When urban rail transit (URT) does not provide 24-hour services, passengers who travel at late night may not be able to reach their destinations with only URT trains. As a result, passengers have to find alternative transport means, or combine URT trains with other transport services to fulfill their journeys. This paper investigates the integrated optimization of last train timetabling and bridging service design with consideration of passenger path choices. Two bridging services are considered: taxis and buses. Based on pre-constructed path sets, a bi-objective mixed-integer nonlinear programming (MINLP) model is developed, aiming at minimizing total passenger travel time and total passenger travel cost. To reduce the model scale and improve solution efficiency, three path dominance principles are proposed to remove redundant passenger paths without loss of optimality. An adaptive iterative algorithm is designed to obtain the Pareto frontier curve. The proposed model and solution methods are demonstrated on the Chengdu URT network. Results indicate that passenger travel costs and travel times can be significantly reduced by the integrated optimization. It also provides passengers with a safer night travel environment due to the reduction in passenger travel times in taxis.

In cities where the urban rail transit (URT) systems do not provide 24-h services, passengers may not be able to reach their destinations if the last train services have closed by the time they arrive at the transfer stations. This paper aims to seek a well-coordinated last train timetable that can transport as many passengers as possible to their destinations (referred to as reachable passengers) and also transport those passengers who cannot reach their destinations (referred to as unreachable passengers) to the stations as close as possible to their destinations. A bi-objective mixed-integer linear programming (MILP) model is developed to maximize the number of reachable passengers and minimize the total remaining travel distance of all passengers. The augmented ε-constraint method is applied to generate all Pareto optimal solutions of the bi-objective MILP model. Numerical experiments were implemented in the Chengdu URT network. Results indicate that compared to the current-in-use timetable, the optimized timetable by our methods significantly increased the number of reachable passengers and meanwhile reduced the average remaining travel distance of unreachable passengers. In addition, we discussed two possible strategies to improve passengers’ destination reachability, which are encouraging passengers to arrive early at their origin stations, and optimizing the timetable of last trains and non-last trains at the same time.

China Railway Express (CRE) is one of the most important constituent parts of the Belt and Road Initiative. Freight demand analysis is fundamental as a basis for the operational strategy of CRE and the investment policy along the CRE-routes. Most of the existing relevant literature has focused on the organization of the train operations of CRE, with little research related to demand analysis. This paper contributes to filling this gap by estimating customers’ demand preferences for rail freight service attributes, by using a novel multi-criteria decision-analysis (MCDA) method namely Best-Worst Method (BWM). To this end, a BWM survey was conducted in China to capture customers’ preferences for the main attributes that define the transport service offered by CRE. Two variants of BWM, the linear and the Bayesian, are employed for the analysis. Reliability is suggested as the most important attribute for CRE to focus on, to gain customers. We also conduct a cluster analysis based on the results, which helps the CRE operator to identify homogeneous customer segments, and to optimize the use of CRE's resources with a differentiated pricing strategy.

In this paper, a matheuristic iterative approach (MHIA) is proposed to solve the line planning problem, also called network design problem, and frequency setting on the Chinese high-speed railway network. Our optimization model integrates the cost-oriented and passenger-oriented objectives into a profit-oriented objective. Therefore, the passenger travel time is incorporated in the ticket price using a travel time value. As a result, transfers and detours will result in lower ticket prices and thus lower revenues for the operator. When evaluating the performance of a given line plan, the way in which passengers will travel through the network needs to be modelled. This passenger assignment is typically a time-consuming calculation. The proposed line planning approach iteratively improves the line plan using easy-to-determine indicators. During the process, a mixed integer linear programming model addresses the passenger assignment and optimizes the frequency setting in order to maximise the operational profit. Extensive computational experiments are executed to show the effectiveness of the proposed approach to deal with the real-world railway network line planning problem. Through extensive computational experiments on the small example network and real-world-based instances, the results show that the proposed model can improve the profits by 22.4% on average comparing to their initial solutions. When comparing to an alternative iterative approach, our proposed method has advantage of obtaining high quality of solutions by improving the profit 10.8% on average. For small, medium, and large size networks, the obtained results are close to the optimal solutions, when available.

This paper aims to demonstrate the effect of recognizing heterogeneity in values of time on the design of a hub network for freight transportation. By taking the VOT distribution into account, we emphasize shippers' broader logistical, social and economic situation in the network design, and are not limited to commodity types. The paper employs the single allocation p-hub median problem which minimizes the total generalized transportation cost (time, distance, etc.) with given demands. VOT is assumed to be discretely distributed, and estimated by mean-dispersion model and Latent Class model, based on a Stated Preference survey conducted in China, investigating railway shippers' choice behavior of railway services. A local railway network with 14 nodes and 20 linkages is applied to discuss the effect of VOT distribution on multiple (i.e., 3) hub location strategy. Simulated Annealing (SA) is designed to solve the single allocation p-hub median problems. The numerical results shows that the VOT and its distribution should be taken into account for better simulating railway shippers' heterogeneous valuations of service time versus time.

Chinese high-speed railways faced a fast development in recent years. Their performances are still confronted with disruptions unavoidably, which impact on the reliability of the traffic and passenger satisfaction. This paper presents a rescheduling model which is able to solve the critical problem of effective disruption management (namely, fast and dynamic train speed adaptation, supervision of braking and changing train sequence due to incidents, warnings or alarms), and consider in detail the signalling and safety systems based on a quasi-moving block system with variable headways. We integrate the modelling of efficient traffic management measures and the supervision of speed, braking and headway in one general job-shop model. We use a commercial solver with a custom-designed two-step method to speed up the procedure in order to solve instances from real-world high-speed networks in China quickly. Overall, the approach guarantees the resolution of the traffic control and speed management within few minutes of computation time. The output demonstrates that the proposed approach can achieve a reduction of train delays by 70% compared to the solution determined by keeping the order of the original timetable, and get the optimality for more than 90% of instances with a realistic case.

Chinese high-speed railways faced large development in the recent years. Their performance is still confronted with disruptions, which impact the traffic and the passengers. With the aim to better understand the influences of the sources of disruptions, we study the statistical characteristics of the causes of disruptions and delayed traffic. We use maximum likelihood estimation to determine the probability density distribution of the different disruption source. A zero-truncated negative binomial distribution model is then developed to link the sources of disruptions and the amount of delayed traffic. This is important to determine the probability and the impact of disruptions sources. We can then suggest which disruption sources should be tackled in order to reduce impact and probability of disruptions.