Modeling sustainable mobility futures using an automated process of road space reallocation in urban street networks: A case study in Zurich

Abstract

Transport policy research must explore different more or less radical mobility futures that would be able to rapidly reduce the carbon footprint of transport. In this paper, we present a method for modeling one such future by focusing on the allocation of road space between different modes of transport. In a case study for Zurich, Switzerland, we apply the method to reshape the street network allocating the maximum possible proportion of road space to cycling infrastructure, while still allowing access for motorized traffic. Multiple design rules are applied: Ensuring the reachability for motorized traffic, guaranteeing the routes needed by the present public transit services, and maintaining street hierarchies. The resulting network shows many single- lane one-way streets, with generous infrastructure for cycling. We use Value-of-Distance indicators from route choice models to represent the benefits created by the added cycling infrastructure. In a descriptive analysis of the present and the rebuilt street network, we show that the overall length of the cycling infrastructure can be increased by a factor of three while reducing the average distances perceived by cyclists by 20%. On the other hand, the average shortest path for motorized traffic would grow by 36%, its network of lanes would become less redundant and its capacity would decrease. A Python implementation is available as open-source software. The networks generated by the software can be used as input in common transport modeling tools for impact assessment studies.