Integrated Crowdsourcing Platform to Investigate Non-Motorized Behavior and Risk Factors on Walking, Running, and Cycling Routes

Al-Fuqaha, Ala; Oh, Jun-Seok; Kwigizile, Valerian; Mohammadi, Sepideh; Alhomadat, Fadi · 2017 · ROSA P / Western Michigan University. Transportation Research Center for Livable Communities

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Summary

This research addresses the growing safety risks associated with non-motorized transportation, specifically walking, running, and cycling. Despite a significant increase in bicycle trips in the United States, fatal and serious injury crashes have risen, driven by infrastructure deficiencies and traffic conditions. The study aims to identify critical risk factors and develop an intelligent software system that leverages crowdsourcing to mitigate these hazards. By informing the public and local authorities about potential dangers, the project seeks to improve route safety, reduce injuries, and support the development of livable communities. To achieve these goals, the researchers developed and launched "BikeableRoute," a mobile application designed for Android and iOS platforms using Apache Cordova, Ionic, and Google App Engine. The platform allows users to anonymously report hazards, track their routes, and share data with fellow cyclists and local authorities. The development process included a comprehensive literature review of existing cycling apps and risk factor categorization, followed by a web survey of 182 participants from local cycling groups and university communities. The survey identified key user preferences and ranked risk factors across three categories: infrastructure-related (e.g., potholes, lack of dedicated lanes), traffic-related (e.g., high-speed traffic, aggressive drivers), and facility-related (e.g., insufficient lighting, blind corners). The application integrates GIS data from OpenStreetMap to pinpoint hazard locations and utilizes mobile sensors to collect travel data such as speed, duration, and distance. The findings highlight that potholes and the lack of dedicated bicycle lanes were ranked as the most impactful infrastructure-related risk factors by survey participants. The study also revealed that 92% of respondents would be interested in using an app that allows them to report risk factors, with mapping and tracking identified as the most useful features. The BikeableRoute platform successfully collected anonymous self-reported risk factors and biking data, enabling the generation of statistical reports for local authorities. These reports provide estimates of traffic volume on different routes and prioritize the remedy of reported hazards. The system allows for real-time updates, where hazards are removed from the database once eliminated, ensuring current information for users. The significance of this work lies in its demonstration of how crowdsourcing and mobile technology can enhance non-motorized transportation safety. By bridging the gap between citizen feedback and municipal action, the platform enables local authorities to operate more efficiently in addressing infrastructure issues. The study provides a scalable model for collecting granular, real-time data on road conditions and cyclist behavior, which can inform urban planning and policy decisions. Ultimately, the research contributes to the field by offering a practical tool for reducing crash risks and promoting safer, more accessible routes for walkers, runners, and cyclists.

Key finding

Potholes were identified as the most significant infrastructure-related risk factor by survey participants, and the BikeableRoute application successfully enabled the collection of anonymous hazard reports to assist local authorities in prioritizing road maintenance.

Methodology

mixed_methods

Sample size: 158

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• cyclist safety

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: crash risk outcomes, observational prevalence