Demand, Supply, and Performance of Street-Hail Taxi

TL;DR

This study models and analyzes the demand and supply of street-hail taxis in NYC at a detailed street segment level using GPS data, revealing insights into their performance relative to TNCs and implications for urban regulation.

Contribution

It introduces a novel non-stationary Poisson random field model for high-resolution estimation of taxi demand and supply using GPS data, validated with extensive trip records.

Findings

Taxi demand remained stable between 2011 and 2012 but declined in 2013.

Street-hail taxis outperform TNCs like Uber in high-demand areas.

Demand estimates are consistent across different supply levels and years.

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.