Which Is the Greenest Way Home? A Lightweight Eco-Route Recommendation Framework Based on Personal Driving Habits

A vehicle's fuel consumption is strongly related to both its loading and the driver's driving behavior, such as aggressive/tender acceleration, improper/proper gear change or running/stopping the engine while waiting. This paper introduces EasyRoute, an economical route recommendation system for modern vehicles, implemented in smartphones, to improve fuel efficiency. EasyRoute senses the vehicle's fuel consumption through the On-Board Diagnostics (OBD) adapter and then models the driver's personal fuel consumption according to OBD data from two aspects: i) when the vehicle is moving and ii) when the vehicle is idling or waiting. Based on the crowdsourced traffic information, EasyRoute can near-correctly predict total fuel consumptions of different routes and recommend drivers with the greenest route. We describe the EasyRoute framework and evaluate it by collecting OBD and GPS data from 559 taxis in Beijing. Comparing with some commonly used baselines with error metrics, the experimental results show that using a small 10-minute dataset for training, the total fuel consumption estimated by EasyRoute has a relative error of at least 30% less than the baselines.