The distribution of crowding costs in public transport: New evidence from Paris
Luke Haywood and
Martin Koning
Transportation Research Part A: Policy and Practice, 2015, vol. 77, issue C, 182-201
Abstract:
Whilst congestion in automobile traffic increases trip durations, this is often not the case in rail-based public transport where congestion rather leads to in-vehicle crowding, often neglected in empirical studies. Using original survey data from Paris, this article assesses the distribution of comfort costs of congestion in public transport. Estimating willingness to pay for less crowded trips at different levels of in-vehicle passenger density we cannot reject a simple linear relationship between crowding costs and density. We apply our results to the cost-benefit analysis of a recent Parisian public transport project.
Keywords: Evaluation of non-market goods; Travel comfort; Crowding costs; Contingent valuation method; Paris subway (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (42)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S096585641500083X
Full text for ScienceDirect subscribers only
Related works:
Working Paper: Estimating Crowding Costs in Public Transport (2013) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:transa:v:77:y:2015:i:c:p:182-201
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01
DOI: 10.1016/j.tra.2015.04.005
Access Statistics for this article
Transportation Research Part A: Policy and Practice is currently edited by John (J.M.) Rose
More articles in Transportation Research Part A: Policy and Practice from Elsevier
Bibliographic data for series maintained by Catherine Liu ().