Road Users’ Reports on Danger Spots: The Crowd as an Underestimated Expert?

Abstract

As part of the project EDDA+ (Early Detection of Dangerous Areas in road traffic using smart data), a web-based crowdsourcing platform has been launched on which road users can report danger spots they face in everyday traffic. Whereas official police collision data can only be used reactively, these user reports are intended to warn other road users and provide road safety stakeholders with detailed information for proactive measures. Since this approach is relatively novel, the present pilot study aimed to evaluate the validity of these subjective road user reports. A quasi-randomized sample of N = 77 danger spots distributed over four major German cities was audited using a 70-item objective road safety deficit inventory to identify infrastructural deficits. Based on these items, an overall rating of objective hazardousness for each danger spot was derived. In more than half of the audited danger spots, infrastructural deficits were identified in the audit (=confirmed hazard). In another quarter of audited dangers spots, the reported hazard could not be identified without any doubt due to a lack of infrastructural deficit or detailed information about the nature of the hazard (=uncertain, no certain match between audit and report). Our analysis further revealed that an increased number of road user interactions for the respective danger spot yielded a higher likelihood of confirmation of a danger spot’s hazardousness. Descriptively, pedestrians and bicyclists were most often mentioned as exposed to danger, with the most prevalent nature of danger being areas with poor visibility and misconduct by drivers. The results were blended with police collision data in the next step. We did not find a significant relationship between our danger spots’ rating and the number of collisions at the respective spot. Our results indicate that reports of danger spots and the increased user related activity can serve as an indicator for the early detection of road traffic hazards.