Sensitivity analysis method for increasing the passenger demands of parking and riding

Focused on finding out the relationship between passenger demands of P&R and its influencing factors, a nested-logit mode choice model was developed based on the characteristic of different modes and transfer rules. The utility functions were given respectively according to the characteristic of each alternative. Passenger demands of different modes between O-D pairs were obtained by making use of the binary logit model. Then an equilibrium model for different modes was proposed. Under this condition, the approximate relationship between passenger demands of different modes and their characteristic indexes was modeled by the sensitivity analysis method. Shift volume among different modes was achieved by utilizing this model when their characteristic indexes were changed. A case study indicates that the model and algorithm presented in this paper are effective.

Last train timetable optimization for metro network to maximize the passenger accessibility over the end-of-service period

Purpose-This study aims to improve the passenger accessibility of passenger demands in the end-ofoperation period.Design/methodology/approach-A mixed integer nonlinear programming model for last train timetable optimization of the metro was proposed considering the constraints such as the maximum headway,the minimum headway and the latest end-of-operation time.The objective of the model is to maximize the number of reachable passengers in the end-of-operation period.A solution method based on a preset train service is proposed,which significantly reduces the variables of deciding train services in the original model and reformulates it into a mixed integer linear programming model.Findings-The results of the case study of Wuhan Metro show that the solution method can obtain highquality solutions in a shorter time;and the shorter the time interval of passenger flow data,the more obvious the advantage of solution speed;after optimization,the number of passengers reaching the destination among the passengers who need to take the last train during the end-of-operation period can be increased by 10%.Originality/value-Existing research results only consider the passengers who take the last train.Compared with previous research,considering the overall passenger demand during the end-of-operation period can make more passengers arrive at their destination.Appropriately delaying the end-of-operation time can increase the proportion of passengers who can reach the destination in the metro network,but due to the decrease in passenger demand,postponing the end-of-operation time has a bottleneck in increasing the proportion of passengers who can reach the destination.

A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line

Regular coronavirus disease 2019(COVID-19)epidemic prevention and control have raised new require-ments that necessitate operation-strategy innovation in urban rail transit.To alleviate increasingly seri-ous congestion and further reduce the risk of cross-infection,a novel two-stage distributionally robust optimization(DRO)model is explicitly constructed,in which the probability distribution of stochastic scenarios is only partially known in advance.In the proposed model,the mean-conditional value-at-risk(CVaR)criterion is employed to obtain a tradeoff between the expected number of waiting passen-gers and the risk of congestion on an urban rail transit line.The relationship between the proposed DRO model and the traditional two-stage stochastic programming(SP)model is also depicted.Furthermore,to overcome the obstacle of model solvability resulting from imprecise probability distributions,a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form.A hybrid algorithm that combines a local search algorithm with a mixed-integer linear programming(MILP)solver is developed to improve the computational efficiency of large-scale instances.Finally,a series of numerical examples with real-world operation data are executed to validate the pro-posed approaches.

Optimization method of urban rail train operational plan based on O-D time-varying demand

Purpose–Under the constraints of given passenger service level and coupling travel demand with train departure time,this study optimizes the train operational plan in an urban rail corridor to minimize the numbers of train trips and rolling stocks considering the time-varying demand of urban rail passenger flow.Design/methodology/approach–The authors optimize the train operational plan in a special network layout,i.e.an urban rail corridor with dead-end terminal yard,by decomposing it into two sub-problems:train timetable optimization and rolling stock circulation optimization.As for train timetable optimization,the authors propose a schedule-based passenger flow assignment method,construct the corresponding timetabling optimization model and design the bi-directional coordinated sequential optimization algorithm.For the optimization of rolling stock circulation,the authors construct the corresponding optimization assignment model and adopt the Hungary algorithm for solving the model.Findings–The case study shows that the train operational plan developed by the study’s approach meets requirements on the passenger service quality and reduces the operational cost to the maximum by minimizing the numbers of train trips and rolling stocks.Originality/value–The example verifies the efficiency of the model and algorithm.

Estimating walking access area for rail transit station based on discrete choice model

The discrete choice model is used to estimate the walking access area of rail transit stations while considering the influence of existing competition from other traffic modes. The acceptable walking access area is determined according to the willingness of passengers to walk who prefer rail transit compared with bus and automobile. Empirical studies were conducted using the survey data of six stations from the rail transit in Nanjing, China. The results indicate that the rail transit is more preferable compared with bus and private automobile in this case when excluding the influence of individual and environmental factors. It is found that passengers tend to underestimate their willingness to walk. The acceptable walking access area of every rail transit station is different from each other. Suburban stations generally have a larger walking access area than downtown stations. In addition, a better walking environment and a scarcer surrounding traffic environment can also lead to a larger walking area. The model was confirmed to be effective and reasonable according to the model validation. This study can be of benefit to the passenger transportation demand estimation in the location planning and evaluation of rail transit stations.