Transportation Research Part F: Traffic Psychology and Behaviour
A framework for evaluating aggressive driving behaviors based on in-vehicle driving records
Introduction
Driving behavior, which is reflected in how drivers control speed and steer their vehicles, is a major interest in traffic safety research. Driving behavior widely varies across individuals; differences of driving behavior have significant effects on roadway traffic. Especially, aggressive driving behavior has been reported as the main cause of traffic safety problems (NHTSA, 1998). In support of this, there have been many studies that have revealed the causal relation between frequency of aggressive driving behavior and crash occurrence (Boyce and Geller, 2002, Evans, 2004, Klauer et al., 2009). To enhance traffic safety, therefore, it is effective to identify aggressive driving behaviors and provide drivers with proper feedback to adjust their driving (Toledo, Musicant, & Lotan, 2008).
Earlier studies about aggressive driving behavior have been separated largely into two groups: (i) finding influential factors on aggressive driving occurrences (Evans and Wasielewski, 1983, Cooper, 1990, Gregersen and Berg, 1994, Shinar, 1998, Shinar and Compton, 2004) and (ii) identifying and defining the categories of aggressive driving behavior (Tasca, 2000, AAA Foundation for Traffic Safety, 2009, Abou-Zeid et al., 2011). The essential part of driving behavior analysis is driving data collection. However, the driving data used in these studies were collected from questionnaires, site investigations, or laboratory simulations, which may differ from real driving environments and result in biased outcomes.
Recent advance in in-vehicle sensors allow a real driving data collection by continuously recording driving behavior during whole trips. The collected data include vehicle’s operating conditions such as speed, trip distance, brake signal and vehicle locations. Since the inception of this so-called driving recorder, they were first installed in commercial vehicles that are generally exposed to higher risk of crash occurrences and severe injuries as they are driven longer distances and carry greater weights than other vehicles. Hence, there have been some efforts to give feedback to drivers about driving behavior by collecting and analyzing their driving records (Misener et al., 2008, Toledo et al., 2008, Wouters and Bos, 2000). In addition to these studies, some recent studies have attempted to characterize various types of aggressive driving behaviors and to develop their criteria (Oh et al., 2009, Murphey et al., 2009, Li et al., 2014, Carboni and Bogorny, 2015). However, the criteria developed in previous studies were based on simple threshold values of recorders or often developed with lack of quantitative evidence although driving behavior should be viewed in multidimensional perspectives. This could cause false-positive or true-negative detections. To systematically identify aggressive driving behaviors, there are needs for developing more comprehensive criteria that can reflect multidimensional aspects of driving behavior.
This paper aims to develop a framework to evaluate in-vehicle driving record data and, thus, to identify driving behaviors with high potential to indicate aggressive driving. The developed framework is based on abrupt change detection and a two-level clustering approach. In the following section, the procedure to identify abrupt changes in driving and to cluster driving events is described (Section 2). This procedure is then applied to real in-vehicle driving record data that were collected from taxis in Korean metropolitan cities (Section 3). The clusters established in Section 3 are used to diagnose other drivers’ driving records and, consequently, to provide information on potential aggressive driving that may lead to risk of crashes (Section 4). The findings and implications are discussed in Section 5.
Section snippets
Framework for clustering driving behaviors
This study applied a sequential procedure for clustering driving record data to identify aggressive driving behaviors. The procedure is composed of three steps – (i) abrupt change detection; (ii) feature extraction; and (iii) two-level clustering. In-vehicle driving recorder was designed to record driving conditions continuously over time. Thus, driving record data included information from the time periods when driving characteristics were not particularly pronounced. Driving characteristics
Data description
In Korea, it was legislated in 2011 that all commercial vehicles (e.g. trucks, buses and taxis) must install a digital tachograph (DTG), which is a type of in-vehicle driving recorder, for monitoring safety (Traffic Safety Act Article 55 in Korea). The DTG device imports driving records from On-Board Diagnostic Systems (OBD-II) terminal and stores them in the Secure Digital (SD) memory card. The stored data in the memory are periodically downloaded from the memory and transmitted to the server
Driving event detection
Event time periods that have high potential for the observation of aggressive driving behaviors were identified using ACD. The change point is identified when the change point score becomes greater than a threshold value. In this study, the threshold was set at 500, which was the top 5% of all scores. In computing this score, the size of the time window was set at 15 s because the time needed to complete a single driving event is 15 s (Zhang, Zhao, & Rong, 2014). Then, the change point score
Diagnosis of driving behaviors using the established clusters
The cluster map, identified as an outcome of the process, represents the norm of driving events by taxi drivers. Thus, the map can be used as a reference to evaluate how a driver behavior deviate from the norm. In this section, we obtained driving records from four different taxi drivers and diagnosed their driving patterns based on the cluster map. Driving records of the four taxi drivers were filtered using ACD and driving events with abrupt changes were identified. Then, the events were
Conclusions
This paper has developed a framework to evaluate driving behaviors using in-vehicle driving record data. Data of this kind are becoming more available as in-vehicle sensor technologies advance and spread widely. The framework is composed of abrupt change detection, which extracts driving events with sudden changes in driving behaviors, and clustering, which categorizes driving events using an unsupervised learning approach. The case study applying the framework to real data from taxis shows
Acknowledgements
This research was supported by the National Research Foundation of Korea grant funded by the Korea government (MSIP) (NRF-2014R1A2A1A11052725).
References (29)
- et al.
An instrumented vehicle assessment of problem behavior and driving style: Do younger males really take more risks?
Accident Analysis & Prevention
(2002) - et al.
Review of the self-organizing map (SOM) approach in water resources: commentary
Environmental Modelling & Software
(2009) Differences in accident characteristics among elderly drivers and between elderly and middle-aged drivers
Accident Analysis & Prevention
(1990)- et al.
Risky driving related to driver and vehicle characteristics
Accident Analysis & Prevention
(1983) - et al.
Lifestyle and accidents among young drivers
Accident Analysis & Prevention
(1994) - et al.
A review of unsupervised feature learning and deep learning for time-series modeling
Pattern Recognition Letters
(2014) - et al.
Change-point detection in time-series data by relative density-ratio estimation
Neural Networks
(2013) Aggressive driving: the contribution of the drivers and the situation
Transportation Research Part F: Traffic Psychology and Behaviour
(1998)- et al.
Aggressive driving: An observational study of driver, vehicle, and situational variables
Accident Analysis & Prevention
(2004) - et al.
In-vehicle data recorders for monitoring and feedback on drivers’ behavior
Transportation Research Part C: Emerging Technologies
(2008)
SOM-based data visualization methods
Intelligent Data Analysis
Traffic accident reduction by monitoring driver behaviour with in-car data recorders
Accident Analysis & Prevention
A study of individual characteristics of driving behavior based on hidden markov model
Sensors & Transducers
Aggressive driving: Research update
Cited by (43)
Driving risk prevention in usage-based insurance services based on interpretable machine learning and telematics data
2023, Decision Support SystemsAnalysis of driver characteristics, self-reported psychology measures and driving performance measures associated with aggressive driving
2023, Accident Analysis and PreventionOptimized structure learning of Bayesian Network for investigating causation of vehicles’ on-road crashes
2022, Reliability Engineering and System SafetyCitation Excerpt :Thus, investigating the causal relationships between the contributing factors and vehicles’ crashes is of importance towards the remediation of crashes and the enhancement of road transportation reliability. Previous studies have focused on the contributing factors from various aspects in recent years, such as vehicles’ kinetic characteristics (e.g. position, velocity, etc., extracted from trajectory) [15,55,69,72], driving behaviors (e.g. improper driving maneuver, aggressive driving, etc.) [4,44,71], surrounding environment (e.g., weather, road condition, etc.) [29,48,75,82], human factors (e.g. physiology, psychology, social background, etc.) [3,5,25,26], transportation infrastructure [8],[83], etc. Since a vehicles’ crash can be regarded as a systematic failure which usually results from the effect of multiple factors rather than only a single mistake [62], many researchers have attempted to synthesize the factors from multiple aspects when investigating a vehicles’ crash [12,28,79].
Benchmarking the driver acceleration impact on vehicle energy consumption and CO<inf>2</inf> emissions
2022, Transportation Research Part D: Transport and EnvironmentCitation Excerpt :Finally, Section 6 draws general conclusions and thoughts for future work. The characterization of the driving style has been tackled in the literature through the analysis of different experimentally acquired features (Li et al., 2017) (Van Ly et al., 2013) (Tawfeek and El-Basyouny, 2019) (Zhao et al., 2020) (Yan et al., 2019) (Lee and Jang, 2019) (Feng et al., 2017) (Eboli et al., 2016). A complete review of literature on the topic can be found in (Makridis et al., 2022) and references therein.
Modeling aggressive driving behavior based on graph construction
2022, Transportation Research Part C: Emerging TechnologiesCitation Excerpt :On the other hand, many researchers have attempted to model aggressive driving behavior from varied methods (Hong, Dong, Zheng, & Wei, 2011). For example, Lee and Jang (2019) developed a framework to evaluate large-scale driving records and to establish clusters to identify potentially aggressive driving behaviors. Some of those research always use instantaneous, mean, and Standard deviation (S.D.) as model inputs (Songchitruksa & Tarko, 2006; Davis, 2008; Saunier & Sayed, 2008; Kluger, Smith, Park, & Dailey, 2016).