Citation

Zhang W, Chen F, Wang Z, Huang J, Wang B. J. Air Waste Manag. Assoc. (1995) 2017; 67(11): 1249-1257.

Affiliation

c Beijing Transportation Information Center , Block B, Shoufa Mansion, No.A9, Liuliqiao South Lane, Fengtai District, Beijing , 100073 , P. R . China.

Copyright

(Copyright © 2017, Air and Waste Management Association, Publisher Informa- Taylor and Francis)

DOI

10.1080/10962247.2017.1320597

PMID

28453402

Abstract

Public transportation automatic fare collection (AFC) systems are able to continuously record large amounts of passenger travel information, providing massive, low-cost data for research on regulations pertaining to public transport. This data can be used not only to analyze characteristics of passengers' trips but also to evaluate transport policies that promote a travel mode shift and emission reduction. In this study, models combining card, survey, and GIS data are established with a research focus on the private driving restriction policies being implemented in an ever-increasing number of cities. It aims to evaluate the impact of these policies on the travel mode shift as well as relevant carbon emission reductions. The private driving restriction policy implemented in Beijing is taken as an example. The impact of the restriction policy on the travel mode shift from cars to subways is analyzed through a model based on metro AFC data. The routing paths of these passengers are also analyzed based on the GIS method as well as survey data while associated carbon emission reductions are estimated. The analysis method used in this study can provide reference for the application of big data in evaluating transport policies. Implications Motor vehicles have become the most prevalent source of emissions and subsequently air pollution within Chinese cities. The evaluation of the effects of driving restriction policies on the travel mode shift and vehicle emissions will be useful for other cities in the future. Transport big data, playing an important support role in estimating the travel mode shift and emission reduction considered can help related departments to estimate the effects of traffic jam alleviation as well as environment improvement before the implementation of these restriction policies and provide a reference for relevant decisions.

Language: en

Keywords

Carbon emissions; driving restrictions; smart-card data; urban transport