Public acceptance of a crowdsourcing platform for traffic enforcement

Abstract

Iran is a country with numerous traffic accidents, offenses, and disruptions. According to the deterrence theory, increased detection of traffic offenses would decrease violations. Detection of traffic offenses is insufficient in Iran due to the limited operational capacity of the police. Reducing traffic violations would reduce accidents, disorders, and socio-economic costs. In this paper, a scheme for detecting and reporting speeding and parking violations to infringement processing through a crowdsourcing platform is proposed as a solution considering Iran’s economic and social context. Two scenarios for detecting and reporting speeding and parking violations were considered: public reporting and private sector reporting. Public acceptance is a potential challenge for the implementation of the scenarios. Therefore, an online survey with 548 samples in Iran was conducted to identify the challenges. A bivariate ordered probit model was developed to identify the variables that influence the level of public acceptance for each of the two scenarios of the proposed scheme. Several factors contributing to public acceptance were found, including privacy concerns, penalties, traffic police performance and actions, and the private sector’s past performance. Based on the public acceptance of the proposed scheme, the results of this study recommend that transportation policymakers implement it based on monitoring feedback on implementation in three phases, including detecting violations on public transit and sending a warning message to the violator, issuing tickets based on the private sector’s reporting, and sending warning messages based on public reporting.