CN108764538B - Bus network optimization design method suitable for changes of unobvious demands - Google Patents

Bus network optimization design method suitable for changes of unobvious demands Download PDF

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CN108764538B
CN108764538B CN201810461114.4A CN201810461114A CN108764538B CN 108764538 B CN108764538 B CN 108764538B CN 201810461114 A CN201810461114 A CN 201810461114A CN 108764538 B CN108764538 B CN 108764538B
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任刚
张涛
杨阳
徐磊
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Abstract

The invention discloses a bus network optimization design method specially suitable for changes of unobvious demands, which is characterized in that an effective bus line network optimization model aiming at the total cost of drivers who go to the bus in a minimized and direct way and drivers who do not meet the demands is established by improving the existing genetic algorithm, so that a more economic, effective, reasonable and convenient bus network design method is provided under the changes of the unobvious demands, the traditional streamline unified bus network design method is broken through, the flexibility is higher, the operability and sustainability are higher, and the optimization quality and the search efficiency are greatly improved.

Description

Bus network optimization design method suitable for changes of unobvious demands
Field of the invention
The invention relates to the field of traffic engineering, provides a design method aiming at the layout planning of a public traffic network, and particularly relates to an optimal design method of the public traffic network, which is suitable for the condition of unobvious demand change.
Background
With the continuous promotion of the urbanization process in China, urban public transport becomes a very important aspect of urban construction. The public transportation can not only ensure normal traffic travel of urban residents, but also become an important means for improving the utilization rate of traffic resources, relieving traffic jam, reducing traffic pollution and saving land resources and energy. The public transport is one of the transportation modes with the widest transportation range and the highest audience and utilization rate.
Public transport net is generally with resident's trip demand as the direction, because their trip demand is constantly changing, so public transport net scheme needs frequent design and update. When the demand of the public transport is not obviously changed and the original public transport network scheme needs to be redesigned, the traffic planner and the manager usually do not want the configuration of the public transport network to be changed too much. However, if the traditional public transportation network design model is continuously used, a new scheme with greatly changed line configuration can be obtained, which is contrary to the actual situation, and meanwhile, the implementation and the arrangement of the new scheme also generate unnecessary manpower and time waste.
Therefore, by combining the actual development of the current traffic engineering field and the characteristics of complexity and uncertainty of the design of the existing public traffic network, an operable, economical and effective public traffic network is designed based on the current situation of urban traffic in China, is particularly suitable for the public traffic network with unobvious demand change, and has great significance for the development of urban political economy, cultural education, scientific technology and the like.
Disclosure of Invention
The invention provides a bus network optimization design method specially suitable for the change of unobvious demands aiming at the problem that the prior art has little attention on the bus network design method suitable for the change of the unobvious demands, and establishes a bus line network optimization effective model aiming at the total cost of the passengers and the unsatisfied demands which are the minimum direct travelers by improving the prior genetic algorithm, thereby providing a more economic, effective, reasonable and convenient bus network design method under the condition of the unobvious demand change.
In order to achieve the purpose, the invention adopts the technical scheme that: a bus network optimization design method suitable for the condition of unobvious demand change comprises the following steps:
step 1: establishing an objective function and setting a limiting condition;
step 2: selecting an original bus network scheme as an initial scheme, and calculating an adaptive value of the original bus network scheme by using a network analysis program;
and step 3: setting the initial scheme as a candidate optimal scheme;
and 4, step 4: performing a propagation process including a selection process and a mutation process on the candidate optimal solution in the step 3,
step 41: the selection process comprises the following steps: and selecting the lines to be mutated in all schemes according to the probability.
Step 42: performing intermediate single-site mutation process according to probability
Step 421: determining two adjacent stops of the intermediate stop on the bus line and a direct connection stop of the intermediate stop not on the line, checking whether the direct connection stop not on the line and the two adjacent stops on the bus line are directly connected or not, if so, turning to step 422, wherein the connectable stop is a stop to which the intermediate stop can be suddenly changed; if the "directly connected station not on the line" of all the intermediate stations is not directly connected to the "two adjacent stations on the bus line", go to step 43;
step 422, the following steps: taking the intermediate sites determined in the step 421 and the sites that can be mutated into the intermediate sites as candidate sites, and determining the sites to be mutated finally according to the sum of the upstream and downstream demands of the candidate sites, wherein the upstream demand refers to the number of trips from all the sites in front of the intermediate sites to the candidate sites, and the downstream demand refers to the number of trips from the candidate sites to all the sites behind the intermediate sites;
step 43: carrying out the mutation process of the initial site according to the probability;
step 431: when the starting station and a certain station can be directly connected, the starting station can be changed into a second station, and the original second station can be changed into the starting station;
step 432: if the initial site is not directly connected with any site and no site capable of being mutated exists, the step 5 is carried out; then, performing the mutation process of the initial station as step 431 on the terminal station according to the probability;
and 5: calculating an adaptive value of the new scheme obtained after the steps according to a network analysis program, comparing the adaptive value with the adaptive value of the candidate optimal scheme in the step 2, and selecting a small candidate optimal scheme as the new candidate optimal scheme, wherein only one final scheme is selected;
step 6: repeating the step 4-5, if the repetition times reach the preset times, stopping iteration, and selecting a candidate optimal scheme as an 'optimal scheme'; and if the reproduction times do not reach the preset times, returning to the step 4.
As a modification of the present invention, the probability in step 4 is set according to the size of the network scale and the design time requirement.
As another improvement of the present invention, the objective function in step 1 is shown by the following formula:
Figure DEST_PATH_IMAGE002
the constraints in step 1 are as follows:
Figure DEST_PATH_IMAGE004
Figure DEST_PATH_IMAGE006
in the formula: v is the set of all sites, i denotes site i, j denotes site j, dijRepresents the travel demand, L, from site i to site jmaxIndicating the maximum length of the bus route, LminIndicating the minimum length, Q, of the bus routemaxRepresenting the maximum capacity, T, of each vehicledRepresenting the time cost of each person who does not meet the public transportation demand, n representing the nth line of a scheme, tr representing the transfer path when travelling more than two paths, and LnThe total length of the line n is indicated,
Figure DEST_PATH_IMAGE008
indicating that the bus demand from station i to station j is satisfied on path n,
Figure DEST_PATH_IMAGE010
indicating that the bus demand from station i to station j is satisfied on the transfer path tr, DRijRepresenting the set of direct lines serving from site i to site j, TRijRepresenting the set of transfer lines servicing from site i to site j,
Figure DEST_PATH_IMAGE012
representing the travel time from station i to station j on path n,
Figure DEST_PATH_IMAGE014
representing the travel time from station i to station j on transfer path tr,
Figure DEST_PATH_IMAGE016
denotes the maximum flow on path n, C1、C2、C3Weight influence coefficients (C) representing the direct person cost, the transfer person cost and the cost for satisfying the demander respectively1+C2+C3＝1)。
As an improvement of the present invention, the network analysis program in step 2 includes:
step 21: eliminating the direct bearing flow of one path from the original demand matrix, distributing the flow serving as direct demands to the path, and obtaining a direct update demand matrix after the direct bearing flow of all paths is eliminated;
step 22: and (3) eliminating the transfer bearer flow of one transfer path from the direct update demand matrix in the step (21), distributing the flow serving as the transfer demand to the corresponding path, and obtaining a transfer update demand matrix updated by the transfer demand after all the transfer bearer flows are eliminated.
Step 23: and (3) outputting all parameters and variables required in the objective function in the step (1), and calculating an adaptive value (Z in the objective function) of the scheme.
As a further improvement of the present invention, in the step 6, the predetermined number is determined according to the running time, and when the running time is limited, the predetermined number is guaranteed to be within the limit time, and the running of the program is ended; when there is no runtime restriction, the predetermined number of times is decided according to the feasible solution space, the larger the predetermined number of times.
Compared with the prior art, the invention provides the bus network optimization design method specially suitable for the bus network with unobvious demand change, improves the selection and mutation process in the traditional genetic algorithm, deletes the crossing process, ensures that the bus network optimization design method has better optimization quality and search efficiency, can meet the redesign of the bus network scheme under the condition of unobvious demand change, is economic and effective, greatly saves the manpower, material resources and economic cost generated by the implementation and arrangement of a new scheme, is reasonable in resource configuration, breaks through the traditional assembly line type unified bus network design method, and is more flexible and flexible, and stronger in operability and sustainability.
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FIG. 1 is a schematic diagram of a bus network optimization design without significant changes;
fig. 2 is a schematic diagram of a case network according to embodiment 1 of the present invention;
FIG. 3 is a graph showing the results of comparative tests conducted in example 2 of the present invention.
Detailed Description
The invention will be explained in more detail below with reference to the drawings and examples.
Example 1
A bus network optimization design method suitable for the condition of unobvious demand change is disclosed, as shown in figure 1, and comprises the following steps:
step 1: an objective function is established and a constraint is set,
the objective function is shown by the following equation:
Figure DEST_PATH_IMAGE018
the limiting conditions are as follows:
Figure DEST_PATH_IMAGE020
Figure DEST_PATH_IMAGE022
in the formula: v is the set of all sites, i denotes site i, j denotes site j, dijRepresents the travel demand, L, from site i to site jmaxIndicating the maximum length of the bus route, LminIndicating the minimum length, Q, of the bus routemaxRepresenting the maximum capacity, T, of each vehicledThe time cost of each person who does not meet the public transportation demand is shown, n represents the nth line of a scheme, tr represents a transfer route when more than two routes are used for going outDiameter, LnThe total length of the line n is indicated,
Figure DEST_PATH_IMAGE024
indicating that the bus demand from station i to station j is satisfied on path n,
Figure DEST_PATH_IMAGE026
indicating that the bus demand from station i to station j is satisfied on the transfer path tr, DRijRepresenting the set of direct lines serving from site i to site j, TRijRepresenting the set of transfer lines servicing from site i to site j,
Figure DEST_PATH_IMAGE028
representing the travel time from station i to station j on path n,
Figure DEST_PATH_IMAGE030
representing the travel time from station i to station j on transfer path tr,
Figure DEST_PATH_IMAGE032
denotes the maximum flow on path n, C1、C2、C3Weight influence coefficients (C) representing the direct person cost, the transfer person cost and the cost for satisfying the demander respectively1+C2+C3＝1)。
Step 2: selecting an original bus network scheme as an initial scheme, and calculating an adaptive value of the original bus network scheme by using a network analysis program, wherein the network analysis program comprises the following steps:
step 21: eliminating the direct bearing flow of one path from the original demand matrix, distributing the flow serving as direct demands to the path, and obtaining a direct update demand matrix after the direct bearing flow of all paths is eliminated;
step 22: removing the transfer bearer flow of one transfer path from the direct update demand matrix in the step 21, distributing the flow serving as a transfer demand to a corresponding path, and obtaining a transfer update demand matrix updated by the transfer demand after all the transfer bearer flows are removed;
step 23: and (3) outputting all parameters and variables required in the objective function in the step (1), and calculating an adaptive value (Z in the objective function) of the scheme.
And step 3: and setting the initial scheme as a candidate optimal scheme.
And 4, step 4: performing a propagation process including a selection process and a mutation process on the candidate optimal solution in the step 3,
step 41: the selection process comprises the following steps: and setting probability according to the network scale and the design time, and selecting the lines to be mutated in all schemes according to the probability.
Step 42: performing intermediate single-site mutation process according to probability
Step 421: determining two adjacent stops of the intermediate stop on the bus line and a direct connection stop of the intermediate stop not on the line, checking whether the direct connection stop not on the line and the two adjacent stops on the bus line are directly connected or not, if so, turning to step 422, wherein the connectable stop is a stop to which the intermediate stop can be suddenly changed; if the "directly connected station not on the line" of all the intermediate stations is not directly connected to the "two adjacent stations on the bus line", go to step 43;
step 422, the following steps: taking the intermediate sites determined in the step 421 and the sites that can be mutated into the intermediate sites as candidate sites, and determining the sites to be mutated finally according to the sum of the upstream and downstream demands of the candidate sites, wherein the upstream demand refers to the number of trips from all the sites in front of the intermediate sites to the candidate sites, and the downstream demand refers to the number of trips from the candidate sites to all the sites behind the intermediate sites;
step 43: carrying out the mutation process of the initial site according to the probability;
step 431: when the starting station and a certain station can be directly connected, the starting station can be changed into a second station, and the original second station can be changed into the starting station;
step 432: if the initial site is not directly connected with any site and no site capable of being mutated exists, the step 5 is carried out; then, performing the mutation process of the initial station as step 431 on the terminal station according to the probability;
and 5: calculating an adaptive value of the new scheme obtained after the steps according to a network analysis program, comparing the adaptive value with the adaptive value of the candidate optimal scheme in the step 2, and selecting a small candidate optimal scheme as the new candidate optimal scheme, wherein only one final scheme is selected;
step 6: repeating the step 4-5, if the repetition times reach the preset times, stopping iteration, and selecting a candidate optimal scheme as an 'optimal scheme'; and if the reproduction times do not reach the preset times, returning to the step 4. The preset times are determined according to the running time, and when the running time is limited, the preset times are ensured to be within the limit time, and the running of the program is finished; when there is no runtime restriction, the predetermined number of times is decided according to the feasible solution space, the larger the predetermined number of times.
Assuming we choose a sample network as shown in fig. 2, with 10 stations and 19 segments, we make the following assumptions (decisions) according to the model requirements:
1, all buses have the same operation speed and capacity limit, and roads are not crowded;
2, when the travel demand is not obviously changed and the original bus network scheme needs to be redesigned, compared with the original scheme, the optimization scheme does not change too much in the network configuration;
and 3, in different schemes, the number of the bus routes, the operation frequency of each vehicle and the operation cost are the same.
Setting the original scheme as an initial scheme, calculating an adaptive value by using a network analysis program, and setting the adaptive value as a first candidate optimal scheme. The bus network scheme with three lines (lines 1: 7, 2, 3, 1, 6; lines 2: 9, 10, 2, 3, 4; lines three: 8, 7, 1, 4, 6) is a candidate optimal scheme, and the following steps are carried out from step 4:
step 41: the selection process comprises the following steps: setting the selection probability to 1 means that each line needs to perform a mutation process, and the step 4 is performed by setting a certain probability according to the size of the network scale and the design time.
Step 42: and (3) intermediate site mutation process:
taking line 1 as an example, the intermediate sites have 2, 3 and 1; the intermediate station 2 has neighbors on the line at points 3 and 7 and direct connection sites off the line at points 8, 9 and 10; the adjacent stations of the intermediate station 3 on the bus line are points 2 and 1, and the directly connected stations not on the line are points 4 and 10; the adjacent stations of the intermediate station 1 on the bus line are points 3 and 6, and the directly connected stations not on the line are points 4; intermediate site 1 can mutate to a site that is point 4, and intermediate sites 2 and 3 have no mutable site. Thus when (d)71+d21+d31+d16)<(d74+d24+d34+d46) The newly generated line 1 is: 7, 2, 3, 4, 6; when (d)71+d21+d31+d16)>(d74+d24+d34+d46) The line 1 is unchanged.
Step 43: initial site mutation process:
taking line 2 as an example, when the originating station is directly connected to the third station, the originating station and the second station exchange positions, and the newly generated line 2 is: 10,9,2,3,4.
Step 432: end site mutation process:
taking line 3 as an example, when the destination station is directly connected to the third last station, the destination station and the second last station exchange positions, and the newly generated line 2 is: 8,7,1,6,4.
And 5: and (3) scheme comparison: and obtaining a new bus network scheme through the steps 41-42-43-432-5, calculating an adaptive value of the new bus network scheme by using a network analysis program, comparing the adaptive value with the adaptive values of the candidate optimal schemes, and selecting a small candidate optimal scheme as a new candidate optimal scheme, wherein only one candidate optimal scheme is provided.
Step 6: checking the iteration times: checking whether the iteration times reach the preset times, and if not, returning to the step 3; and if so, ending the program and outputting the candidate optimal scheme as a final optimal scheme.
As shown above, the original scheme is set as line 1: 7,8,910, 3, 4; line 2: 5, 4, 3, 1, 7; line 3: 9, 10, 2, 1, 4, 6; and a line 4: 2, 3, 1, 6, 5; line 5: 8,2,1,6,5,4. Assuming that the original bus network scheme can basically meet the original bus travel demand, only the travel demand within one hour after the occurrence of the inconspicuous demand change is listed, as shown in the following chart 1. Other settings are as follows: the length and time units are the same (same as the length units in fig. 2); the time cost of each unsatisfied bus demander is 80; the maximum capacity of each vehicle is 40; the maximum length and the minimum length of the bus line are 45 and 20 respectively; the departure frequency of each path is 5 vehicles/hour; the execution probabilities of steps 3.2-3.4 are all 0.1; parameter C1、C2、C3Respectively 0.15, 0.3 and 0.55.
TABLE 1 requirement matrix (order)
Figure DEST_PATH_IMAGE034
And comparing and evaluating each index of the optimal scheme and the original scheme, wherein the content of the evaluation index comprises the net, the number of direct persons, the number of transfer persons, the number of persons who do not meet the demand, the total trip mileage of the direct persons, the total trip mileage of the transfer persons and the scheme adaptive value, as shown in the following table 2.
TABLE 2 comparison of original and optimal solution results
Figure DEST_PATH_IMAGE036
As can be seen from the above table, the two schemes do not have too large line configuration change, the optimal scheme can meet more direct and transfer times than the original scheme, the times of unsatisfied demand are reduced, the function of the bus network is exerted to the maximum extent, the total travel distance of direct travelers and transfer passengers is increased, and the average transfer distance is reduced.
Example 2
The present embodiment is different from embodiment 1 in that: whereas the Traditional Genetic Algorithm (TGA) in the traffic engineering field includes selection, crossover and mutation processes, our Improved Genetic Algorithm (IGA) deletes the crossover process, including only the improved selection and mutation processes, in the comparison of example 1, we apply the improved IGA and the traditional TGA, respectively, in our case. Each method was tested 10 times separately with the predetermined number of iterations set to 1000. The comparative results are shown in FIG. 3.
As can be seen in figure 3, the adaptation values for IGA are superior to TGA, which should be due to the improvement we have made in the selection and mutation process. The computation time for IGA is generally higher than that for TGA, probably because we have eliminated the crossover process. This comparison can prove that: compared with TGA, the IGA can improve the optimization quality and the search efficiency when applied to the bus network design problem under the condition of unobvious requirement change.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited by the foregoing examples, which are provided to illustrate the principles of the invention, and that various changes and modifications may be made without departing from the spirit and scope of the invention, which is also intended to be covered by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (4)

1. A bus network optimization design method suitable for the condition of unobvious demand change comprises the following steps:
step 1: establishing an objective function and setting a limiting condition, wherein the objective function is shown as the following formula:
Figure FDA0002486427250000011
the limiting conditions are as follows:
Figure FDA0002486427250000012
Figure FDA0002486427250000013
in the formula: v is the set of all sites, i denotes site i, j denotes site j, dijRepresents the travel demand, L, from site i to site jmaxIndicating the maximum length of the bus route, LminIndicating the minimum length, Q, of the bus routemaxRepresenting the maximum capacity, T, of each vehicledRepresenting the time cost of each person who does not meet the public transportation demand, n representing the nth line of a scheme, tr representing the transfer path when travelling more than two paths, and LnThe total length of the line n is indicated,
Figure FDA0002486427250000014
indicating that the bus demand from station i to station j is satisfied on path n,
Figure FDA0002486427250000015
indicating that the bus demand from station i to station j is satisfied on the transfer path tr, DRijRepresenting the set of direct lines serving from site i to site j, TRijRepresenting the set of transfer lines servicing from site i to site j,
Figure FDA0002486427250000016
representing the travel time from station i to station j on path n,
Figure FDA0002486427250000017
representing the travel time from station i to station j on transfer path tr,
Figure FDA0002486427250000018
denotes the maximum flow on path n, C1、C2、C3Weight influence coefficients (C) representing the direct person cost, the transfer person cost and the cost for satisfying the demander respectively1+C2+C3＝1)；
Step 2: selecting an original bus network scheme as an initial scheme, and calculating an adaptive value of the original bus network scheme by using a network analysis program;
and step 3: setting the initial scheme as a candidate optimal scheme;
and 4, step 4: performing a propagation process including a selection process and a mutation process on the candidate optimal solution in the step 3,
step 41: the selection process comprises the following steps: selecting lines to be mutated in all schemes according to probability;
step 42: carrying out intermediate single-site mutation process according to probability;
step 421: determining two adjacent stops of the intermediate stop on the bus line and a direct connection stop of the intermediate stop not on the line, checking whether the direct connection stop not on the line and the two adjacent stops on the bus line are directly connected or not, if so, turning to step 422, wherein the connectable stop is a stop to which the intermediate stop can be suddenly changed; if the "directly connected station not on the line" of all the intermediate stations is not directly connected to the "two adjacent stations on the bus line", go to step 43;
step 422: taking the intermediate sites determined in the step 421 and the sites that can be mutated into the intermediate sites as candidate sites, and determining the sites to be mutated finally according to the sum of the upstream and downstream demands of the candidate sites, wherein the upstream demand refers to the number of trips from all the sites in front of the intermediate sites to the candidate sites, and the downstream demand refers to the number of trips from the candidate sites to all the sites behind the intermediate sites;
step 43: carrying out the mutation process of the initial site according to the probability;
step 431: when the starting station and a certain station can be directly connected, the starting station can be changed into a second station, and the original second station can be changed into the starting station;
step 432: if the initial site is not directly connected with any site and no site capable of being mutated exists, the step 5 is carried out; then, performing the mutation process of the initial station as step 431 on the terminal station according to the probability;
and 5: calculating an adaptive value of the new scheme obtained after the steps according to a network analysis program, comparing the adaptive value with the adaptive value of the candidate optimal scheme in the step 2, and selecting a small candidate optimal scheme as the new candidate optimal scheme, wherein only one final scheme is selected;
step 6: repeating the step 4-5, if the repetition times reach the preset times, stopping iteration, and selecting a candidate optimal scheme as an 'optimal scheme'; and if the reproduction times do not reach the preset times, returning to the step 4.
2. The method as claimed in claim 1, wherein the probability in step 4 is set according to the size of the network and the design time.
3. The method as claimed in claim 2, wherein the network analysis procedure in step 2 comprises:
step 21: eliminating the direct bearing flow of one path from the original demand matrix, distributing the flow serving as direct demands to the path, and obtaining a direct update demand matrix after the direct bearing flow of all paths is eliminated;
step 22: removing the transfer bearer flow of one transfer path from the direct update demand matrix in the step 21, distributing the flow serving as a transfer demand to a corresponding path, and obtaining a transfer update demand matrix updated by the transfer demand after all the transfer bearer flows are removed;
step 23: and (3) outputting parameters and variables required in all the objective functions in the step 1, and calculating an adaptive value of the scheme, namely Z in the objective function in the step 1.
4. A method as claimed in any one of claims 1 to 3, wherein the predetermined number of times in step 6 is determined according to the running time, and when there is a limit to the running time, the predetermined number of times is guaranteed to be within the limit time, and the running of the program is finished; when there is no runtime restriction, the predetermined number of times is decided according to the feasible solution space, the larger the predetermined number of times.
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