A Travel Behavior-Based Skip-Stop Strategy Considering Train Choice Behaviors Based on Smartcard Data

This study analyzes a skip-stop strategy considering four types of train choice behavior with smartcard data. The proposed model aims to minimize total travel time with realistic constraints such as facility condition, operational condition, and travel behavior. The travel time from smartcard data i...

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Bibliographic Details
Published inSustainability Vol. 11; no. 10; p. 2791
Main Authors Lee, Eun Hak, Lee, Inmook, Cho, Shin-Hyung, Kho, Seung-Young, Kim, Dong-Kyu
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 16.05.2019
Subjects
Behavior
Data envelopment analysis
Data points
Economic models
Efficiency
Energy consumption
Engineering
Environmental engineering
Genetic algorithms
Parameter estimation
Passengers
Public transportation
Railroads
Railway stations
Smart cards
Stochasticity
Studies
Sustainability
Traffic assignment
Traffic congestion
Trains
Transfer stations
Transportation services
Travel
Travel time
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Summary:This study analyzes a skip-stop strategy considering four types of train choice behavior with smartcard data. The proposed model aims to minimize total travel time with realistic constraints such as facility condition, operational condition, and travel behavior. The travel time from smartcard data is decomposed by two distributions of the express trains and the local trains using a Gaussian mixture model. The utility parameters of the train choice model are estimated with the decomposed distribution using the multinomial logit model. The optimal solution is derived by a genetic algorithm to designate the express stations of the Bundang line in the Seoul metropolitan area. The results indicate the travel times of the transfer-based strategy and the high ridership-based strategy are estimated to be 21.2 and 19.7 min/person, respectively. Compared to the travel time of the current system, the transfer-based strategy has a 5.8% reduction and the high ridership-based strategy has a 12.2% reduction. For the travel behavior-based strategy, the travel time was estimated to be 18.7 minutes, the ratio of the saved travel time is 17.9%, and the energy consumption shows that the travel behavior-based strategy consumes 305,437 (kWh) of electricity, which is about 12.7% lower compared to the current system.
ISSN:2071-1050
2071-1050
DOI:10.3390/su11102791