Improved Driver Clustering Framework by Considering the Variability of Driving Behaviors across Traffic Operation Conditions

Zhang, Jianbo; Lu, Hongyu; Sun, Jian

doi:10.1061/jtepbs.0000686

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…Wang et al have especially pointed out that it is difficult to find an effective method to explain the deep correlation between driving style and its corresponding quantitative risk value and have tried to propose a driving risk assessment method based on fuzzy multi-criteria decision-making (MCDM) suitable for PHYD vehicle insurance premium actuary and have verified the effectiveness and reliability of this method through examples [109]. Only during the past year or two have the scholars tried to employ specific driving parameters to analyze the risk of different driving styles with the help of clustering algorithms based on AI algorithms, Zhang et al and Sun et al have used 3 key parameters related to speed and 2 key parameters related to vehicle interval respectively to effectively realize driving style clustering and to distinguish aggressive drivers and non-aggressive drivers [110,111].…”

Section: Research On Differentiated Macro Driving Stylementioning

confidence: 95%

A Comprehensive Review of Driving Style Evaluation Approaches and Product Designs Applied to Vehicle Usage-Based Insurance

Nai¹,

Yang²,

Wei³

et al. 2022

Sustainability

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Vehicle insurance is a very important source of income for insurance companies, and it is closely related to the driving style performed by driving behavior. Different driving styles can better reflect the driving risk than the number of violations, claims, and other static statistic data. Subdivide the vehicle insurance market according to the personal characteristics and driving habits of the insured vehicles, and studying the personalized vehicle insurance products, will help the insurance companies to improve their income, help the drivers to change their bad driving habits, and thus help to realize the healthy development of the vehicle insurance industry. In the past 20 to 30 years, more and more insurance companies around the world have launched vehicle usage-based insurance (UBI) products based on driving style analysis. However, up to now, there are few comprehensive reports on commercial vehicle UBI products and their core driving risk assessment methods. On the basis of literature indexing on the Web of Science and other academic platforms by using the keywords involved in vehicle UBI, over 100 relevant works of literature were screened in this paper, and a detailed and comprehensive discussion on the driving style evaluation methods and the design of commercial vehicle UBI products during the past 20 to 30 years has been made, hoping to get a full understanding of the possible factors affecting driving style and the collectible data that can reflect these factors, and to get a full grasp of the developing status, challenges and future trends in vehicle insurance branch of the Internet of Vehicles (IoV) industry.

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Section: Research On Differentiated Macro Driving Stylementioning

confidence: 95%

A Comprehensive Review of Driving Style Evaluation Approaches and Product Designs Applied to Vehicle Usage-Based Insurance

Nai¹,

Yang²,

Wei³

et al. 2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…The majority of the existing literature suggests that the speed ( 27, 30–33, 52 ), acceleration–deceleration ( 27, 31–33, 35, 52, 53 ), and position change rate of the SV ( 31 ) are the influential factors for the detection of driving behavior of a vehicle. To classify driving style during the lateral shift process, the speed change of the subject MTW rider, change in angular position, longitudinal gap maintenance with the rear vehicle, and lateral gap utilization by the subject rider are considered as the predictors of driving style in this work, a detailed discussion of which is provided in the following sections.…”

Section: Driving Style Assessment Of Motorized Two-wheeler Ridersmentioning

confidence: 99%

“…As labeling driving style becomes challenging because of its latent nature, most of the existing studies have performed cluster-based unsupervised classification methodology to classify it either in a three-class ( 27 – 29 ) or in a multi-class system ( 30 – 34 ). Zhang et al ( 35 ) and Chen and Chen ( 27 ) defined a three-clustered model, naming the driving style groups as prudent, normal, and aggressive drivers. However, certain studies used multi-group-based clustering approaches (more than three groups) as a classification methodology for understanding vehicle driving style with cluster variations of 4 ( 30 , 36 ) and 6 ( 31 ).…”

mentioning

confidence: 99%

Model for Predicting Lateral Shifts and Driving Style of Motorized Two-Wheelers

Dey,

Das

2023

Transportation Research Record: Journal of the Transportation R

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The compact sizes, lack of available physical protection, and complex maneuvering patterns of motorized two-wheeler (MTW) riders make them more vulnerable to crash risks and accidents. Considering the increased vulnerability of MTWs in dense urban mixed traffic environments, a proper evaluation and modeling of their lateral movement decisions and driving style can enhance safety associated with the riders and augment the reliability of the existing microsimulation models. Utilizing trajectory data of a six-lane divided urban arterial, the current study attempts to investigate the lateral movement (lateral shift) tendency of MTWs, addressing different influential variables that may affect riders’ decisions and describing how this choice is affected by prevailing traffic conditions. Based on several driving style parameters, such as change in speed, change in angular position, and longitudinal gap maintained with rear vehicles, this study further contributes to the existing literature by proposing a new index for identifying the driving style of riders during the lateral shifting process. Modeling results of a multinomial logit model indicated the importance of considering longitudinal and lateral gaps, vehicle speeds, and lateral fluctuations made by the subject MTW in the past trajectories in modeling the lateral shift decisions of riders. Considering the number of lateral fluctuations as an indicator of MTWs’ driving style, a new “aggressiveness index” is defined and, accordingly, a modeling approach is proposed to classify aggressive and non-aggressive driving styles of the riders. The results suggest that the history of past trajectories of the subject MTW during lateral shifting should be considered, and consideration of a non-linear relationship among the parameters can result in a better classification of driving style of MTW riders.

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“…Te clustering results were compared with those without considering trafc conditions. Te improved clustering framework performs better in both intraclass aggregation and interclass separation [19]. Yang et al constructed a multidimensional multilevel system for trafc crash analysis, this system was capable of accurately and efciently capturing the mechanics of high-consequence (and possibly low support) highway crashes [20].…”

Section: Introductionmentioning

confidence: 99%

Study on Driver Behavior Pattern in Merging Area under Naturalistic Driving Conditions

Li,

Zhang,

Wang

et al. 2024

Journal of Advanced Transportation

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To reduce the risk of traffic conflicts in merging area, driver’s behavior pattern was analyzed to provide a theoretical basis for traffic control and conflict risk warning. The unmanned aerial vehicle (UAV) was used to collect the videos in two different types of merging zones: freeway interchange and service area. A vehicle tracking detection model based on YOLOv5 (the fifth version of You Only Look Once) and Deep SORT was constructed to extract traffic flow, speed, vehicle type, and driving trajectory. Acceleration/deceleration distribution and vehicle lane-changing behavior were analyzed. The influence of different vehicle models on vehicle speed and lane-changing behavior was summarized. Based on this data, the mean and standard deviation of velocity, acceleration, and variable acceleration were selected as the characteristic variables for driving style clustering. To avoid redundant information between features, principal component dimensionality reduction was performed, and the dimensionality reduction data was used for K-means and K-means++ clustering to obtain three driving styles. The results show that there are obvious differences in the driving behaviors of vehicles in different types of merging areas, and the characteristics of different areas should be fully considered when conducting traffic conflict warnings.

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Improved Driver Clustering Framework by Considering the Variability of Driving Behaviors across Traffic Operation Conditions

Cited by 4 publications

References 18 publications

A Comprehensive Review of Driving Style Evaluation Approaches and Product Designs Applied to Vehicle Usage-Based Insurance

A Comprehensive Review of Driving Style Evaluation Approaches and Product Designs Applied to Vehicle Usage-Based Insurance

Model for Predicting Lateral Shifts and Driving Style of Motorized Two-Wheelers

Study on Driver Behavior Pattern in Merging Area under Naturalistic Driving Conditions

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