Video Recognition for Analyzing the Characteristics of Vehicle–Bicycle Conflict

Abstract

Vehicle-bicycle conflict incurs a higher risk of traffic accidents, particularly as it frequently takes place at intersections. Mastering the traffic characteristics of vehicle-bicycle conflict and optimizing the design of intersections can effectively reduce such conflict. In this paper, the conflict between right-turning motor vehicles and straight-riding bicycles was taken as the research object, and T-Analyst video recognition technology was used to obtain data on riding (driving) behavior and vehicle-bicycle conflict at seven intersections in Changsha, China. Herein, eight typical traffic characteristics of vehicle-bicycle conflict are summarized, the causes of vehicle-bicycle conflict are analyzed using 18 factors in three dimensions, the internal relationship between intersection design factors and traffic conflicts is explored, and the guiding of design optimization based on the width of bicycle lanes and the soft separation between vehicles and bicycles is discussed. The results showed that colored paved bicycle lanes were better, performing better at a width of 2.5 m compared to 1.5 m. However, the colored pavement was not suitable for the entire road and had to be set at the position, at which the trajectories of a bicycle and motor vehicle overlapped. Thus, a 2.5-m-wide bicycle lane provides good safety. However, there are still defects in the existing safety indicators, so it is necessary to develop new indicators to reflect real vehicle-bicycle conflict situations more comprehensively.

Safety Analysis of Riding at Intersection Entrance Using Video Recognition Technology

“…The widely used vehicle-bicycle conflict models [19] mainly include traditional regression analysis models and generalized linear models, fuzzy neural network models, probability models, Bayesian hierarchical models, and so on. Cheng et al [20] used linear and non-linear regression analysis methods to construct a model of the relationship between bicycle traffic volume and the number of vehiclebicycle conflicts; Bai et al [21] constructed a generalized linear regression model with fixed effects, random effects, and random parameters; Stipancic et al [15] divided the traffic conflicts into 3 categories based on the PET value, and establish an ordered logit regression model of 3 conflict categories; Wang et al [22] judged the probability of right-turning motor vehicles and straightgoing non-motor vehicles through the probability model; Gao et al [23] established a regression model considering the factors of distance, speed, and angle of vehicle-bicycle conflict and explained the number of vehicle-bicycle conflict with the arrivals of straight-going non-motor vehicles and equivalent cars.…”

Multi Chunk Learning Based Auto Encoder for Video Anomaly Detection

“…For example, to identify whether pedestrians exhibit the same type of abnormal behavior or action, such as going over railings or falling, researchers usually use human trajectory or action recognition [4][5][6] methods to detect whether pedestrians are abnormal in the video. However, in non-open scenes and dense traffic, pedestrians or vehicles [7] can be obscured, which poses some troubles for researchers adopting both methods, allowing them to extract only non-occluded features. Lacking occluded features, complete and accurate features cannot be obtained.…”