Using computer vision and machine learning to identify bus safety risk factors. (June 2023)

• Record Type: Journal Article
• Authors: Loo, Becky P.Y.; Fan, Zhuangyuan; Lian, Ting; Zhang, Feiyang
• Is Part Of: Accident analysis and prevention. Volume 185(2023)
• Publication Date: 2023-06
• Subjects: Bus safety -- Pedestrian behaviour -- Video analytics -- Crash modeling; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2023.107017
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Use deep learning-based computer vision methods to extract street and behavioural factors from bus dashcam videos across the city. Incorporate human-scale high-resolution urban design elements into the bus crash analysis. Risk factors rank differently among serious bus crashes, slight bus crashes, and non-bus crashes. Pedestrian exposure factor is important in predicting crash frequencies at different severity levels. Protection railing is highly important in predicting fatal and serious bus crashes. Bus stop crowding is significant for predicting slight bus crashes. Abstract: In road safety research, bus crashes are particularly noteworthy because of the large number of bus passengers involved and the challenge that it puts to the road network (with the closure of multiple lanes or entire roads for hours) and the public health care system (with multiple injuries that need to be dispatched to public hospitals within a short time). The significance of improving bus safety is high in cities heavily relying on buses as a major means of public transport. The recent paradigm shifts of road design from primarily vehicle-oriented to people-oriented urge us to examine street and pedestrian behavioural factors more closely. Notably, the street environment is highly dynamic, corresponding to different times of the day. To fill this research gap, this study leverages a rich dataset - video data from bus dashcam footage - to identify some high-risk factors for estimating the frequency of bus crashes. This research applies deep learning models and computer vision techniques and constructs a series of behavioural and street factors: pedestrian exposure factors, pedestrian jaywalking, bus stop crowding, sidewalk railing, and sharp turning locations. Important risk factors are identified, and future planning interventions are suggested. In particular, road safety administrations need to devote more efforts to improve bus safety along streets with a high volume of pedestrians, recognise the importance of protection railing in protecting pedestrians during serious bus crashes, and take measures to ease bus stop crowding to prevent slight bus injuries.