Authors: Jianying Wang*, 1. Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University; 2. Department of Urban and Environmental Policy and Planning, Tufts University, Yu Liu, Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University; , Shan Jiang, Department of Urban and Environmental Policy and Planning, Tufts University
Topics: Transportation Geography, China
Keywords: Traffic efficiency equality, mobility on demand, congestion
Session Type: Paper
Start / End Time: 10:15 AM / 11:30 AM
Room: Governors Square 10, Sheraton, Concourse Level
Presentation File: No File Uploaded
Recent advances in the geo-information system and information and communication technologies has rapidly changed the landscape of urban mobility markets, generating ubiquitous digital traces of human travel that enable a new wave of studies on the urban transportation systems. However, few efforts have been made to examine the inequality of the mobility on demand (MoD) services due to the lack of traveler sociodemographic information associated with the big data for privacy constraints. In this study, using Beijing as an example, we analyze thousands of millions of taxi trajectories (in 2015 and 2016) and develop a framework to estimate the mobility inequality among different social-economic groups by comparing actual trips revealed from the data with the estimated optimal travel time. By applying a map-matching algorithm to infer the real trajectories of taxi trips, we estimate latent optimal travel time and the delay due to various route choices under different economic and social incentives enabled by the MoD services. By comparing the origin and destination pairs in communities with different social-economic profiles we estimate the spatial inequity of the MoD service in Beijing. Our research will help planners and policy makers develop policy measures to address the inequality issues in mobility services in metropolitan areas.