Accelerated failure time modeling of in-lane street hawkers’ lane entry and exit behaviors at signalized intersections

Abstract

In-lane street hawking is the intermittent entry of signalized intersections by traders to sell groceries to drivers and passengers. Studies have shown that hawkers get exposed to traffic injuries but the lack of quantitative analysis of their lane entry and exit behaviors in signalized intersections makes it difficult to improve traffic safety. This study analyzes the significant predictors of in-lane street hawkers’ (1) lane entry within 30 s after the red signal illumination, (2) lane exit within 30 s after the green signal illumination, and (3) probability of getting injuries during the green signal time. Drone-based trajectory data were collected from a selected signalized intersection in Accra, Ghana. A Weibull accelerated failure time duration model incorporating Gamma frailty was used to evaluate hawkers’ behaviors. Overall, the majority of hawkers exhibited red-light running behaviors exposing them to traffic injuries. An increase in traffic speed, especially beyond 20 km/h, exposed hawkers to injury risks significantly. Notably, hawkers’ lane entry decreased significantly as the traffic speed increased. Their lane exit duration was significantly predicted by the queue lengths and traffic volumes. Accordingly, safety practitioners can enhance traffic regulation and control methods in addition to pro-poor social interventions to demotivate hawking at signalized intersections.

Keywords:

• In-lane street hawking
• traffic hawkers
• traffic violations
• trajectory data
• survival analysis
• accelerated failure time model

Disclosure statement

No potential conflict of interest was reported by the authors.

Author contributions

Philip Kofi Alimo: Conceptualization, methodology, data curation, formal analysis, writing-original draft, visualization, writing – review & editing; Lawrencia Agen-Davis: Investigation, Writing – review & editing; Ling Wang: Conceptualization, writing – review & editing, validation; Wanjing Ma: Supervision, funding acquisition, resources. All authors approved the final manuscript.

Data availability statement

The dataset is the subject of ongoing research.

Additional information

Funding

This research was supported by the National Natural Science Foundation of China (No. 52325210, 52131204).