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.

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Explaining the differential growth of peer-to-peer car-sharing in European cities

How Do On-demand Ridesharing Services Affect Traffic Congestion? The Moderating Role of Urban Compactness

With the development of vehicle automation and sharing economy, a mix of human-driven and autonomous vehicles can be expected in the next few decades, which means the conventional vehicles, carsharing, autonomous vehicles (AV) and shared AVs will coexist, affecting the way of travel. AVs, especially combined with shared use, may contribute to reduce or solve some of the present most intractable urban problems such as traffic congestion, crashes and wasted use of urban spaces. Given the emerging trend of new mobility options, understanding the decision of people in owning a private human-driven vehicle or private AV versus using a shared AV or joining car sharing would have important indications for policy makers to reduce car ownership in general. Therefore, this paper is devoted to gain insight into individuals’ propensity for purchasing an autonomous vehicle or using shared (autonomous) cars. Based on the data collected using a stated choice experiment, we estimated a random parameter error component model to identify the possible heterogeneity between individuals in the preference and possible correlations between various mobility alternatives. Results show substantial heterogeneity exists among individuals in their preferences. People in general prefer to keep their current car or buy an AV rather than to use shared cars, and shared AVs are more favorable than the traditional car sharing. Regarding the shared AVs, people are not only concerned about the operational costs and hourly rate, but also vehicle availability and access time. Moreover, younger people are more willing to accept these new mobility options.

Explaining the differential growth of peer-to-peer car-sharing in European cities

Citations

How Do On-demand Ridesharing Services Affect Traffic Congestion? The Moderating Role of Urban Compactness

Using autonomous vehicles or shared cars? Results of a stated choice experiment

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