Imperial College London

MrAlexanderBarron

Faculty of EngineeringDepartment of Civil and Environmental Engineering

Associate Director/Head of Metro Benchmarking
 
 
 
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Contact

 

+44 (0)20 7594 3974alexander.barron CV

 
 
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Location

 

607Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Canavan:2018:10.1177/0361198118794547,
author = {Canavan, S and Graham, D and Anderson, R and Barron, A},
doi = {10.1177/0361198118794547},
journal = {Transportation Research Record},
pages = {288--296},
title = {Urban metro rail demand: evidence from dynamic generalised method of moments (GMM) estimates using panel data},
url = {http://dx.doi.org/10.1177/0361198118794547},
volume = {2672},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - This paper estimates elasticities of demand for metro service with respect to fares, income, quality of service, population and network length. Data for 32 world metro systems covering the period from 1996 to 2015 are analysed within a dynamic panel data specification. Three key contributions are made. First, we collate a database for estimation that is more extensive than that used in previous studies. Second, the quality of the data we have available allows us to more accurately represent quality of service than has been possible previously. And lastly, we estimate and compare two different measures of demand. Our analysis finds a statistically significant negative fare elasticity of -0.25 in the long run for a passenger km specified model and -0.4 in the long run for a passenger journeys specified model, and a positive long run income elasticity of 0.17 and 0.18 for the passenger km and passenger journey models respectively. Regarding quality of service we find positive long run elasticities of 0.56 and 0.47 for the passenger km and passenger journey models respectively. Income levels, population and the size of the network are also found to be statistically significant and positive in nature. The results suggest passenger km and passenger journeys will increase more in response to changes in service (here represented by increased capacity) than to changes in fares, with the difference in elasticities of service and fares being more pronounced for passenger km.
AU - Canavan,S
AU - Graham,D
AU - Anderson,R
AU - Barron,A
DO - 10.1177/0361198118794547
EP - 296
PY - 2018///
SN - 0361-1981
SP - 288
TI - Urban metro rail demand: evidence from dynamic generalised method of moments (GMM) estimates using panel data
T2 - Transportation Research Record
UR - http://dx.doi.org/10.1177/0361198118794547
UR - http://hdl.handle.net/10044/1/58889
VL - 2672
ER -