Demand, Supply, and Performance of Street-Hail Taxi

Abstract

Travel decisions are fundamental to understanding human mobility, urban economy, and sustainability, but measuring it is challenging and controversial. Previous studies of taxis are limited to taxi stands or hail markets at aggregate spatial units. Here we estimate the dynamic demand and supply of taxis in New York City (NYC) at street segment level, using in-vehicle Global Positioning System (GPS) data which preserve individual privacy. To this end, we model taxi demand and supply as non-stationary Poisson random fields on the road network, and pickups result from income-maximizing drivers searching for impatient passengers. With 868 million trip records of all 13,237 licensed taxis in NYC in 2009 -2013, we show that while taxi demand are almost the same in 2011 and 2012, it declined about 2% in spring 2013, possibly caused by transportation network companies (TNCs) and fare raise. Contrary to common impression, street-hail taxis out-perform TNCs such as Uber in high-demand locations, suggesting a taxi/TNC regulation change to reduce congestion and pollution. We show that our demand estimates are stable at different supply levels and across years, a property not observed in existing matching functions. We also validate that taxi pickups can be modeled as Poisson processes. Our method is thus simple, feasible, and reliable in estimating street-hail taxi activities at a high spatial resolution; it helps quantify the ongoing discussion on congestion charges to taxis and TNCs.

Drivers learn city-scale dynamic equilibrium

“…For on-demand services provided by transportation network companies (TNCs), the demand and supply dynamics are necessarily different, which merit separate studies. Perhaps surprisingly, in [38] we show that taxis out-perform TNCs in high-demand locations. Future demand-supply studies should look deeper into the differences and the interaction between on-demand services.…”

“…Each trip record contains medallion ID (for vehicles), hack license (for drivers), latitude, longitude and time stamp of pickup and drop-off, trip distance, fare amounts, and other attributes. We use the ID fields to link a taxi between consecutive trips, and derive new attributes for use in our studies [37], [38]. The original and processed data are available for reuse at [40].…”

“…In this paper, we use a game-theoretic model to predict drivers' spatio-temporal search strategies, examine the individual and group learning dynamics as drivers gain experience, and validate these model predictions with large-scale GPS trajectory data. Following our initial work [37], [38], we regard the regularity in urban transportation as the equilibrium outcome of individual decision-making, in response to transportation demand and services. For on-demand services, given exogenous traffic speeds and passenger demand, the income maximization of drivers leads to an economic equilibrium.…”

Drivers learn city-scale dynamic equilibrium

“…Pickup rate can thus be expressed as a function of supply rate, demand rate and hailer impatience: µ px (µ sx , µ dx , µ tx ). [32] proposed a class of pickup models and proved that the pickup rate functions are increasing, strictly concave, and arbitrarily differentiable, with respect to supply rate; for three representative models, analytical forms of the pickup rate functions are also provided.…”

“…In this paper, we regard the regularity in urban transportation as the equilibrium outcome of individual decision making in response to transportation demand and services [32]. In particular, we provide a microscopic, game-theoretic model for taxi transportation and interpret its equilibrium in a macroscopic framework of thermodynamics.…”