Optimal Fleet Size and Fare Setting in Emerging Taxi Markets with Stochastic Demand

Abstract

The emerging taxi services, for instance, Uber and Lyft, are challenging traditional fully regulated taxi markets. Transportation agencies are spending significant efforts to understand the optimal pricing and fleet size taxis that are efficient for a given urban area. This study develops a modeling framework for studying a decentralized equilibrium based market study where the fare is strictly regulated by a taxi commission. The nature of demand‐supply equilibria with stochastic demand are discussed to determine optimal development strategies, for instance, number of issued licenses and fare setting. Two Stackelberg games are formulated to specify leader‐follower relationships between transportation authorities and and the followers — taxi drivers and passengers. An iterative approach is designed to simulate the games and solve corresponding mathematical optimization problems. The case study is based on New York City data which shows that the taxi market may be oversupplied and underpriced, which confirms findings from other studies and price hikes in 2012. Furthermore, different development strategies are proposed based on two Stackelberg games to respond to intended taxi system changes, such as price and quantity elasticity of taxi demand, levels of demand variance, average taxi operation speed, passengers' waiting time value, and taxi service coverage. The results have important implications in determining development strategies for taxi industry with emerging taxi services, stochastic demand, and the rapidly changing environment.

Using ‘Big Data’ to understand the impacts of Uber on taxis in New York City

“…The number of taxis to be licensed by regulators is a vital decision. Oversupply of taxis can lead to unsustainable prices, high vacancy rates (Zhang and Ukkusri 2016) and poorly-qualified drivers (Schaller 2005). However, under-supply can cause extortionately high prices, long wait times and can lead to certain areas being deprived of service at all (Benz and Sumares 2015).…”

Modeling the Taxi Drivers’ Customer-Searching Behaviors outside Downtown Areas

“…The widespread existence of location-aware techniques has provided a new perspective for taxi studies along with the pervasive dataset, such as taxi GPS data recording detailed trip-level characteristics. The applications vary from the taxi movements tracking, the link traffic state estimation, to the taxi system efficiency measurement [30][31][32][33][34]. Szeto et al [28] and Wong et al [14,29] also introduced a pervasive dataset to validate customer searching modeling, but only sampled one-week GPS data from a small portion of 400 taxicabs (out of 10,000 taxicabs) in the city.…”