A new study found that it significantly decreased congestion.
Early this year, a paper in PNAS using a computer model estimated that car sharing services like Uber and Lyft could reduce the number of taxi vehicles on roads by ~76% without significantly impacting travel time. As Joe Cortright has said, the authors are overly optimistic.
There is another study from last year that analyzed what actually happened to congestion levels when Uber entered the market in some US cities (abstract below). The results of this study are not really comparable to the the paper in PNAS, though. The methods are sound but I have the impression the authors pay too much attention to the statistical significance of the results and do not really discuss the magnitude of the effects of Uber entry on congestion. In any case, it’s a good read.
Li, Z., Hong, Y., & Zhang, Z. (2016). Do Ride-Sharing Services Affect Traffic Congestion? An Empirical Study of Uber Entry. Available at SSRN: https://ssrn.com/abstract=2838043
Sharing economy platform, which leverages information technology (IT) to re-distribute unused or underutilized assets to people who are willing to pay for the services, has received tremendous attention in the last few years. Its creative business models have disrupted many traditional industries (e.g., transportation, hotel) by fundamentally changing the mechanism to match demand with supply in real time. In this research, we investigate how Uber, a peer-to-peer mobile ride-sharing platform, affects traffic congestion and environment (carbon emissions) in the urban areas of the United States. Leveraging a unique data set combining data from Uber and the Urban Mobility Report, we examine whether the entry of Uber car services affects traffic congestion using a difference-in-difference framework. Our findings provide empirical evidence that ride-sharing services such as Uber significantly decrease the traffic congestion after entering an urban area. We perform further analysis including the use of instrumental variables, alternative measures, a relative time model using more granular data to assess the robustness of the results. A few plausible underlining mechanisms are discussed to help explain our findings.
A good-looking video of the computer simulation model of the PNAS paper.
Rafael Pereira is a PhD candidate in the Transport Studies Unit (TSU) at Oxford University. He is currently on leave from a research position at the Institute for Applied Economic Research (Ipea - Brazil), where he conducts policy-oriented research in the fields of urban and regional development, transport policies and demography.
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