S1-4.2 - Deep Learning in Transportation (Data-driven and learning transportation)

In this work, we develop different scenarios regarding the initiatives and incentives to foster the use of electric vehicles (EVs) and test the different results on an optimal allocation tool for charging stations (ETCharger [1]) in The Hague city, the Netherlands. This is a sub-task from project ELECTRIC TRAVELLING [2]. Firstly, we overview the initiatives and incentives that have been implemented to foster the use of EVs in different regions in Europe [3] and analyzes the incentives that will be used in the implementation of the project to foster electromobility (e-mobility) in smart cities. Then the database of the city The Hague including existing transport network, and transport infrastructure parameters, especially the ones related to e-mobility etc., are introduced in order to apply the proposed tool to the case study. Based on the analysis focused on the incentives, we develop different scenarios of the transport system and use them to allocate charging stations. A comparison is done between different scenarios to select the optimal station locations according to the main goal of the project: foster increase the number of travellers by using EVs.

Air pollution and in particular PM2.5 emissions are a major problem worldwide. Road transport is a significant contributor to PM2.5 emissions in urban areas and as such it is important to understand and be able to accurately model the effects of vehicles on PM2.5 emissions. In this paper a computer vision algorithm is introduced which is able to extract vehicle trajectories from video footage. The algorithm has a 100% accuracy for overall total vehicle counting. Comparing the speeds predicted by the computer vision script to manually following a single vehicle feature on the video file, the average relative speed accuracy is 2.7% at a 1 Hz time resolution. Using these vehicle trajectories in an instantaneous vehicle emissions model and also as input to COPERT v5, tailpipe PM2.5 emissions were estimated and compared to on-road measurements. It was shown that a local sensor is not sufficient to determine vehicle tailpipe emissions due to the influence of meteorological conditions and other emission sources. Combining computer vision with an instantaneous vehicle emissions model is a useful method to evaluate changes in emissions caused by transport policies.