Transport Networking Companies Demand and Flow Estimation in New York City

Abstract

Given the burgeoning growth in transport networking companies (TNC)-based ride hailing systems and their growing adoption for trip making, it is important to develop modeling frameworks to understand TNC ride hailing demand flows at the system level. Two choice dimensions are identified: (1) a demand component that estimates origin level TNC demand at the taxi zone level and (2) a distribution component that analyzes how these trips from an origin are distributed across the region. The origin level demand is analyzed using linear mixed models while flows from origin to multiple destinations is analyzed using a multiple discrete-continuous extreme value (MDCEV) model. The data for the analysis is drawn from New York City Taxi and Limousine Commission for 12 months from January through December 2018. For this analysis, weekday morning peak hour demand and distribution patterns are examined. The model components are developed using a comprehensive set of independent variables. The model estimation results offer very intuitive results for origin demand and distribution of flows across destinations. The model was validated by predicting trips to destination taxi zones and it was found that predicted model performs well in identifying high preference destination zones. In addition, elasticity effects are computed by evaluating the percentage change in baseline marginal utility in response to increasing the value of exogenous variables by 10%, 25% and 50%, respectively.