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Transport operators have a range of intervention options available to improveor enhance their networks. Such interventions are often made in the absence ofsound evidence on resulting outcomes. Cycling superhighways were promoted as asustainable and healthy travel mode, one of the aims of which was to reducetraffic congestion. Estimating the impacts that cycle superhighways have oncongestion is complicated due to the non-random assignment of such interventionover the transport network. In this paper, we analyse the causal effect ofcycle superhighways utilising pre-intervention and post-interventioninformation on traffic and road characteristics along with socio-economicfactors. We propose a modeling framework based on the propensity score andoutcome regression model. The method is also extended to the doubly robustset-up. Simulation results show the superiority of the performance of theproposed method over existing competitors. The method is applied to analyse areal dataset on the London transport network. The methodology proposed canassist in effective decision making to improve network performance.

Analysing the causal effect of London cycle superhighways on traffic congestion