A Real‐Time Data‐Driven Cycling Routing Approach: A Case Study in Singapore

ABSTRACT Since cyclists must consider dynamic criteria such as weather and traffic safety when selecting routes, incorporating real‐time road conditions into route planning is essential. However, few existing studies have addressed real‐time cycling route planning. Therefore, this study proposed a real‐time data‐driven cycling routing approach. The approach integrated multiple criteria concerning cyclists' health, safety, and comfort, while accounting for their dynamic variations. To respond to these real‐time changes in criteria, two Dijkstra‐based dynamic routing models were introduced, distinguished by their mechanisms for triggering path recalculation: the Periodic Update Model (PUM) and the Node‐Based Update Model (NUM). The proposed method was validated through simulation experiments of cyclist travel behavior in a case study conducted in Singapore. The experimental results, measured by the Optimal Route Provision Rate (ORPR), confirmed that both proposed dynamic routing models consistently outperformed the traditional shortest‐path model across all evaluated scenarios. Nuanced differences in the models' performance were also revealed: while NUM proved superior in route quality for short‐distance scenarios, PUM demonstrated robust scalability for long‐distance scenarios. The conclusions affirm the effectiveness of the proposed method in supporting cyclists' route choice decisions and highlight the potential for advancing more systematic bicycle route planning.