Harnessing GPS Data for the Future of Electric Cars in Europe
Driving a Tesla may sound exotic, but electric cars are anything but a novel idea. The first successful electric automobile in the U.S. debuted in 1891. Over the last century, the development of electric vehicles (EVs) has been intimately tied to the oil market; in recent years, growing environmental concerns have ushered in a new era of EVs. If all light-duty vehicles in the U.S. were replaced by EVs, carbon pollution from transportation would decline by as much as 20 percent.
However, many questions need to be answered before embarking on such a daunting task. For instance, to what extent can EVs satisfy the transportation needs in the current market where gasoline vehicles still take up 99% of its share? How can automobile manufactures and city planners efficiently establish the charging infrastructure for EVs? The answers lie in big data.
In Europe, a group of researchers established the Transport Technology and Mobility Assessment (TEMA) platform, with the aim of “supporting EU transport policies assessment via big data.” Recently, these researchers published a study titled “Big Data for Supporting Low-Carbon Road Transport Policies in Europe,” which showcases how GPS data can justify and facilitate the adoption of EVs.
The researchers collected two large datasets from GPS devices on over 90,000 conventional fuel vehicles in the Italian provinces of Modena and Firenze. Information including time, coordinates, engine status, speed, and distance were recorded, reflecting driving and mobility patterns. Multiple filters were subsequently applied to narrow the sample’s focus to urban areas. 4.5 million trips were selected, with each trip lasting from an engine’s start to finish and sub-aggregated by day, week, and month.
Statistical analyses revealed that the majority of the time, more than 90 percent of the cars were parked. Moreover, trips were largely fragmented, especially during the day, averaging five kilometers and ten minutes. Seventy percent of urban vehicles never took a trip exceeding 100 kilometers. Therefore, with such short average distances, driving EVs would conserve battery life and provide ample opportunities for drivers to recharge, implying EVs’ compatibility with current market needs.
The authors explore the possible impact of replacing portions of the fleet with EVs while considering battery capacity, recharging constraints, and driver behavior. Their results suggested that 80 percent of current urban mobility could be served by small-to-medium sized EVs and about 20 percent of the fleet would never suffer any range limitation, i.e., trip failure due to dead battery.
According to the GPS data, spots with the highest energy demand were mapped out, and as one would expect, they clustered around the most urbanized areas. In order for automobile manufactures and city planners to determine the optimal locations of charging stations, each point of interest was evaluated based on a matching algorithm. Multiple factors were taken into account, including a Repetitiveness Index that described how likely a spot would attract recurring customers, and the vehicle-to-grid system (V2G), through which parked vehicles return electricity to the grid. It was shown that V2G could decrease peak demand by as much as 50 percent.
Utilizing new analytic techniques, GPS records offer useful insight into how to justify and facilitate the adoption of EVs. Government support is indispensable to the development of EVs, from transforming the automobile industry, to regulating greenhouse gas emissions, allocating R&D funding, designing smart city infrastructures, and invoking “polluter-pays” principles. This recent study epitomizes the power of big data, of which policy-makers should make use when addressing challenges embedded in the future of EVs and transportation.
Article source: De Gennaro, Michele, Elena Paffumi, and Giorgio Martini. “Big Data for Supporting Low-Carbon Road Transport Policies in Europe: Applications, Challenges and Opportunities.” Big Data Research 6 (2016): 11-25.
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