  Transport operators have a range of intervention options available to improve
or enhance their networks. Such interventions are often made in the absence of
sound evidence on resulting outcomes. Cycling superhighways were promoted as a
sustainable and healthy travel mode, one of the aims of which was to reduce
traffic congestion. Estimating the impacts that cycle superhighways have on
congestion is complicated due to the non-random assignment of such intervention
over the transport network. In this paper, we analyse the causal effect of
cycle superhighways utilising pre-intervention and post-intervention
information on traffic and road characteristics along with socio-economic
factors. We propose a modeling framework based on the propensity score and
outcome regression model. The method is also extended to the doubly robust
set-up. Simulation results show the superiority of the performance of the
proposed method over existing competitors. The method is applied to analyse a
real dataset on the London transport network. The methodology proposed can
assist in effective decision making to improve network performance.
