BikeWay: A Multi-Sensory Fuzzy-Based Quality Metric for Bike Paths and Tracks in Urban Areas

Abstract

Large cities are increasingly investing in efficient mobility and sustainable transportation as an alternative to expensive and pollutant traditional vehicles. In parallel, people are also seeking cleaner and healthier options for short and last-mile distances. In order to cope with this trend, which has boosted the adoption of bikes in urban areas, a major concern of the governments has been to build bike paths and tracks that allow cyclists to move safely and through shorter distances. However, such initiatives may result in bicycles infrastructure built alongside traditional vehicular roads or on inadequate regions. Therefore, there should be a flexible way to assess the quality of bike paths and tracks, combining variables that indicate adverse conditions for the health and safety of cyclists, such as statistical risk of accidents, air and noise pollution, excessive sunlight exposure, dangerous UV radiation, among others. This article then proposes a new comprehensive quality metric that combines sensed environmental data and historical reported incidents related to bike paths and tracks, which may be employed for traditional physical signaling or to support computer-assisted solutions for cycling monitoring and alerting. Such new metric exploits fuzzy logic to model a group of variables to ultimately provide a unified safety and health quality level, referred as BikeWay. Doing so, we expect to allow dynamic and historical perceptions of how healthy and safe the bike paths and tracks are for cyclists in modern cities.

Keywords

• Smart cycling
• smart cities
• Internet of Things
• environmental monitoring
• sustainability