Quantifying the social impacts of the London Night Tube with a double/debiased machine learning based difference-in-differences approach
place - europe, place - urban, mode - subway/metro, planning - personal safety/crime, planning - safety/accidents
Public transportation, Night-time transport services, Difference-in-differences, Causal machine learning, Double/debiased machine learning
There is a worldwide trend toward a growing number of people involved in various night-time activities. The night-time public transport service is of central importance for the urban night-time mobility. In London, the Night Tube service was launched in 2016 to meet the constantly growing night-time travel demand and support London’s night-time economy. Yet limited empirical evidence on the ex-post impacts of the London Night Tube has been provided. In this study, we conduct a causal analysis on such impacts using a double/debiased machine learning based difference-in-differences approach. Specifically, we quantify the impacts of the Night Tube on London’s night-time economy, house prices, road crashes and related casualties, and crimes. We further investigate the spatial variations in such impacts. Our results indicate a rise in house prices associated with the announcement and the implementation of the service. The number of night-time workplaces showed a limited response. Regarding the safety dimension, we find that the Night Tube service led to a small reduction in the frequency of road crashes but a substantial reduction in crash-related casualties. However, the crime rate in areas served by the Night Tube was increased, especially for the following two categories, robbery of personal property and violence against the person. Moreover, the impact on the crime rate is found to be larger in the inner London area. These findings provide practical implications for urban planners and policy makers, and reveal the need for monitoring the social impacts of the Night Tube service from a long-term perspective.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Zhang, Y., Li, H., & Ren, G. (2022). Quantifying the social impacts of the London Night Tube with a double/debiased machine learning based difference-in-differences approach. Transportation Research Part A: Policy and Practice, Vol. 163, pp. 288-303.
Transportation Research Part A Home Page: