Urban rail transit (URT) demand often exhibits tidal or centripetal shapes during peak/off-peak hours. Inefficiency occurs when the supply of service is not adjusted to the fluctuating demand. This optimization can be realized by redesigning the URT timetables and/or through operational strategies. Certainly, implementation of the optimal/best-adjusted timetables must be feasible in order to accommodate efficient operations planning. This work develops a new methodology to derive an optimal timetabling design procedure for multiphase demands on a bi-directional URT line. It consists of three operational policies: marshaling, skip-stop operation, and robustness. The order of service is determined by the departure times. Following these considerations, this work develops a hybrid model formulated as a mixed-integer programming framework. The case study demonstrates the ability of the modeling framework to assess integrated effects, guarantee its robustness and to bring about 13% saving of the total cost associated with passengers, operations and reliability.

Stop-skipping is a key method for alleviating congestion in rail transit, where schedules are sometimes difficult to implement. Several mechanisms have been proposed and analyzed in the literature, but very few performance comparisons are available. This study formulated train choice behavior estimation into the model considering passengers’ perception. If a passenger’s train path can be identified, this information would be useful for improving the stop-skipping schedule service. Multi-performance is a key characteristic of our proposed five stop-skipping schedules, but quantified analysis can be used to illustrate the different effects of well-known deterministic and stochastic forms. Problems in the novel category of forms were justified in the context of a single line rather than transit network. We analyzed four deterministic forms based on the well-known A/B stop-skipping operating strategy. A stochastic form was innovatively modeled as a binary integer programming problem. We present a performance analysis of our proposed model to demonstrate that stop-skipping can feasibly be used to improve the service of passengers and enhance the elasticity of train operations under demand variations along with an explicit parametric discussion.

Unanticipated events inevitably occur in daily urban rail transit operations, disturbing the scheduled timetable. Despite the mild delay, the busy operation system probably tends to worsen a larger disturbance and even lead to a knock-on disruption if no rescheduling is timely carried out. We propose a bi-objective mixed-integer linear programming model (MILP) that employs the skip-stop operation strategy to eliminate unscheduled delays. This model addresses two distinct, yet interconnected objectives. Firstly, it aims to minimize the difference between the plan and the actual operation. Secondly, it strives to minimize the number of left-behind passengers. In order to resolve this MILP problem, we devised a Pareto-based genetic algorithm (GA). Based on the case study, we certify the superior effectiveness with comparisons to the whale optimization algorithm and the epsilon constraint method. The outcomes affirm that our model has the potential to reduce the total delay time of the line by 44.52% at most compared with the traditional all-stop running adjustment model. The optimal scheme saved 6.08% of the total costs based on a trade-off between operators’ interests and passenger satisfaction.

This study proposed an investigation-based multiple-criteria coordinated model to evaluate the sustainable development of urban public transport (PT) infrastructure, based on economic, social and environmental data from 2009 to 2019. The main problem with the traditional approach for assessing urban PT development is that economic and social benefits are considered individually, but also attention to environmental factors and coordination among the three issues are nearly overlooked. This leads to the likelihood of inaccuracies in the handling/assessment of sustainable development or an imbalance among the attributes in different cities. An investigation-based coordinated model was introduced in which a survey of 35 sub-criteria was conducted to derive the criteria necessary for coupling/coordination. A case study involving 13 cities in Jiangsu Province, China, illustrated the problems in coordinating PT systems and verified the efficacy of the proposed approach. With employing the entropy method, this study validated coordination of the PT infrastructure development of various cities in a balanced manner and used panel regression formulas to analyse the theoretical gap and empirical bottlenecks existing among economic, social and environmental benefits. With the findings of the study, the data-based investigation from 13 cities enabled the city planners/managers (including ones from other cities with similar urban levels) to give the individual priority between the ternary benefits, advance technology, allow big data-based informatisation and implement near-future autonomous PT vehicles.