coverage rate of the customized bus requirements is high, the average attendance rate is high, the satisfaction degree of the passengers can be effectively improved, and the operation cost of the customized bus is reduced.

The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.

Claims

1. A method for generating customized bus routes based on heuristic algorithm is characterized in that after urban OD data is acquired, the following steps are carried out,

step one, reducing the calculated amount in an OD polymerization mode;

step two, sampling a set number of non-interconnected ODs and generating a corresponding trip scheme to complete initialization of a new line;

step three, taking out the current missed OD and disturbing the traversal sequence, trying to splice all nearby new lines for each OD to obtain an optimal insertion mode, inserting the OD into the new line with the minimum cost, and after traversing all the ODs, finishing the generation of the new line;

2. The generation method of the customized bus route based on the heuristic algorithm of claim 1, wherein in the first step, the OD polymerization is divided into two steps,

the method comprises a polymerization step one, wherein the same OD is combined in a Geohash combination demand mode, O and D are converted into Geohash values, the Geohash values at the starting point and the end point are combined, the OD with higher frequency is combined and compressed into a weighted OD, and the subsequent calculated amount is reduced;

3. The method for generating the customized bus route based on the heuristic algorithm as claimed in claim 2, wherein in the aggregating step two, the greedy point selection mode comprises a greedy point selection step one, wherein a station with the highest frequency is selected, if the frequency of the station is the same, the station with the small walking total distance is selected, and the corresponding frequency is subtracted from other stations corresponding to the finally selected station and having the reachable original OD; and a greedy point selection step II, repeating the greedy point selection step I until the frequency of the rest sites is 0.

4. The method as claimed in claim 3, wherein in the second aggregation step, after the initial station set is set, an extended station set is required, and all stations and corresponding stations around each station in the initial station set are supplemented to the initial station set to form the station set used in the second step.

5. The method as claimed in claim 4, wherein in the second step, a set number of weighted ODs are sampled and obtained based on their weights, and reachable stations near O and D are randomly selected, and the new route generated is a route that only includes two stations in its initial state.

6. The method as claimed in claim 5, wherein in the third step, the insertion cost is an added cost of the line, wherein the time-consuming cost is converted into a fee through a driver salary, and the mileage cost is converted into a fee through a vehicle fuel consumption.

7. A customized bus route generation method based on a heuristic algorithm as in claim 5 or 6, characterized in that in step four, the sub-step of route elimination comprises: combining the new routes into all the generated public transportation routes, matching all OD requirements with all the public transportation routes, selecting the public transportation route with the minimum total time consumption by each OD, and performing weight conversion according to OD satisfaction; and counting the number of people, the consumed time, the mileage, the cost, the income and the benefit index of the line, and comprehensively screening the line based on the multiple dimensions.

8. The customized bus route generation method based on the heuristic algorithm of claim 7, wherein the step of performing weight conversion on the OD satisfaction degree comprises calculating the satisfaction degree of time consumption of customized bus trip based on the time consumption of taxi trip and the time consumption of bus subway trip of the OD.

9. The generation method of the customized bus route based on the heuristic algorithm as claimed in claim 5 or 6, wherein in the fourth step, the route optimization substep comprises deleting stations without people getting on or off the bus, deleting stations with low profit, adjusting the sequence of the stations in each route based on a single-point movement and 2-opt method, and adjusting the stations between routes based on a 2-opt and cross-exchange method.