Panacea or Placebo? Exploring Causal Effects of Nonlocal Vehicle Driving Restriction Policies on Traffic Congestion Using Difference-in-differences Approach
Yuan Liang, Quan Yuan, Daoge Wang, Yong Feng, Pengfei Xu, Jiangping, Zhou

TL;DR
This study evaluates the short-term impact of nonlocal vehicle driving restrictions in Shanghai on traffic speeds using a difference-in-differences approach, revealing limited congestion reduction but some positive effects and side benefits.
Contribution
It provides empirical evidence on the effects of nonlocal vehicle restrictions using high-resolution traffic data and a quasi-experimental design, highlighting spatial and temporal heterogeneity.
Findings
Traffic speed increased by 1.47% during evening peak hours after policy implementation.
Limited effects during morning peak hours and in some unrestricted hours.
Heterogeneous effects across zones, greater impact in areas with lower metro density.
Abstract
Car dependence has been threatening transportation sustainability as it contributes to congestion and associated externalities. In response, various transport policies that restrict the use of private vehicle have been implemented. However, empirical evaluations of such policies have been limited. To assess these policies' benefits and costs, it is imperative to accurately evaluate how such policies affect traffic conditions. In this study, we compile a refined spatio-temporal resolution data set of the floating-vehicle-based traffic performance index to examine the effects of a recent nonlocal vehicle driving restriction policy in Shanghai, one of most populous cities in the world. Specifically, we explore whether and how the policy impacted traffic speeds in the short term by employing a quasi-experimental difference-in-differences modeling approach. We find that: (1) In the first…
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Taxonomy
TopicsUrban Transport and Accessibility · Vehicle emissions and performance · Transportation Planning and Optimization
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
