Characterization of Taxi Fleet Operational Networks and Vehicle Efficiency: Chicago Case Study

Abstract

Taxi fleets serve a significant and important subset of travel demand in major cities around the world. This paper characterizes the Chicago taxi fleet operational network using complex network metrics and analyzes the operational efficiency of individual taxis over the past four years using an extensive taxi-trip dataset. The dataset, recently released by the city of Chicago, includes the pickup and drop-off census tracts and time stamps for over 100 million taxi trips. The paper explores year-over-year changes in the spatial distribution of Chicago taxi travel demand. The taxi pickup and drop-off census tract locations are modeled as nodes, and links are generated between unique pickup and drop-off node pairs. The analysis shows that high-demand pickup and drop-off location pairs in 2013 generated similar trip volumes in 2016; however, the low-demand pairs in 2013 generated significantly fewer trips in 2016. Additionally, this paper presents temporal efficiency and spatial efficiency metrics. The temporal efficiency metric determines the percentage of in-service time taxis are productive (i.e., transporting travelers), rather than empty. The spatial efficiency metric measures the percentage of taxi miles that are productive (i.e., loaded), rather than empty. The efficiency analysis of the Chicago taxi fleet shows that, for most taxis, around 50% of their in-service time and travel distance are unproductive. This inefficiency negatively affects the profitability of individual drivers and the fleet, traffic congestion, vehicle emissions, the service quality provided to customers, and the ability of taxi services to compete with emerging mobility services.