December 2021 Analysing the causal effect of London cycle superhighways on traffic congestion
Prajamitra Bhuyan, Emma J. McCoy, Haojie Li, Daniel J. Graham
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Ann. Appl. Stat. 15(4): 1999-2022 (December 2021). DOI: 10.1214/21-AOAS1450


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 nonrandom assignment of such intervention over the transport network. In this paper we analyse the causal effect of cycle superhighways utilising preintervention and postintervention information on traffic and road characteristics along with socioeconomic 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.

Funding Statement

The authors would like to acknowledge the Lloyd’s Register Foundation for funding this research through the programme on Data-Centric Engineering at the Alan Turing Institute.


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Prajamitra Bhuyan. Emma J. McCoy. Haojie Li. Daniel J. Graham. "Analysing the causal effect of London cycle superhighways on traffic congestion." Ann. Appl. Stat. 15 (4) 1999 - 2022, December 2021.


Received: 1 December 2020; Revised: 1 February 2021; Published: December 2021
First available in Project Euclid: 21 December 2021

MathSciNet: MR4355086
zbMATH: 1498.62304
Digital Object Identifier: 10.1214/21-AOAS1450

Keywords: Average treatment effect , confounder , difference-in-difference , intelligent transportation system , potential outcome

Rights: Copyright © 2021 Institute of Mathematical Statistics


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Vol.15 • No. 4 • December 2021
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