Analysis of GPS-based High Resolution Vehicle Mobility Data towards the Electrification of Transportation in Qatar

Vehicle mobility analysis is a critical input to make data-driven decisions to deploy electric vehicle (EV) charging infrastructures that are required for mass EV adoption. Traditionally, infrastructure planning is carried out by following manufacturer's specification on EV performance and using low-resolution data from national travel surveys. On the other hand, EV performance significantly degrades in countries like Qatar and other neighbouring Gulf States due to the hot desert climate. Moreover, the lack of public travel surveys require the creation of GPS-based mobility datasets to analyse spatio-temporal EV demand. To that end, this paper presents the first vehicle mobility dataset in the Gulf Cooperation Council (GCC) region by analysing data collected from seven vehicles (six petrol car and one EV) using telematics devices for nine months. The gathered data is processed using machine learning based clustering algorithms to reveal location analysis to examine daily activities, trip patterns, and impacts of weather on fuel efficiency. The results show that the EV driving experience is very risk-adverse due to reduced driving ranges in summer and lack of charging infrastructure. Also, home and workplace charging are well-suited for the recorded population, as the daily trip lengths are within the range of most EV models. We also report significant differences in fuel efficiency between summer and winter driving due to air-conditioning needs. The findings will shed light into GCC region's net-zero transition and decarbonise one of the most carbon-intensive economies of the world.