  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
month, the policy led to an increase of the network-level traffic speed by
1.47% (0.352 km/h) during evening peak hours (17:00-19:00) but had no
significant effects during morning peak hours (7:00-9:00). (2) The policy also
helped improve the network-level traffic speed in some unrestricted hours
(6:00, 12:00, 14:00, and 20:00) although the impact was marginal. (3) The
short-term effects of the policy exhibited heterogeneity across traffic
analysis zones. The lower the metro station density, the greater the effects
were. We conclude that driving restrictions for non-local vehicles alone may
not significantly reduce congestion, and their effects can differ both
temporally and spatially. However, they can have potential side effects such as
increased purchase and usage of new energy vehicles, owners of which can obtain
a local license plate of Shanghai for free.
