Search

We proposed a passenger control strategy and train operation plan collaborative optimization (PCS&TOP) model to schedule the train operation that improves the efficiency of passengers' travel and reduces the cost of train operation for a rail transit network. The model is an integer non linear programming model that aims to minimize the entrance and platform waiting time of passengers and the operation cost of trains. The timetable and variable train formation are integrated optimized, and the turnaround of rolling stock is also considered by constructing the train operation constraints for the model. The coordination of timetables on different lines and the coordination of passenger control strategies at different stations are mentioned by considering the transfer passengers when constructing the passenger control constraints. To solve the model, a multi-operator simulated annealing (MOSA) algorithm that includes three types of execution operators corresponding to three main decision variables is proposed. A numerical case that includes 2 bidirectional crossed lines and a real-world case from China are introduced to test the efficiency of the proposed method, which demonstrates better performance than the single and respective optimization solutions. [ABSTRACT FROM AUTHOR]

When passengers are oversaturated in the urban rail transit system and a further increase of train frequency is impossible, passenger flow control strategy is an indispensable approach to avoid congestion and ensure safety. To make the best use of train capacity and reduce the passenger waiting time, coordinative flow control is necessary at each station on a line. In most published studies, the equilibrium of passenger distributions among different stations and periods is not considered. As a result, two issues occur making it hard to implement in practical. First, a large number of passengers are held up outside a small number of stations for very long time. Second, there is a large variation of controlled flows for successive time intervals. To alleviate this problem, a single-line equilibrium passenger flow control model is constructed, which minimizes the total passenger delay. By applying different forms of the delay penalty function (constant and linear), flow control strategies such as independent flow control and equilibrium flow control can be reproduced. An improved simulated annealing algorithm is proposed to solve the model. A numerical case is studied to analyze the sensitivity of the functions, and the best parameter relationship in different functions could be confirmed. A real-world case from Batong Line corridor in Beijing subway is used to test the applicability of the model and algorithm, and the result shows that the solution with linear delay penalty functions can not only reduce the total passenger delay but also equilibrate the number of flow control passengers on spatial and temporal. [ABSTRACT FROM AUTHOR]

• Developed a macro framework optimized transportation network utility. • Proposed a general passenger utility maximization model considered efficiency and proportional fairness. • Proved the existence of optimal passenger allocation with maximum passenger utility. • Jointly optimize passenger allocation and vehicle schedules by maximizing transportation network utility. • Numerical tests verified the improvement of passengers' fairness and operation's cost. To deal with the contradiction between oversaturated demand and the restricted transport infrastructure and capacity during peak periods in a rail transit system, several passenger flow control methods that include soft pricing incentive, rigorous physical barrier and so on are widely used in current scenarios. However, the traffic disorderliness at the bottleneck and the control unfairness for the flows on specific stations may occur with those methods in the rail transit corridor. To alleviate those problems, this paper focus on the passenger allocation and matching with the corresponding schedules by considering the fairness of passengers and cost of operation from a macro perspective, where the passenger trip plan can be collected via a reservation platform in advance. A passenger utility maximization model that programs passenger allocation is proposed firstly, and we theoretically prove that there is an optimal traffic flow allocation with maximum utility under the oversaturated flow condition for any network. The model is solved by the Newton iteration algorithm and further proven to effectively make a tradeoff between efficiency and fairness. Furthermore, the transportation network utility maximization model is constructed by jointly optimizing the passenger allocation and vehicle schedules, thereby saving the cost of vehicle operation while guaranteeing the fairness of passenger allocation macroscopically. The model is solved by Multi-tree depth-first search algorithm based on Lagrangian relaxation with a subgradient in an iterative manner. A real-world case from the Batong subway line in Beijing is studied to validate the proposed models and algorithms. The results show that the model can jointly optimize the passenger allocation and vehicle schedules effectively in both an economical and fair manner, and the passengers can pass through the bottleneck in an orderly way. [ABSTRACT FROM AUTHOR]

• A scenario-based train timetabling framework is constructed to divide demand input methods into multi-scenario method (MM), average-scenario method (AM) and one-scenario method (OM). • A MM-based bi-objective mixed-integer linear programming model is formulated to design an adaptable and stable train timetable at network level from perspectives of enterprises and passengers, in which multi-scenario small-granularity passenger demand follows actual distribution. • Advanced and Adaptive NSGA-II (AANSGA-II) with advanced population sorting and adaptive genetic operation is proposed to solve the high-complexity bi-objective problem. It is critical to design an adaptable and stable train timetable for long-term use in rail transit network that not only meets the dynamicity of passenger demand in different hours within one day, but also meets the uncertainty of passenger demand in different days. In this study, a scenario-based train timetabling framework is constructed to classify the possibilities of passenger demand in multiple days into a set of scenarios based on profile and volume of passenger demand. On this basis, multi-scenario demand input method (MM) is introduced to deal with the uncertainty of passenger demand, which is different from one-scenario method (OM) and average-scenario method (AM). A MM-based mixed-integer linear programming model is formulated for the bi-objective train timetabling problem under uncertain and dynamic demand at acyclic network level, in which multi-scenario small-granularity passenger demand follows actual distribution processed from historical data. The two objectives are to minimize train service cost and penalized passenger waiting time from perspectives of enterprises and passengers. Advanced and Adaptive NSGA II (AANSGA-II) is proposed to cope with the high-complexity bi-objective problem, which applies advanced population sorting based on neighborhood distance, adaptive genetic operation based on scoring mechanism and improved population initialization based on boundary individuals. The model and algorithm are testified by a small-scale numerical experiment on a virtual line and a large-scale real-world instance in Shenyang Metro network. As a result, MM-based train timetables are generally better than AM-based and OM-based train timetables in reducing generalized cost and raising robustness. Besides, AANSGA-II is more applicable than NSGA-II and CPLEX in shortening computation time at the same time of improving computation result. [ABSTRACT FROM AUTHOR]

• Develop integrated model to optimize the train stop plan and timetable under time-dependent passenger demand. • Improves passenger travel efficiency and reduces train running costs. • The number of trains and stops can be changed freely, oversaturation is permitted. • Improved extended adaptive large neighborhood search approach for solving. Train stop plans and timetables play key roles in railway operation. Previous research has demonstrated that their integration can significantly improve the quality of a train timetable, especially for commuter railways with flexible service frequencies and multiple stop plans. However, solving the dilemma of the mathematical tractability and practicality of the model is still an open challenge. To obtain a high-quality timetable and simultaneously consider more realistic conditions, an integrated combination optimization model of both train stop plans and timetables under time-dependent passenger demand is proposed in this article. More realistic conditions, such as no predefined schedule, a variable total number of trains and oversaturation, are taken into account. The problem is modeled as a mixed-integer nonlinear programming problem (MINLP) to optimize passenger travel efficiency and mainly consists of (1) the total waiting time at stations, (2) the delay time for trains due to a train stop, and (3) the minimization of the total train running time. An extended adaptive large-scale neighborhood search (ALNS) algorithm is developed to solve the problem. A numerical experiment is designed to test the validity of the model and the algorithm. Then, the integrated approach is applied in a real-world case. The results show that the proposed approach can simultaneously reduce the passenger total waiting time and delay time as well as the train running time within a short computation time and demonstrate the effectiveness of the model and the approach. [ABSTRACT FROM AUTHOR]