RoadLab: Revamping Road Condition and Road Safety Monitoring by Crowdsourcing with Smartphone App

Abstract

Road agencies constantly face the challenge of developing cost-effective asset management strategies with limited resources and only a modest understanding of road infrastructure conditions from road users’ perspective. This paper described a pilot project that allows the determination of user-focused road condition indicators and road safety concerns by developing a road user oriented smartphone based app - RoadLab and extracting information from big data collected through drivers and other road users. This crowdsourcing-based approach provides wider coverage of road networks at frequent intervals and collects uniform data to support strategic and network level asset management decision making. This approach also promotes citizen engagement in decision making and enhances government accountability by enabling road agencies to promptly respond to collectively identified problems. This initiative is undertaken in conjunction with the expansion and development of the Traffic and Road Safety Coordination Center in Belarus, which the World Bank is providing technical assistance under the Transit Corridor Improvement Project. As one of the first that develops a mobile app in collaboration with a national road management agency and integrates their direct inputs into the product development, the RoadLab app and data management server developed through this pilot project is practical for practitioners - the innovative approach of applying empirical Bayes methods to update existing road roughness values presented in this paper demonstrated that big data collected from this smartphone app prove to be valuable inputs for road asset management.