Data-Driven Approach for Urban Micromobility Enhancement through Safety Mapping and Intelligent Route Planning

Abstract

Micromobility responds to urban transport challenges by reducing emissions, mitigating traffic, and improving accessibility. Nevertheless, the safety of micromobility users, particularly cyclists, remains a concern in urban environments. This study aims to construct a safety map and a risk-averse routing system for micromobility users in diverse urban environments, as exemplified by a case study in Lisbon. A data-driven methodology uses object detection algorithms and image segmentation techniques to identify potential risk factors on cycling routes from Google Street View images. The ‘Bikeable’ Multilayer Perceptron neural network measures these risks, assigning safety scores to each image. The method analyzed 5321 points across 24 parishes in Lisbon, with an average safety score of 4.5, indicating a generally safe environment for cyclists. Carnide emerged as the safest area, while Alcântara exhibited a higher level of potential risks. Additionally, an equation is proposed to compute route efficiency, enabling comparisons between different routes for identical origin-destination pairs. Preliminary findings suggest that the presented routing solution exhibits higher efficiency than the commercial routing benchmark. Risk-averse routes did not result in a substantial rise in travel distance or time, with increments of 7% on average. The study also contributed to increasing the existing amount of cycle path data in Lisbon by 12%, correcting inaccuracies, and updating the network in OpenStreetMap, providing access to more precise information and, consequently, more routes. The key contributions of this study, such as the safety map and risk-averse router, underscore the potential of data-driven tools for boosting urban micromobility. The solutions proposed demonstrate modularity and adaptability, making them fit for a range of urban scenarios and highlighting their value for cities prioritizing safe, sustainable urban mobility.