GreenPlanner: Planning personalized fuel-efficient driving routes using multi-sourced urban data

Greenhouse gas emission by the increasing number of vehicles have become a significant problem in modern cities. To save energy and protect environment, recommending fuel-efficient routes to drivers becomes a promising way to alleviate this issue. To this end, in this paper, we present a novel fuel-efficient path-planning framework called GreenPlanner, which contains two phases. In the first phase, we build a personalized fuel consumption model (PFCM) for each driver, based on the individual driving behaviors and the physical features (e.g., traffic lights, stop signs, road network topology) along the routes. In the second phase, with the real-time traffic information collected via the mobile crowdsensing manner, we are able to estimate and compare the cost fuel among different routes for a given driver, and recommend him/her with the most fuel-efficient one. We evaluate the two-phase framework using the real-world datasets, consisting of road network, POI, the GPS trajectory data and the OBD-II data generated by 559 taxis in one day in the city of Beijing, China. Experimental results demonstrate that, compared to the baseline models, the proposed model achieves the best accuracy, with a mean fuel consumption error of less 7% for paths longer than 10 km. Moreover, users could save about 20% fuel consumption on average if driving along our suggested routes in our case studies.