Driving Behavior Identification and Real-World Fuel Consumption Estimation With Crowdsensing Data

Abstract

It is now well established that car driving behavior impacts gas pollutant emission, fuel consumption and safety. With the rise of low-cost and easily accessible mobility data (measured by GPS, accelerometer, gyroscope, etc), through OBD or smartphones devices for instance, the community is increasingly interested in characterizing these driving behaviors. In this context, we used the IFPEN's application named GECO AIR to access GPS data on which we derived statistical descriptors to perform unsupervised classifications and highlight different trip- and driver-related behaviors (dynamic, slow, traffic jam, etc.), on different road types (urban, highway, etc). Using Markov chain, we then generate for each characterized behavior a representative velocity profile and combine them according to a given use to estimate the associated real-world fuel consumption. Promising performances have been obtained with more than half of the recorded trips for which the real-world fuel consumption is estimated with less than 10% of error. The added value of the proposed work is the capability to estimate fuel consumption in a posterior way without any GPS records, as information obtained through a questionnaire for instance could be sufficient.