Intervention analysis of the impact of opening a new railway line on passenger ridership in Seoul

In this study, using public transport smart card data observed in Seoul, Korea, we provide empirical evidence related to the impact of the opening of a new railway line on passenger demand changes at transfer stations. An intervention time series modeling framework was applied to evaluate the travel...

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Bibliographic Details
Published inKSCE journal of civil engineering Vol. 20; no. 6; pp. 2524 - 2534
Main Authors Park, Man Sik, Eom, Jin Ki, Heo, Tae-Young, Song, Jiyoung
Format Journal Article
LanguageEnglish
Published Seoul Korean Society of Civil Engineers 01.09.2016
Springer Nature B.V
대한토목학회
Subjects
Automatic fare collection
Civil Engineering
Demand
Empirical analysis
Engineering
Geotechnical Engineering & Applied Earth Sciences
Impact analysis
Industrial Pollution Prevention
Intervention
Market prices
Passengers
Public transportation
Railway engineering
Railway stations
Railways
Ridership
Smart cards
Transfer stations
Transit
Transportation Engineering
Transportation planning
Travel demand
Trends
토목공학
Korea, Rep., Seoul
passenger ridership
transit planning
intervention
AFC
Online AccessGet full text
ISSN1226-7988
1976-3808
DOI10.1007/s12205-015-1190-6

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Summary:In this study, using public transport smart card data observed in Seoul, Korea, we provide empirical evidence related to the impact of the opening of a new railway line on passenger demand changes at transfer stations. An intervention time series modeling framework was applied to evaluate the travel demand patterns based upon massive card data collected before and after a new line opened. The results show that the new line opening has had meaningful intervention effects on passenger demand, but significant changes were not observed at all stations. This implies that the trends have been changed structurally and the changes may last for a long time. Consequently, it is found that the new line opening changed the trends of passenger ridership and these changes can be successfully explained by various intervention patterns derived from extensive Automatic Fare Collection (AFC) data. The intervention analysis will provide valuable information to transit agencies to enable them to make better decisions related to transit planning, pricing, and the deployment of new transit services.
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G704-000839.2016.20.6.048
ISSN:1226-7988
1976-3808
DOI:10.1007/s12205-015-1190-6