A probabilistic approach for determining the influence of urban traffic management policies on energy consumption and greenhouse gas emissions from a battery electric vehicle

Drivers appear to be in control of all factors affecting vehicle performance. However, they are still subject to following traffic rules. These may be regulations like prohibitions or warnings, such as speed limits, or actions required by traffic control such as traffic lights and stop signs, that randomly influence the vehicle speed profile while driving. Driving behavior has an undeniable impact on vehicle greenhouse gas emissions, either from a tailpipe or in the form of indirect emissions resulting from charging batteries with electricity from the grid, and is a milestone for successful decarbonization of road transport. In this study, the authors propose a probabilistic approach to reaffirm and evaluate the influence of traffic management conditioning factors on driving behavior, energy consumption, and greenhouse gas emissions of a battery electric vehicle in a city. A Stochastic Route Speed Profile was developed based on the stochastic behavior of route elements affecting vehicle energy consumption. In this approach, the route rather than the driver, is analyzed for constraints and limitations that randomize energy consumption and air emissions. Human influence on driving behavior becomes irrelevant when route driving obligations are decisive. This probabilistic approach suggests an alternative view for understanding and evaluating the environmental impacts of traffic control decisions or urban planning regulations in terms of their effects on routes and their characteristics. A case study is presented for a route in Madrid, reflecting variations in vehicle energy consumption up to 70 Wh/km for a set of proposed scenarios with varying the number and performance of traffic lights. (C) 2019 Elsevier Ltd. All rights reserved.