The Analysis of Classification and Spatiotemporal Distribution Characteristics of Ride-Hailing Driver’s Driving Style: A Case Study in China

Monitoring the driving styles of ride-hailing drivers is helpful for providing targeted training for drivers and improving the safety of the service. However, previous studies have lacked analyses of the temporal variation as well as spatial variation characteristics of driving styles. Understanding the variations can also help authorities formulate driver management policies. In this study, trajectory data are used to analyze driving styles in various temporal and spatial scenarios involving 34,167 drivers. The k-means method is used to cluster sample drivers. In terms of driving style time-varying, we found that only 31.79% of drivers could maintain a stable driving style throughout the day. Spatially, we divided the research area into two parts, namely, road segments and intersections, to analyze the spatial driving characteristics of drivers with different styles. The speed distribution, the acceleration and deceleration distributions are analyzed, results indicated that aggressive drivers display more aggressive driving styles in road segments, and conservative drivers exhibit more conservative driving styles at intersections. The findings of this study provide an understanding of temporal and spatial driving behavior factors for ride-hailing drivers and offer valuable contributions to ride-hailing driver training and road safety management.

[1]  Longyu Shi,et al.  Risks assessment and driving forces of urban environmental accident , 2022, Journal of Cleaner Production.

[2]  Shuyan Chen,et al.  Driving style recognition and comparisons among driving tasks based on driver behavior in the online car-hailing industry. , 2021, Accident; analysis and prevention.

[3]  R. A. Acheampong.  Societal impacts of smart, digital platform mobility services—an empirical study and policy implications of passenger safety and security in ride-hailing , 2021, Case Studies on Transport Policy.

[4]  Jingfeng Xu,et al.  Influence of coloured light projected from night-time excessive luminance outdoor LED display screens on vehicle driving safety along urban roads , 2021 .

[5]  Si Qiao,et al.  Effects of state-led suburbanization on traffic crash density in China: Evidence from the Chengdu City Proper. , 2020, Accident; analysis and prevention.

[6]  R. Borgo,et al.  Sharing the air: Transient impacts of ride-hailing introduction on pollution in China , 2020 .

[7]  Guangyin Jin,et al.  Urban ride-hailing demand prediction with multiple spatio-temporal information fusion network , 2020, Transportation Research Part C: Emerging Technologies.

[8]  Huiying Mao,et al.  Driving safety assessment for ride-hailing drivers. , 2020, Accident; analysis and prevention.

[9]  Mahmood Fathy,et al.  Driver behavior detection and classification using deep convolutional neural networks , 2020, Expert Syst. Appl..

[10]  C. Kloeden,et al.  Impact speed and the risk of serious injury in vehicle crashes. , 2020, Accident; analysis and prevention.

[11]  Rui Jiang,et al.  Trajectory data-based traffic flow studies: A revisit , 2020 .

[12]  J. Rong,et al.  Modeling of individual vehicle safety and fuel consumption under comprehensive external conditions , 2020 .

[13]  Tuqiang Zhou,et al.  Analysis of commercial truck drivers' potentially dangerous driving behaviors based on 11-month digital tachograph data and multilevel modeling approach. , 2019, Accident; analysis and prevention.

[14]  Asad J Khattak,et al.  The role of pre-crash driving instability in contributing to crash intensity using naturalistic driving data. , 2019, Accident; analysis and prevention.

[15]  R. J. MacKay,et al.  Using telematics data to find risky driver behaviour. , 2019, Accident; analysis and prevention.

[16]  Wen-Hua Chen,et al.  A machine learning based personalized system for driving state recognition , 2019, Transportation Research Part C: Emerging Technologies.

[17]  Xiaohua Zhao,et al.  Effects of connected vehicle-based variable speed limit under different foggy conditions based on simulated driving. , 2019, Accident; analysis and prevention.

[18]  P. Mokhtarian,et al.  What drives the use of ridehailing in California? Ordered probit models of the usage frequency of Uber and Lyft , 2019, Transportation Research Part C: Emerging Technologies.

[19]  Chen Chen,et al.  A graphical modeling method for individual driving behavior and its application in driving safety analysis using GPS data , 2019, Transportation Research Part F: Traffic Psychology and Behaviour.

[20]  D. Alfred Owens,et al.  Drivers’ assessments of the risks of distraction, poor visibility at night, and safety-related behaviors of themselves and other drivers , 2019, Transportation Research Part F: Traffic Psychology and Behaviour.

[21]  Ziyuan Pu,et al.  Characterization of ridesplitting based on observed data: A case study of Chengdu, China , 2019, Transportation Research Part C: Emerging Technologies.

[22]  Jie Sun,et al.  A car-following model considering asymmetric driving behavior based on long short-term memory neural networks , 2018, Transportation Research Part C: Emerging Technologies.

[23]  Julian Eggert,et al.  Intersection Warning System for Occlusion Risks Using Relational Local Dynamic Maps , 2018, IEEE Intelligent Transportation Systems Magazine.

[24]  Yizhe Huang,et al.  Effects of congestion on drivers' speed choice: Assessing the mediating role of state aggressiveness based on taxi floating car data. , 2018, Accident; analysis and prevention.

[25]  Ruimin Li,et al.  Studying the benefits of carpooling in an urban area using automatic vehicle identification data , 2018, Transportation Research Part C: Emerging Technologies.

[26]  Nicolas Saunier,et al.  Vehicle manoeuvers as surrogate safety measures: Extracting data from the gps-enabled smartphones of regular drivers. , 2018, Accident; analysis and prevention.

[27]  Carl Pankok,et al.  The effect of navigation display clutter on performance and attention allocation in presentation- and simulator-based driving experiments. , 2018, Applied ergonomics.

[28]  Li Li,et al.  Risky Driver Recognition Based on Vehicle Speed Time Series , 2018, IEEE Transactions on Human-Machine Systems.

[29]  Rui Ma,et al.  Driving behavior recognition using EEG data from a simulated car-following experiment. , 2017, Accident; analysis and prevention.

[30]  J. Weller,et al.  Who is a Distracted Driver? Associations between Mobile Phone Use while Driving, Domain‐Specific Risk Taking, and Personality , 2017, Risk analysis : an official publication of the Society for Risk Analysis.

[31]  Tom Brijs,et al.  Inhibitory Control and Reward Predict Risky Driving in Young Novice Drivers: A Simulator Study , 2017 .

[32]  Lin Li,et al.  Driving Style Classification Using a Semisupervised Support Vector Machine , 2017, IEEE Transactions on Human-Machine Systems.

[33]  Xiqun Chen,et al.  Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach , 2017 .

[34]  Xingda Qu,et al.  The role of personality traits and driving experience in self-reported risky driving behaviors and accident risk among Chinese drivers. , 2017, Accident; analysis and prevention.

[35]  Mark Vollrath,et al.  The influence of attention allocation and age on intersection accidents , 2016 .

[36]  Chao Yang,et al.  Multimode Energy Management for Plug-In Hybrid Electric Buses Based on Driving Cycles Prediction , 2016, IEEE Transactions on Intelligent Transportation Systems.

[37]  Juan de Oña,et al.  Socio-economic and driving experience factors affecting drivers’ perceptions of traffic crash risk , 2016 .

[38]  Yvonne Barnard,et al.  How I reduce fuel consumption: An experimental study on mental models of eco-driving , 2015 .

[39]  Fridulv Sagberg,et al.  A Review of Research on Driving Styles and Road Safety , 2015, Hum. Factors.

[40]  Yu Zheng,et al.  Trajectory Data Mining , 2015, ACM Trans. Intell. Syst. Technol..

[41]  Adrian B. Ellison,et al.  Driver behaviour profiles for road safety analysis. , 2015, Accident; analysis and prevention.

[42]  Paulius Lengvenis,et al.  Driving style classification using long-term accelerometer information , 2014, 2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR).

[43]  Lei Zhao,et al.  Effect of driving experience on collision avoidance braking: an experimental investigation and computational modelling , 2014, Behav. Inf. Technol..

[44]  Weina Qu,et al.  The Dula dangerous driving index in China: an investigation of reliability and validity. , 2014, Accident; analysis and prevention.

[45]  Javier E. Meseguer,et al.  DrivingStyles: A smartphone application to assess driver behavior , 2013, 2013 IEEE Symposium on Computers and Communications (ISCC).

[46]  Ram Dantu,et al.  Safe Driving Using Mobile Phones , 2012, IEEE Transactions on Intelligent Transportation Systems.

[47]  Omar Bagdadi,et al.  Jerky driving--An indicator of accident proneness? , 2011, Accident; analysis and prevention.

[48]  Monica Vladoiu,et al.  Driving Style Analysis Using Data Mining Techniques , 2010, Int. J. Comput. Commun. Control.

[49]  Jianqiang Wang,et al.  A quantification method of driver characteristics based on Driver Behavior Questionnaire , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[50]  Yi Lu Murphey,et al.  Driver's style classification using jerk analysis , 2009, 2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems.

[51]  Hwasoo Yeo,et al.  Asymmetric Microscopic Driving Behavior Theory , 2008 .

[52]  H. M. Zhang.  A mathematical theory of traffic hysteresis , 1999 .

[53]  T. Lajunen,et al.  Driving experience, personality, and skill and safety-motive dimensions in drivers' self-assessments , 1995 .

[54]  Asad J. Khattak,et al.  Classifying travelers' driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning , 2021 .

[55]  Evgenia Suzdaleva,et al.  An online estimation of driving style using data-dependent pointer model , 2018 .

[56]  Mohamed Shawky,et al.  Investigation of drivers' behavior towards speeds using crash data and self-reported questionnaire. , 2017, Accident; analysis and prevention.

[57]  Xiaohua Zhao,et al.  Drunk driving detection based on classification of multivariate time series. , 2015, Journal of safety research.

[58]  Hoong Chor Chin,et al.  Severity of driver injury and vehicle damage in traffic crashes at intersections: a Bayesian hierarchical analysis. , 2008, Accident; analysis and prevention.

[59]  E. Petridou,et al.  Human factors in the causation of road traffic crashes , 2004, European Journal of Epidemiology.