Battery electric vehicles (BEVs) have emerged as a cornerstone of sustainable transportation systems, driving a fundamental transformation in public transport (PT) operations over the past decade. The unique characteristics of BEVs, including range limitations and battery degradation dynamics, necessitate a multi-dimensional optimization framework that simultaneously considers energy supply management, operational efficiency, and battery lifecycle optimization in transit scheduling and timetabling. This paper presents a systematic review of BEV timetabling and scheduling research, structured around three main contributions. First, it comprehensively examines the evolution of electric vehicle timetabling problems, providing a detailed comparative analysis of methodological approaches in this domain. Second, it identifies and critically evaluates key developments in electric vehicle scheduling, including extended research directions (such as the integration with crew scheduling) and their practical implications. Third, it investigates the integration of BEV scheduling and timetabling, synthesizing the strengths and limitations of current methodologies while outlining promising avenues for future research. By offering a comprehensive analysis of the advancements in battery electric public transport scheduling over the past decade, this review serves as both a technical reference and a strategic guide for researchers and practitioners in the field of sustainable transportation systems.

Pure electric public transport management optimization can promote the electrification evolution from conventional diesel emission to low/zero carbon transport revolution. However, emerging electric vehicle scheduling (EVS) takes into account battery capacity, battery-allowed mileage, and charging duration, which are a few concerns present at the conventional motor bus planning level. Concentrating on this new challenge, this paper builds a multi-type electric vehicle scheduling model, featuring rigorous load capacity, battery-allowed mileage, and recharging duration constraints. The binary decision variables involving the connection between departure and arrival times, as well as the recharging necessity, are judged simultaneously. The objective is to minimize the fleet size, idle mileage, and charging cost. A preprocessing-based genetic algorithm is used to handle this mixed-integer nonlinear programing model. Numerical examples are tested to validate the effectiveness of the proposed models and the solution algorithm. Compared with a single large-type vehicle scheme, the total cost of multi-type vehicle scheduling in one-trip, two-trip, and three-trip frequency scenarios are reduced by 20.8%, 6.3%, and 9.1%, respectively.

The past COVID-19 pandemic introduced the world to the necessity of dealing with the trade-off between minimizing probability of contagion, and providing people with services they need. This trade-off stipulates that a large person-to-person distance will reduce contagion probability, but will render service inefficient, and vice versa. This work focuses on the urban rail transit (URT) hub, as an example of a busy passenger area, from which we can derive an optimal preparedness policy to use during the pandemic time of any coronaviruses. We use simulation methodology, based on the classical social force model, to represent behaviors and characteristics of pedestrians. Passenger flow movement process is a mechanism we explore to figure out how the epidemic management policy and pedestrian psychological-related behaviors interact with the URT system. The systems’ complexity regarding contagion-prevention distances are tested over a few scenarios: before/after the outbreak, and for different person-to-person distances demonstrating different crowd levels. A case study of Xinjiekou Station, Nanjing URT, China, enables assessment of passenger management policy with person-to-person distances of 0.5 m, 1.0 m and 2.0 m. Multi-scenario performance illustrates the trade-off in dynamic between the efficiency of pedestrians’ walking behaviors and the distancing needs for preventing coronaviruses transmission. The results show that queuing length with social distancing of 1.0 m and 2.0 m is increased by 4.17% and 21.22%. The average delays in boarding are 14.1 s and 22.5 s for 1.0 m and 2.0 m, which leads to 15.29% and 22.39% increases, respectively, in comparison with ordinary social distancing of about 0.5 m.