Pedestrian safety at unsignalized mid-block crossings remains a major concern in developing countries with heterogeneous traffic conditions. This study examines pedestrian safety margins (PSMs) and their key behavioral and traffic-related determinants in an urban Indian context. A data-driven regression model is developed to quantify the influence of pedestrian behavior, vehicular characteristics, and traffic gaps on safety margins at uncontrolled crossings. Video data were collected at three representative mid-block locations in Patna, India, yielding 722 pedestrian–vehicle interactions for model development. Field observations show that 26% of pedestrians adopted rolling crossing behavior, while 54.8% crossed in groups. The proposed model demonstrates satisfactory explanatory power, with an R 2 value of 0.553. Pedestrian speed, vehicular speed, vehicular gap, and vehicle type emerged as statistically significant predictors of pedestrian safety margin. Larger vehicular gaps were positively associated with higher safety margins. Pedestrian speed ( β = 0.428) and vehicular gap ( β = 0.383) were identified as the most influential positive contributors, whereas vehicular speed exhibited a smaller yet significant positive effect ( β 0.032). Two-wheelers exerted the strongest negative impact on pedestrian safety margins ( β = −0.964) compared to heavy commercial vehicles. The findings underscore the need for integrated, behavior-sensitive interventions to enhance pedestrian safety.
Suman et al. (Thu,) studied this question.
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