Personalized Route Selection Methods in an Urban Computing Scenario

Abstract

As urban areas continue to expand, the infrastructural systems within these regions face multifaceted challenges that detrimentally affect inhabitants’ health and quality of life. The advent of smart urban mobility technologies has introduced a persistent surveillance mechanism over the movement patterns of individuals and the ambient environmental conditions, including but not limited to the prevalence of crime, traffic accidents, and levels of air pollution. These technologies significantly contribute to the enhancement of urban transportation systems. In parallel, Location-Based Social Networks (LBSNs) leverage geolocation data derived from users to analyze travel behaviors and recommend alternative transportation options. This research introduces two innovative strategies aimed at selecting routes characterized by lower levels of pollution: the first strategy employs a multimodal transportation integration approach, and the second endorses route selection based on a comprehensive set of personalized criteria. The multimodal transport strategy offers journey options that are both cost-efficient and minimize environmental pollution. Upon assessing all potential routes, the personalized, multi-criteria-based approach demonstrates superior efficacy in route selection compared to methods that rely on a singular criterion within identical scenarios.