Electric vehicles are gaining importance and help to reduce dependency on oil, increase energy efficiency of transportation, reduce carbon emissions and noise, and avoid tail pipe emissions. Because of short driving distances, high mileages, and intermediate waiting times, fossil-fuelled taxi vehicles are ideal candidates for being replaced by battery electric vehicles (BEVs). Moreover, taxis as BEVs would increase visibility of electric mobility and therefore encourage others to purchase an electric vehicle. Prior to replacing conventional taxis with BEVs, a suitable charging infrastructure has to be established. This infrastructure, which is a prerequisite for the use of BEVs in practice, consists of a sufficiently dense network of charging stations taking into account the lower driving ranges of BEVs. In this case study we propose a decision support system for placing charging stations to satisfy the charging demand of electric taxi vehicles. Operational taxi data from about 800 vehicles is used to identify and estimate the charging demand for electric taxis based on frequent origins and destinations of trips. Next, a variant of the maximal covering location problem is formulated and solved, aiming at satisfying as much charging demand as possible with a limited number of charging stations. Already existing fast charging locations are considered in the optimization problem. In this work, we focus on finding regions in which charging stations should be placed, rather than exact locations. The exact location within an area is identified in a post-optimization phase (e.g., by authorities), where environmental conditions are considered, e.g., the capacity of the power network, availability of space, and legal issues. Our approach is implemented in the city of Vienna, Austria, in the course of an applied research project conducted in 2014. Local authorities, power network operators, representatives of taxi driver guilds as well as a radio taxi provider participated in the project and identified exact locations for charging stations based on our decision support system.

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In the modeling of signalized intersections, one parameter is of crucial importance: the saturation flow rate. This value defines the number of vehicles that pass an intersection within 1 h of effective green time per lane. In this study, changes in the saturation flow were investigated under adverse weather conditions, such as precipitation or snow that covered the road surface. Data were obtained from video recordings, and a timestamp was recorded for each vehicle as its rear axle crossed the intersection. Subsequently, all observations were aggregated to a longer time interval. These measurements were then used to train a model by minimizing the squared error between model output and observation. The advantages of the model were the incorporation of various vehicle classes and the consideration of driving behavior at the beginning and the end of the green phase (start and end lags). These parameters were investigated under various weather conditions and showed that the saturation flow rate was significantly influenced by snow on the road surface. To improve traffic models, it is thus important to consider the dependence of the saturation flow rate on the weather. To adjust the saturation flow, adjustments in certain other parameters influenced by prevailing weather conditions were investigated in a microscopic traffic simulation.

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