Pricing equilibrium of transportation systems with behavioral commuters

Abstract

We study Wardrop equilibrium in a transportation system with profit-maximizing firms and heterogeneous commuters. Standard commuters minimize the sum of monetary costs and equilibrium travel time in their route choice, while "oblivious" commuters choose the route with minimal idle time. Three possible scenarios can arise in equilibrium: A pooling scenario where all commuters make the same transport choice; A separating scenario where different types of commuters make different transport choices; A partial pooling scenario where some standard commuters make the same transport choice as the oblivious commuters. We characterize the equilibrium existence condition, derive equilibrium flows, prices and firms' profits in each scenario, and conduct comparative analyses on parameters representing route conditions and heterogeneity of commuters, respectively. The framework nests the standard model in which all commuters are standard as a special case, and also allows for the case in which all commuters are oblivious as the other extreme. Our study shows how the presence of behavioral commuters under different route conditions affects equilibrium behavior of commuters and firms, as well a the equilibrium outcome of the transportation system.

The impact of COVID-19 on the ride-sharing industry and its recovery: Causal evidence from China

“…Third, due to data limitation, we focus mainly on the impact of the COVID-19 pandemic on ride-sharing supply, and our analysis on the impact of COVID-19 on ride-sharing demand is limited to the average trip distance. It would be interesting to collect passenger data and directly explore the impact of the pandemic on the demand side, especially when passengers act as behavioral commuters (Lien et al, 2020). Finally, since our data are from one single ride-sharing company, we have abstracted away from competition between ride-sharing companies and drivers' multihoming behavior.…”

“…Second, by analyzing driver behavior in response to COVID-19, we add to the literature on labor supply in the taxi industry and, more recently, in the ride-sharing industry. In the taxi industry, it has been demonstrated that drivers exhibit reference-dependent preferences (Camerer et al, 1997;Crawford and Meng, 2011), respond positively to both unanticipated and anticipated increases in earning opportunities (Farber, 2015), strategically respond to surge pricing (Cachon et al, 2017;Castillo, 2018;Miao et al, 2021), overtreat passengers under a usage-based pricing scheme (Miao and Chu, 2020), and are strategic in location choices (Buchholz, 2017). In the ride-sharing industry, prior research has demonstrated flexibility in drivers' schedules and labor supply (Hall and Krueger, 2016;Chen et al, 2019), and the importance of external incentives (Allon et al, 2018;Brodeur and Nield, 2018).…”