You Are How You Drive: Peer and Temporal-Aware Representation Learning for Driving Behavior Analysis


Driving is a complex activity that requires multi-level skilled operations (e.g., acceleration, braking, turning). Analyzing driving behavior can help us assess driver performances, improve traffic safety, and, ultimately, promote the development of intelligent and resilient transportation systems. While some efforts have been made for analyzing driving behavior, existing methods can be improved via representation learning by jointly exploring the peer and temporal dependencies of driving behavior. To that end, in this paper, we develop a Peer and Temporal-Aware Representation Learning based framework (PTARL) for driving behavior analysis with GPS trajectory data. Specifically, we first detect the driving operations and states of each driver from GPS traces. Then, we derive a sequence of multi-view driving state transition graphs from the driving state sequences, in order to characterize a driver's driving behavior that varies over time. In addition, we develop a peer and temporal-aware representation learning method to learn a sequence of time-varying yet relational vectorized representations from the driving state transition graphs. The proposed method can simultaneously model both the graph-graph peer dependency and the current-past temporal dependency in a unified optimization framework. Also, we provide effective solutions for the optimization problem. Moreover, we exploit the learned representations of driving behavior to score driving performances and detect dangerous regions. Finally, extensive experimental results with big trajectory data demonstrate the enhanced performance of the proposed method for driving behavior analysis.

Meeting Name

24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 (2018: Aug. 19-23, London, United Kingdom)


Computer Science

Research Center/Lab(s)

Intelligent Systems Center


This research was partially supported by the National Science Foundation (NSF) via the grant number: 1755946. This research was partially supported by the University of Missouri Research Board (UMRB) via the proposal number: 4991.

Keywords and Phrases

Automobile drivers; Data mining; Driver's driving behaviors; Driving behavior; Driving performance; Optimization problems; Representation Learning; Spatio-temporal graphs; Transportation system; Unified optimization framework; Digital storage; Driving Behavior Analysis

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


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© 2018 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

01 Aug 2018