Zhang Wandi, Chen Feng, Wang Zijia, Huang Jianling, Wang Bo
a School of Civil Engineering , Beijing Jiaotong University , Beijing , People's Republic of China.
b Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention , Beijing Jiaotong University , Beijing , People's Republic of China.
J Air Waste Manag Assoc. 2017 Nov;67(11):1249-1257. doi: 10.1080/10962247.2017.1320597. Epub 2017 Apr 28.
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. These 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 geographic information systems (GIS) data are established with a research focus on the private driving restriction policies being implemented in an ever-increasing number of cities. The study 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 and on 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.
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 and environment improvement before the implementation of these restriction policies and provide a reference for relevant decisions.
公共交通自动收费（AFC）系统能够持续记录大量乘客出行信息，为公共交通相关法规研究提供海量低成本数据。这些数据不仅可用于分析乘客出行特征，还可用于评估促进出行方式转变和减排的交通政策。在本研究中，结合卡片、调查和地理信息系统（GIS）数据建立模型，重点研究越来越多城市正在实施的私家车限行政策。该研究旨在评估这些政策对出行方式转变以及相关碳排放减少的影响。以北京实施的私家车限行政策为例，通过基于地铁AFC数据的模型分析限行政策对从汽车出行向地铁出行转变的影响。还基于GIS方法和调查数据分析这些乘客的出行路径，并估算相关的碳排放减少量。本研究使用的分析方法可为大数据在评估交通政策中的应用提供参考。
机动车已成为中国城市最主要的排放源及空气污染来源。评估限行政策对出行方式转变和车辆排放的影响，对未来其他城市将有所帮助。交通大数据在估算出行方式转变和减排方面发挥着重要支撑作用，可帮助相关部门在实施这些限行政策前估算缓解交通拥堵和改善环境的效果，并为相关决策提供参考。
