Towards Optimized Deployment of Electric Bus Systems Using Cooperative ITS

In this paper we analyze the impact of using cooperative intelligent transportation systems (C-ITS) to manage electrical bus systems. A simulation-based study is presented where three control strategies are used to regulate the operations of a line, namely bus holding, Green Light Optimal Dwell Time Adaptation (GLODTA) and Transit Signal Priority (TSP). The results show, using a realistic scenario of a major line in Luxembourg City, that buses are efficiently operated without necessarily providing additional priority to public transport, hence without negatively affecting the capacity of the private vehicles system. Benefits in terms of headway regulations, energy consumption and travel time variance reductions are quantified

Experimental analysis of eGLOSA and eGLODTA transit control strategies

Battery powered electric buses have higher energy efficiency, lower emissions and noise when compared to buses with internal combustion engines. However, due to battery charging requirements, their large-scale integration into public transport operations is more complex. This study proposes a novel concept supporting said integration via new control strategies, dubbed e-GLOSA and e-GLODTA. These strategies extend the existing Green Light Optimal Speed and Dwell Time Systems (GLOSA/GLODTA) to account for the specific needs of electric buses. That is, they include the goals of minimizing the energy consumption between charging stations, and maximizing available charging time. At the same time, interference with schedule requirements is minimized. The formulated heuristics are tested on a Bus Rapid Transit (BRT) corridor case study, where different scenarios—such as placement of charging stations and bus regularity—are studied to assess under which conditions each action (maintain speed, accelerate or dwell for a longer time at a stop) is beneficial. Results show that eGLOSA contributes to schedule adherence while eGLODTA allows satisfying charging time constraints