CN113988368A - Bus route optimization method considering existing bus network structure - Google Patents
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Abstract
The invention belongs to the technical field of public transport, and particularly relates to a bus route optimization method considering the existing bus network structure. The method is characterized in that on the basis of the existing public transportation routes, the actual road network and OD data are combined, the road and the public transportation routes are respectively represented in a node mode, meanwhile, the OD data and the node space are unified, and then the node-represented road network data, the public transportation route data and the OD data are fused and optimized through an ant colony algorithm. The method can automatically complete the planning of the bus route trend only by setting the starting point of the bus route on the basis of the existing bus network, considers the bus compound line coefficient during the road section selection, ensures the reasonability of the route and the route trend, can maximize the direct passenger flow, can effectively reduce the generation of high-repeated routes, and can effectively control the route trend.
Description
Technical Field
The invention belongs to the technical field of public transport, and particularly relates to a bus route optimization method considering the existing bus network structure.
Background
The public transport system is one of effective forms of connection of all functional areas in a city and is also one of important travel modes of urban residents. Along with the continuous change of external environments such as city expansion, functional area conversion, subway input operation and the like, public transport network adjustment and optimization become one of the conventional works of public transport management and operation units. In the case of incomplete informatization and digitization, field research is still one of the main methods for adjusting the public transportation network.
Public transport network optimization is a complex line planning and multi-objective optimization problem, and various route planning algorithms and optimization algorithms are introduced into related research fields in order to increase the scientificity and convenience of network optimization. For example, in the article of ant colony algorithm optimization aiming at vehicle path problems of Sunxiao et al, a hybrid ant colony algorithm is provided based on an ant colony algorithm, the performance of the algorithm is improved by introducing a simulated annealing algorithm, the algorithm is realized based on a parallel framework, and a better effect is achieved in a similar task. The Adam of Ursi obtains good effect in two-dimensional and three-dimensional path planning by introducing the frog leap algorithm and other strategies in the 'improvement of ant colony algorithm and application research of ant colony algorithm in path planning'. It can be seen that various optimization algorithms represented by ant colony algorithm are widely applied to the field of vehicle path selection and planning. Specifically, in the aspect of optimization of the public traffic network, Yan good knots are in the article of 'village and town public traffic network optimization design based on node multidimensional analysis', the public traffic network is optimized by adopting a genetic algorithm through abstracting and grading the public traffic nodes and combining factors such as direct passenger flow of the public traffic network, average travel distance of residents and the like. Based on the OD data and the like between the bus stations, the agility verifies the feasibility of bus route planning by adopting the ant colony algorithm on the simulation data containing 25 stations.
In addition, in the aspect of specific bus route planning and optimization, the optimization of a bus line network is completed by taking the maximum direct passenger flow and the minimum per capita cost as targets on the basis of an improved KSP algorithm in 'a bus route optimization model and a solving algorithm based on the direct passenger flow'. Wangyi provides a bus network optimization model capable of bypassing congested road sections on the basis of considering road node degrees and betweenness places in the research of urban bus network optimization model and solving algorithm based on combination of node degrees and betweenness, and solves the model based on a genetic algorithm.
Based on the research, the network optimization has various optimization targets such as maximum direct passenger flow, minimum operation cost, maximum bus network efficiency and the like, and various optimization algorithms such as ant colony algorithm, genetic algorithm and the like. Most algorithms take a random line as a candidate solution, and in the actual bus line planning, consideration and limitation of various factors such as bus stations, subway connection and the like exist, so that a certain limitation needs to be performed on the starting point or the end point of the bus, and the universality of the algorithms is also limited. In addition, the influence of the existing bus lines on the newly planned lines is considered in the rare research, and all the lines are cancelled to be re-laid, so that the actual application requirements cannot be met generally.
Disclosure of Invention
Aiming at the defects and problems that the existing bus lines are not considered in the current bus line optimization, and the algorithm has poor applicability and is not universal, the invention provides the bus line optimization method considering the existing bus line network structure.
The technical scheme adopted by the invention for solving the technical problems is as follows: a bus route optimization method considering the existing bus network structure comprises the following steps:
step one, data acquisition: acquiring actual road network data of a research area, and abstracting the actual road network data into line structure data in a GIS; meanwhile, acquiring existing bus route data in a research area and OD data information in the area;
step two, data nodularization representation:
(1) according to actual road network data, taking road intersections or intersections providing steering selection as key nodes of roads, numbering the key nodes of the roads as nodes of the roads, and then expressing the road network data into a graph data structure in the form of an adjacency matrix or an adjacency list in a graph concept;
(2) the existing bus line data is regarded as a section of adjacent road connection, and the line connection point is used as a node of the bus line;
(3) merging the origin-destination point of each OD to the nearest road node, and aggregating to obtain OD data based on the road node number according to the origin-destination node number of the OD;
step three, data fusion and optimization: the method is characterized in that the node-based road network data, the bus line data and the OD data are fused by adopting an ant colony algorithm, and the fusion method comprises the following steps:
(1) initializing parameters: planning the number M of paths and initial pheromones of road sections;
(2) initializing a starting point: taking the number of the ant populations as the number K of the bus lines needing to be optimized or planned;
(3) calculating alternative path points according to the graph structure data abstracted and completed by the road data;
(4) respectively calculating the transition probability of path selection: calculating the probability of transferring the ants from the current node to the next node:
T＝αP+βF1+δF2+ωF3
in the formula: p, F1、F2、F3Respectively representing pheromone residue, a passenger flow elicitation factor, a line length elicitation factor and a line nonlinear coefficient elicitation factor, wherein alpha, beta, delta and omega respectively represent corresponding weights, and the attention degree of the corresponding factors can be adjusted;
(5) determining the next waypoint: determining the next path point according to the probability of the selected inducing pheromone or road section;
(6) updating the path pheromone, wherein the updating of the pheromone comprises two processes of pheromone release and volatilization, and the updating strategy is as follows:
Pnew＝(1-ρ)PoldVolatilize
Wherein
Original pheromone residue on a road section with two end nodes numbered as i, j, rho is an pheromone evaporation coefficient, Q is the pheromone quantity left by the current ant, the original pheromone residue can be actually determined by referring to the passenger flow quantity on the current ant path, and theta is an adjusting parameter, and the pheromone quantity left by the current ant in each search and iteration can be adjusted as required;
(7) and (5) iteratively repeating the processes of the steps (2) to (6) until an optimal solution is output.
Step four, evaluating the public traffic network by the direct passenger flow of the public traffic network
In the formula: m is the number of bus routes in the network,
path for direct traffic between nodes i, jnIs a line in the current public transport network.
In the bus route optimization method considering the existing bus network structure, the optimal solution is the path with the maximum direct passenger flow.
In the bus route optimization method considering the existing bus network structure, the road network data is the road network of the actual roads in the research area.
According to the bus route optimization method considering the existing bus network structure, the next route point is determined according to the probability of the selected road section, and the transition probability of each road section is as follows:
wherein i is an optional ith road segment, piRepresenting the probability of selecting the road section, piThe pheromone content on a road segment.
The invention has the beneficial effects that: on the basis of the existing public traffic network, the invention can automatically complete the planning of the public traffic route by only setting the starting point of the public traffic route, and can reach the direct passenger flow to the maximum extent.
The method is adopted to plan the bus route, the bus multi-route coefficient (the number of bus routes passing through the current road section) is considered when the road section is selected, the trend of the existing line network is considered, and the generation of high-repeat routes can be effectively reduced while the reasonability of the routes and the route trend is ensured.
The method adopts the bus nonlinear coefficient and the structural change factor, can effectively control the trend of the line, avoids the generation of a large number of invalid lines such as high detour lines and the like, improves the quality of the ant colony algorithm solution, and simultaneously accelerates the convergence speed of the algorithm to a certain extent.
Drawings
Fig. 1 is a schematic representation of a road node.
Fig. 2 is a node expression example diagram of a bus line.
Fig. 3 is a process of net optimization based on the ant colony algorithm.
Fig. 4 is a schematic diagram of next path point determination.
Detailed Description
Aiming at the problem that the influence of the existing bus lines on the newly planned lines is not considered in the current bus line network optimization, the redistribution of all lines is cancelled, and the actual application requirements cannot be met, the invention provides the bus line optimization method considering the existing bus line network structure.
Example 1: the embodiment provides a bus route optimization method considering the existing bus network structure, which specifically comprises the following steps.
1. Data acquisition
Acquiring actual road network data of a research area, wherein the road network data is a road network of actual roads in the research area, abstracting the actual road network data into line structure data in a GIS, and focusing attention on the link relation of all road sections;
meanwhile, the existing bus route data in the research area and the OD data information (population travel rule) in the area are obtained.
2. Data nodularized representation
(1) According to actual road network data, road intersections or intersections providing steering selection are used as key nodes of roads, the key nodes of the roads are numbered and used as nodes of the roads, and then the road network data is expressed into a graph data structure in the form of an adjacent matrix or an adjacent table in a graph concept.
(2) The existing bus line data is regarded as a section of adjacent road connection, and the line connection point is used as a node of the bus line;
(3) and merging the origin-destination point of each OD to the nearest road node, and aggregating to obtain the OD data based on the road node number according to the origin-destination node number of the OD.
3. Data fusion and optimization
The method is characterized in that the node-based road network data, the bus line data and the OD data are fused by adopting an ant colony algorithm, and the fusion method comprises the following steps:
(1) initializing parameters: planning the number M of paths and initial pheromones of road sections;
(2) initializing a starting point: taking the number of the ant populations as the number K of the bus lines needing to be optimized or planned;
(3) and calculating alternative path points according to the data graph.
(4) Respectively calculating the transition probability of path selection: calculating the probability of transferring the ants from the current node to the next node:
T＝αP+βF1+δF2+ωF3
in the formula: p, F1、F2、F3Respectively representing pheromone residue, a passenger flow enlightening factor, a line length enlightening factor and a line nonlinear coefficient enlightening factor, and respectively representing corresponding weights, wherein the attention degree of the corresponding factors can be adjusted.
(5) Determining the next waypoint: in the process of searching the path, ants do not move along the path with the largest pheromone when transferring between adjacent nodes, each road section has a certain probability to be selected, the probability is positively correlated with the pheromone content, and in this embodiment, the transfer probability of each road section is as follows:
wherein i is an optional ith road segment, piRepresenting the probability of selecting the road section, piThe pheromone content on a road segment.
For example, as shown in fig. 4, the current position is B, the last position is a, the alternative path is { C, D, E }, and the guidance information is {0.2, 0.3, 0.5}, and if the conventional method is adopted, the E point with the largest guidance information is selected as the next waypoint. In the process of path searching, ants do not move along the path with the largest pheromone when transferring between adjacent nodes, each road section has a certain probability to be selected, the probability is positively correlated with the pheromone content, then the next path point is selected by a probability means, the probability of selecting the three points is (0.29,0.32 and 0.39), and the calculation method comprises the following steps:
C：0.29＝exp(0.2)/(exp(0.2)+exp(0.3)+exp(0.5))
D：0.32＝exp(0.3)/(exp(0.2)+exp(0.3)+exp(0.5))
E：0.39＝exp(0.5)/(exp(0.2)+exp(0.3)+exp(0.5))
by adopting the processing mode, the path can keep certain variability during each calculation, and the algorithm is prevented from being converged too fast.
(6) Updating the path pheromone, wherein the updating of the pheromone comprises two processes of pheromone release and volatilization, and the updating strategy is as follows:
Pnew＝(1-ρ)poldVolatilize
Wherein
The original pheromone residue on the road section with the serial number of nodes at two ends being i, j is used, rho is an pheromone evaporation coefficient, Q is the pheromone quantity left by the current ant, the original pheromone residue can be actually determined by referring to the passenger flow quantity on the path of the current ant, and theta is an adjusting parameter, and the pheromone quantity left by the current ant in each search and iteration can be adjusted according to needs.
(7) And (5) iteratively repeating the processes of the steps (2) to (6) until an optimal solution is output, wherein the optimal solution is a path with the maximum direct passenger flow.
4. Bus network performance assessment
Bus line network is evaluated according to direct passenger flow of bus line network
In the formula: m is the number of bus routes in the network,
path for direct traffic between nodes i, jnIs a line in the current public transport network.
Claims (4)
1. A bus route optimization method considering the existing bus network structure is characterized in that:
the method comprises the following steps:
step one, data acquisition: acquiring actual road network data of a research area, and abstracting the actual road network data into line structure data in a GIS; meanwhile, acquiring existing bus route data in a research area and OD data information in the area;
step two, data nodularization representation:
(1) according to actual road network data, taking road intersections or intersections providing steering selection as key nodes of roads, numbering the key nodes of the roads as nodes of the roads, and then expressing the road network data into a graph data structure in the form of an adjacency matrix or an adjacency list in a graph concept;
(2) the existing bus line data is regarded as a section of adjacent road connection, and the line connection point is used as a node of the bus line;
(3) merging the origin-destination point of each OD to the nearest road node, and aggregating to obtain OD data based on the road node number according to the origin-destination node number of the OD;
step three, data fusion and optimization: the method is characterized in that the node-based road network data, the bus line data and the OD data are fused by adopting an ant colony algorithm, and the fusion method comprises the following steps:
(1) initializing parameters: planning the number M of paths and initial pheromones of road sections;
(2) initializing a starting point: taking the number of the ant populations as the number K of the bus lines needing to be optimized or planned;
(3) calculating alternative path points according to the graph structure data abstracted and completed by the road data;
(4) respectively calculating the transition probability of path selection: calculating the probability of transferring the ants from the current node to the next node:
T＝αP+βF1+δF2+ωF3
in the formula: p, F1、F2、F3Respectively representing pheromone residue, a passenger flow elicitation factor, a line length elicitation factor and a line nonlinear coefficient elicitation factor, wherein alpha, beta, delta and omega respectively represent corresponding weights, and the attention degree of the corresponding factors can be adjusted;
(5) determining the next waypoint: determining the next path point according to the probability of the selected inducing pheromone or road section;
(6) updating the path pheromone, wherein the updating of the pheromone comprises two processes of pheromone release and volatilization, and the updating strategy is as follows:
Pnew＝(1-ρ)PoldVolatilize
Wherein
Original pheromone residue on a road section with two end nodes numbered as i, j, rho is an pheromone evaporation coefficient, Q is the pheromone quantity left by the current ant, the original pheromone residue can be actually determined by referring to the passenger flow quantity on the current ant path, and theta is an adjusting parameter, and the pheromone quantity left by the current ant in each search and iteration can be adjusted as required;
(7) iteratively repeating the processes of the steps (2) - (6) until an optimal solution is output;
step four, evaluating the public traffic network by the direct passenger flow of the public traffic network
In the formula: m is the number of bus routes in the network,
is a node i, jDirect passenger flow between rooms, pathnIs a line in the current public transport network.
2. The method for optimizing bus routes in consideration of the existing bus network structure as set forth in claim 1, wherein: the optimal solution is the path with the maximum direct passenger flow.
3. The method for optimizing bus routes in consideration of the existing bus network structure as set forth in claim 1, wherein: the road network data is a road network of actual roads in the research area.
4. The method for optimizing bus routes in consideration of the existing bus network structure as set forth in claim 1, wherein: determining the next path point according to the selected probability of the road section, the transition probability of each road section is
Wherein i is an optional ith road segment, piRepresenting the probability of selecting the road section, piThe pheromone content on a road segment.
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Inventor after: Guo Jianguo Inventor after: Zhang Yangcun Inventor after: Qu Hua Inventor after: Li Houlin Inventor after: Zheng Dongdong Inventor after: Pu Xiuxia Inventor after: Wang Honggang Inventor after: Li Junhui Inventor after: Zhao Xinchao Inventor after: Deng Jierong Inventor before: Guo Jianguo Inventor before: Qu Hua Inventor before: Zheng Dongdong Inventor before: Pu Xiuxia Inventor before: Wang Honggang Inventor before: Li Junhui Inventor before: Zhao Xinchao Inventor before: Deng Jierong Inventor before: Zhang Yangcun |
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