Driving Behaviour Style Study with a Hybrid Deep Learning Framework Based on GPS Data

Jingqiu Guo,Lanfang Zhang,Yibing Wang,Yangzexi Liu

doi:10.3390/su10072351

Jingqiu Guo, Lanfang Zhang + Show 2 more

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https://doi.org/10.3390/su10072351

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Journal: Sustainability	Publication Date: Jul 6, 2018
Citations: 32	License type: CC BY 4.0

Affiliation: Tongji University, Zhejiang University

Abstract

Innovative technologies and traffic data sources provide great potential to extend advanced strategies and methods in travel behaviour research. Considering the increasing availability of real-time vehicle trajectory data and stimulated by the advances in the modelling and analysis of big data, this paper developed a hybrid unsupervised deep learning model to study driving bahaviour and risk patterns. The approach combines Autoencoder and Self-organized Maps (AESOM), to extract latent features and classify driving behaviour. The specialized neural networks are applied to data from 4032 observations collected from Global Positioning System (GPS) sensors in Shenzhen, China. In two case studies, improper vehicle lateral position maintenance, speeding and inconsistent or excessive acceleration and deceleration have been identified. The experiments have shown that back propagation through multi-layer autoencoders is effective for non-linear and multi-modal dimensionality reduction, giving low reconstruction errors from big GPS datasets.

Highlights

Road accidents impose serious problems on society in terms of human, economic, medical and environmental costs
Motivated by the success of deep neural networks and considering the time and space characteristics of Global Positioning System (GPS) data, we propose an unsupervised deep learning architecture to learn drivers’ behaviour patterns from GPS data
We demonstrate the application of AESOM networks in a case study using a large-scale GPS dataset

Summary

Introduction

Road accidents impose serious problems on society in terms of human, economic, medical and environmental costs. To understand the various factors associated with fatal and non-fatal road accidents is very crucial [2,3]. Intensive efforts have been made to understand human driving styles and the classification of drivers’ risk patterns [4,5]. The relationship between the sensitivity of the driver to complex driving situations and the vehicle control has been acknowledged as a major contributing factor in accidents [6]. Driving patterns and their influence on environment and fuel-use were well studied [7]. Characterizing driving behaviour can be helpful for the development of vehicle automation [9,10]

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