Abstract
Dynamic bus scheduling refers to adjusting the departure time according to the latest time-varying information or adjusting bus speed in the process of operation. These control strategies can prevent bus bunching and alleviate traffic pressure. The paper studies the multiline bus dynamic scheduling with consideration of departure time and speed meanwhile. The hyperheuristic algorithm is proposed, and low-level heuristics (LLH) operators are designed. The simulation experiment is performed for the passenger flow distribution of different strengths and types of different scenarios. By comparing the experimental results of genetic algorithm (GA) and hyperheuristic algorithm in solving different scenarios, the results show that in smooth, increasing, decreasing, and multiconvex passenger flow mode, the performance of the hyperheuristic algorithm is higher than that of GA. The promotion rate reaches 18∼28%, and especially the average value of the hyperheuristic algorithm designed under multiconvex passenger flow is up to 28.62%, significantly reducing passengers’ waiting time. By comparing the stability of the three passenger flow modes, the results illustrate that the stability of the hyperheuristic algorithm is lower than that of GA. For the smooth passenger flow mode, the stability of medium and lower density of GA is higher than that of the hyperheuristic algorithm. In comparison, the high-density stability of the hyperheuristic algorithm is better than that of GA.
Highlights
Dynamic bus scheduling refers to adjusting the departure time according to the latest time-varying information or adjusting bus speed in the process of operation. ese control strategies can prevent bus bunching and alleviate traffic pressure. e paper studies the multiline bus dynamic scheduling with consideration of departure time and speed . e hyperheuristic algorithm is proposed, and low-level heuristics (LLH) operators are designed. e simulation experiment is performed for the passenger flow distribution of different strengths and types of different scenarios
By comparing the experimental results of genetic algorithm (GA) and hyperheuristic algorithm in solving different scenarios, the results show that in smooth, increasing, decreasing, and multiconvex passenger flow mode, the performance of the hyperheuristic algorithm is higher than that of GA. e promotion rate reaches 18∼28%, and especially the average value of the hyperheuristic algorithm designed under multiconvex passenger flow is up to 28.62%, significantly reducing passengers’ waiting time
Is study is based on a multiline dynamic bus dispatching model with departure time and speed in [2], which is to make the passengers’ waiting time achieve minimum by determining the bus departure time at the first station and average speed between stations. e study designed a Discrete Dynamics in Nature and Society hyperheuristic algorithm, through simulation experiments conducted for passenger flow distribution of different strengths and types in different scenarios, comparing the experimental results with a genetic algorithm (GA)
Summary
Multiline bus dynamic scheduling is known as regional scheduling. ere are generally two methods to solve the transfer coordination problem of regional scheduling in the research, respectively, based on the best adjustment value of the scheduling scheme and achieving the bus number of simultaneous arrivals maximum. Fleurent et al [14] constructed a network flow model when studying the timetable scheduling problem of regional vehicles, introduced three transfer time types of maximum, minimal, and optimal, and developed a Lagrangian relaxation and heuristic algorithm for solution optimization. Li and Li [15] studied the real-time dispatching optimization method in the bus hub, established the real-time dispatching optimization model in the bus hub according to transfer efficiency, presented the optimization model for minimizing the total cost, and developed the random perturbation approximation algorithm to optimize the solution. Sun [17] conducted a detailed and comprehensive study on the dynamic optimization of urban multiline bus transmission under the Internet of ings environment, considered the impact of various factors such as vehicle capacity limit, multimodel, bus company operating expenses on the model, and constructed mathematical models and heuristic algorithm to solve the optimization respectively. Different simulation experiments are designed, and the results are compared with GA and hyperheuristic algorithm, which provides a reference for bus dynamic scheduling model establishment
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