location: Current position: Home >> Scientific Research >> Paper Publications

Induced Traffic in China: Elasticity Models with Panel Data

Hits:

Indexed by:期刊论文

Date of Publication:2015-12-01

Journal:JOURNAL OF URBAN PLANNING AND DEVELOPMENT

Included Journals:SCIE、SSCI、Scopus

Volume:141

Issue:4

ISSN No.:0733-9488

Key Words:Urban transportation planning; Induced traffic; Elasticity model; Panel data

Abstract:The induced traffic model is an essential component of travel demand analysis, which has been primarily researched with elasticity models in the United States and U.K. This paper aims to find the most suitable elasticity model based on the set of panel data regarding annual observations of 30 cities and provinces across China (except Chongqing city) for the years 1990 to 2010. To derive the ideal elasticity model, several basic elasticity models are included; among them are the elasticity-based model, distributed lag model, growth model, and fixed-effect model. Advanced elasticity models, such as the three stages of least squares (3SLS) are also discussed. According to relative researches and data collection, any increase of passenger kilometers of transport (PKT) with the growth of lane kilometers is considered induced traffic and is routinely used as such in this paper. Lane kilometers in China are found to have a statistically significant relationship with PKT measurements of approximately 0.026-0.274 in the short term and 0.367-0.773 in the long term. Population and gross regional product (GRP) numbers are also considered in basic elasticity models. Based on the detailed analysis of empirical results, the 3SLS is judged as the best suitable model for China. It can reflect the time effect, consider endogenous variables (including congestion and vehicle stock), and eliminate the simultaneity bias. (C) 2014 American Society of Civil Engineers.

Pre One:Determining Optimal Strategies for Single-Line Bus Operation by Means of Smartphone Demand Data

Next One:Impact of Transportation Convenience, Housing Affordability, Location, and Schooling in Residence Choice Decisions