A personalized route search method based on joint driving and vehicular behavior recognition

Abstract

Fuel consumption is an important factor in the route search for vehicular navigations. In this paper, we propose a personalized eco-friendly route search method that considers the driver's driving style, road traffic, geographic information and vehicular parameters. Firstly, we classify the driving styles into three categories (calm, normal and aggressive) by adopting a Learning Vector Quantization (LVQ) neural network with inputs based on 16 characteristics related to vehicle speed, acceleration and engine speed. Secondly, we design a roadway traffic estimation model based on functional similarity and congestion propagation characteristics. Thirdly, we propose a model for fuel consumption estimation (FCE) based on multivariate nonlinear regression to accomplish the eco-friendly route search. To evaluate our route search method, we conducted experiments using real-world vehicle data gathered in the city of Beijing. Our experimental results show that the proposed route search method can achieve a driving-style prediction accuracy of 82.17%, and can reduce the fuel consumption by 16% as compared to the time-priority routes.