An improved cellular automata model for train operation simulation with dynamic acceleration

Li, Wenjun

doi:10.1142/s0217984918500872

Cited by 3 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The train operation system is a complex multi-objective nonlinear system, which needs to take into account multiple performance indicators such as safety, punctuality, energy saving, accurate parking and comfort [1][2][3]. At the same time, the train is restricted by a variety of constraints in the operating process, and it has a lot of uncertainty [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Train Operation Strategy Optimization Based on a Double-Population Genetic Particle Swarm Optimization Algorithm

Liu

Wang

2019

Energies

View full text Add to dashboard Cite

Train operation strategy optimization is a multi-objective optimization problem affected by multiple conditions and parameters, and it is difficult to solve it by using general optimization methods. In this paper, the parallel structure and double-population strategy are used to improve the general optimization algorithm. One population evolves by genetic algorithm (GA), and the other population evolves by particle swarm optimization (PSO). In order to make these two populations complement each other, an immigrant strategy is proposed, which can give full play to the overall advantages of parallel structure. In addition, GA and PSO is also improved, respectively. For GA, its convergence speed is improved by adjusting the selection pressure adaptively based on the current iteration number. Elite retention strategy (ERS) is introduced into GA, so that the best individual in each iteration can be saved and enter the next iteration process. In addition, the opposition-based learning (OBL) can produce the opposition population to maintain the diversity of the population and avoid the algorithm falling into local convergence as much as possible. For PSO, linear decreasing inertia weight (LDIW) is presented to better balance the global search ability and local search ability. Both MATLAB simulation results and hardware-in-the-loop (HIL) simulation results show that the proposed double-population genetic particle swarm optimization (DP-GAPSO) algorithm can solve the train operation strategy optimization problem quickly and effectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Train Operation Strategy Optimization Based on a Double-Population Genetic Particle Swarm Optimization Algorithm

Liu

Wang

2019

Energies

View full text Add to dashboard Cite

show abstract

Multiobjective Optimization on the Operation Speed Profile Design of an Urban Railway Train With a Hybrid Running Strategy

Zhu

Zhang

et al. 2022

IEEE Intell. Transport. Syst. Mag.

View full text Add to dashboard Cite

An improved car-following model considering the impact of safety messages

Hui

Zhao

2018

Mod. Phys. Lett. B

View full text Add to dashboard Cite

The existing car-following models of connected vehicles commonly lack experimental data as evidence. In this paper, a Gray correlation analysis is conducted to explore the change in driving behavior with safety messages. The data mining analysis shows that the dominant factor of car-following behavior is headway with no safety message, whereas the velocity difference between the leading and following vehicle becomes the dominant factor when warning messages are received. According to this result, an extended car-following model considering the impact of safety messages (IOSM) is proposed based on the full velocity difference (FVD) model. The stability criterion of this new model is then obtained through a linear stability analysis. Finally, numerical simulations are performed to verify the theoretical analysis results. Both analytical and simulation results show that traffic congestion can be suppressed by safety messages. However, the IOSM model is slightly less stable than the FVD model if the average headway in traffic flow is approximately 14–20 m.

show abstract

An improved cellular automata model for train operation simulation with dynamic acceleration

Cited by 3 publications

References 36 publications

Train Operation Strategy Optimization Based on a Double-Population Genetic Particle Swarm Optimization Algorithm

Train Operation Strategy Optimization Based on a Double-Population Genetic Particle Swarm Optimization Algorithm

Multiobjective Optimization on the Operation Speed Profile Design of an Urban Railway Train With a Hybrid Running Strategy

An improved car-following model considering the impact of safety messages

Contact Info

Product

Resources

About