Commuter cycling policy in Singapore: a farecard data analytics based approach

Abstract

Peak-hour week-day traffic congestion is a common challenge in urban mobility. Promotion of commuter cycling can help in alleviating this problem in many cities. This paper takes a data analytics approach to propose policies for promoting commuter cycling in Singapore. It uses farecard data to assess the commuter cycling potential and develops a data-driven approach to policy making. A spatio-temporal analysis of farecard data helps in finding patterns in the potential demand for first-mile as well as end-to-end cycling. This analysis is used to suggest policies like cycling towns to promote first-mile cycling and cycling regions to enable end-to-end cycling by linking together the cycling towns. Furthermore, an optimization model is developed to make efficient choice of cycling towns and links for a given budget so as to maximize the potential number of commuter cyclists.