Flexibility of scheduling and operation are important factors of bus rapid transit (BRT). Determining the headway and routes combinations according to the travel demand is the key in schedule optimization of BRT. This paper analyzes the existing model and establishes optimal mathematical model for BRT scheduling. This helps to determine the optimal bus headway and routes aiming to minimize the total travel time of passengers and operating costs of BRT. A genetic algorithm (GA) which applies two-dimensional 0–1 coding to represent pass-by stops sets and frequency was designed to solve the problem. Different schedule is determined by one-point crossover of GA. Finally, the model and the algorithms are illustrated as B1 of BRT in Guangzhou. Paramics-based simulation and evaluation results show that the method can improve BRT scheduling optimizing efficiency.

Bus Rapid Transit Scheduling Optimal Model Based on Genetic Algorithm

Bus dispatch (BD) system plays an essential role to ensure the efficiency of public transportation, which has been frequently addressed by the heuristic algorithms. In this paper, five well-exploited heuristic algorithms, i.e. Genetic algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony algorithm (ABC), Bacterial Foraging Optimization (BFO) and Differential Evolution algorithm (DE), are employed and compared for solving the problem of BD. The comparison results indicate that DE is the best method in dealing with the problem of BD in terms of mean, minimum, and maximum, while BFO obtains the minor lower value of standard deviation and achieves the similar convergence speed in comparison to DE. The performance of PSO seems to outperform the remaining two algorithms (i.e. ABC and GA) in most cases. However, among five algorithms, GA achieves the worst results in terms of the weight estimated objective (i.e. number of departures and average waiting time).

A Comparison of Heuristic Algorithms for Bus Dispatch

Focusing on the optimization of bus scheduling,this paper establishes an optimization model that aims to minimize both public transportation company's total costs and passenger's average waiting time.The bus scheduling was optimized by modified particle swarm optimization algorithm and the algorithm was validated by the real scheduling data.The result shows that this algorithm could not only benefit to the public transportation company but also the passengers and it is applicable and effective.

Wang Kai

Papers

Study on bus scheduling based on particle swarm optimization