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

Ziru Li, Chen Liang (), Yili Hong and Zhongju Zhang

Production and Operations Management, 2022, vol. 31, issue 1, 239-258

Abstract: The role of information technology (IT) in managing operations that support environmentally sustainable growth has been emphasized a lot in operations management and information systems research. In this paper, we study the impact of the IT‐based on‐demand ridesharing platforms on an important aspect of sustainability—traffic congestion. Our theoretical prediction suggests two countervailing effects from the entry of ridesharing platforms to urban areas: the efficiency‐enhancing effect that reduces traffic congestion and the demand‐inducing effect that increases traffic congestion. We propose that the impacts of ridesharing services on traffic congestion should vary with urban spatial features. Given the theoretical tension, we investigate the impact of Uber entry on traffic congestion in urban areas of the United States with a focus on the moderating role of urban compactness. Based on a unique dataset that combines multiple archival sources, we empirically examine whether the entry of Uber's on‐demand ridesharing service affects traffic congestion by using a difference‐in‐differences framework. Our empirical evidence indicates that ridesharing services significantly increase traffic congestion in compact areas. Meanwhile, we find some marginal evidence that ridesharing services decrease traffic congestion in sprawling urban areas. The results are robust to a series of additional analyses, including the use of alternative measures, relative time model, entry exogeneity test, and placebo tests. We conclude that the efficiency‐enhancing and demand‐inducing effects shape traffic congestion and that the net effect varies according to different levels of urban compactness. We provide circumstantial evidence for the underlying mechanisms by analyzing public transit and commuting characteristic data.

Date: 2022
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