Electric buses have long been recognized as a promising direction for offering sustainable public transportation services. While range and battery performance constraints have hindered the widespread adoption of electric buses in the past, technological advances make them a prominent and attractive option for public transportation in the future. Still, operational constraints and the need for additional (charging) infrastructure highlight the need for introducing appropriate decision-making tools, tailor-made for supporting the design of transit networks operated by electric buses. This paper focuses on developing and testing a comprehensive route design model for the case of a transit network, operated exclusively by an electric bus fleet (Electric Transit Route Network Design Problem—E-TRNDP). The model is formulated as a bi-level optimization problem, which attempts to jointly design efficient transit routes and locate required charging infrastructure. A multi-objective, particle swarm optimization algorithm, coupled with a mixed linear—integer programming model is used to solve the model. An existing benchmark network is used as a test-bed for the proposed model and solution process; results illustrate that the proposed model and solution method yield realistic design outcomes in an acceptable time frame.
Intelligent Transportation Systems (ITS) applications in public transportation have allowed for automated data collection, which is particularly useful for planning and operations. While technological advancement of ITS has so far been extensive, their usage for developing relevant planning and operational tools is rather limited. Research on planning and operations of public transportation systems has not widely investigated the potential of combining optimization models with data originating from ITS. Such applications, which could benefit from such an approach include route planning, scheduling and resource allocation in real time. In this context, this paper investigates and critically discusses potential models and methodologies in public transport planning and operations, which can benefit from ITS data, highlights their potential and identifies possible research paths on that area. The overview of literature collectively points to a series of common challenges faced by transportation professionals and underlines the need for better decision support tools for ITS data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.