The rapid growth of passenger flow in urban rail transit has led to great service pressures for metro companies in organizing train services to provide higher transportation capacities in order to satisfy passengers&#...The rapid growth of passenger flow in urban rail transit has led to great service pressures for metro companies in organizing train services to provide higher transportation capacities in order to satisfy passengers' travel demand, especially on those metro lines with insufficient rolling stock. In order to cope with high passenger flow service pressure, a mixed integer nonlinear programming(MINLP) model is proposed to optimize the line plan, timetable and rolling stock circulation simultaneously, to reduce the number of rolling stocks and increase the number of full-length services. A two-step algorithm strategy is proposed. In the first stage, the train timetable is optimized under the assumption that all the train services are the full-length services. In the second stage, the rolling stock plan is optimized based on the timetable optimized in the first stage. To ensure a feasible rolling stock circulation, certain full-length services are shortened to the short-length services due to the limited number of rolling stocks. Numerical experiments are performed based on the real-life data of Shanghai Metro Line 8. Results show that the proposed method can efficiently optimize the timetable and rolling stock circulation of the whole operation day. The optimized results are beneficial for both the service and the operational costs.展开更多
This study investigates the use of autonomous vehicles in bus rapid transit lanes during the initial phases of autonomous driving development.The aim is to accelerate the advancement of autonomous driving technologies...This study investigates the use of autonomous vehicles in bus rapid transit lanes during the initial phases of autonomous driving development.The aim is to accelerate the advancement of autonomous driving technologies and enhance the efficiency of bus lane usage.We first develop a dynamic joint optimization model that adjusts autonomous vehicle speeds and bus timetables to minimize vehicle travel times while reducing bus passenger waiting times.We account for random variables such as stochastic passenger arrivals at bus stations and variable demand for autonomous vehicle travel by constructing a stochastic dynamic model.To address the computational challenges of large-scale scenarios,we implement a simulation-based heuristic algorithm framework.This framework is designed to efficiently produce high-quality solutions within feasible time limits.Our numerical studies on an actual bus line show that our approach significantly improves system throughput compared to existing benchmarks.Moreover,by strategically managing the entry of autonomous vehicles into the lane and modifying bus timetables,we further enhance the operational efficiency of the system.展开更多
基金Sponsored by the National Key R&D Program of China (Grant No.2021YFB1600100)。
文摘The rapid growth of passenger flow in urban rail transit has led to great service pressures for metro companies in organizing train services to provide higher transportation capacities in order to satisfy passengers' travel demand, especially on those metro lines with insufficient rolling stock. In order to cope with high passenger flow service pressure, a mixed integer nonlinear programming(MINLP) model is proposed to optimize the line plan, timetable and rolling stock circulation simultaneously, to reduce the number of rolling stocks and increase the number of full-length services. A two-step algorithm strategy is proposed. In the first stage, the train timetable is optimized under the assumption that all the train services are the full-length services. In the second stage, the rolling stock plan is optimized based on the timetable optimized in the first stage. To ensure a feasible rolling stock circulation, certain full-length services are shortened to the short-length services due to the limited number of rolling stocks. Numerical experiments are performed based on the real-life data of Shanghai Metro Line 8. Results show that the proposed method can efficiently optimize the timetable and rolling stock circulation of the whole operation day. The optimized results are beneficial for both the service and the operational costs.
文摘This study investigates the use of autonomous vehicles in bus rapid transit lanes during the initial phases of autonomous driving development.The aim is to accelerate the advancement of autonomous driving technologies and enhance the efficiency of bus lane usage.We first develop a dynamic joint optimization model that adjusts autonomous vehicle speeds and bus timetables to minimize vehicle travel times while reducing bus passenger waiting times.We account for random variables such as stochastic passenger arrivals at bus stations and variable demand for autonomous vehicle travel by constructing a stochastic dynamic model.To address the computational challenges of large-scale scenarios,we implement a simulation-based heuristic algorithm framework.This framework is designed to efficiently produce high-quality solutions within feasible time limits.Our numerical studies on an actual bus line show that our approach significantly improves system throughput compared to existing benchmarks.Moreover,by strategically managing the entry of autonomous vehicles into the lane and modifying bus timetables,we further enhance the operational efficiency of the system.
