Network models of driver behavior

PeerJ

article

Markus T. Mattsson¹ 

[1] Department of Psychology and Logopedics, University of Helsinki;Traffic Research Unit, University of Helsinki

关键词: Driver behavior;    Network psychometrics;    Criterion-keyed scale construction;    Accident prediction;    Traffic psychology;   

DOI  :  10.7717/peerj.6119

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

The way people behave in traffic is not always optimal from the road safety perspective: drivers exceed speed limits, misjudge speeds or distances, tailgate other road users or fail to perceive them. Such behaviors are commonly investigated using self-report-based latent variable models, and conceptualized as reflections of violation- and error-proneness. However, attributing dangerous behavior to stable properties of individuals may not be the optimal way of improving traffic safety, whereas investigating direct relationships between traffic behaviors offers a fruitful way forward. Network models of driver behavior and background factors influencing behavior were constructed using a large UK sample of novice drivers. The models show how individual violations, such as speeding, are related to and may contribute to individual errors such as tailgating and braking to avoid an accident. In addition, a network model of the background factors and driver behaviors was constructed. Finally, a model predicting crashes based on prior behavior was built and tested in separate datasets. This contribution helps to bridge a gap between experimental/theoretical studies and self-report-based studies in traffic research: the former have recognized the importance of focusing on relationships between individual driver behaviors, while network analysis offers a way to do so for self-report studies.

【 授权许可】

CC BY