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

1
Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Korea
2
Future Transport Policy Research Division, Korea Railroad Research Institute, Uiwang 16105, Korea
3
Institute of Engineering Research, Seoul National University, Seoul 08826, Korea
4
Department of Civil and Environmental Engineering and Institute of Construction and Environmental Engineering, Seoul National University, Seoul 08826, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(10), 2791; https://doi.org/10.3390/su11102791
Received: 23 April 2019 / Revised: 6 May 2019 / Accepted: 9 May 2019 / Published: 16 May 2019
(This article belongs to the Special Issue Sustainability Issues in Public Transport)
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Abstract

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. View Full-Text
Keywords: urban railway; skip-stop strategy; smartcard data; travel behavior; Gaussian mixture model; greenhouse gas emission urban railway; skip-stop strategy; smartcard data; travel behavior; Gaussian mixture model; greenhouse gas emission
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Lee, E.H.; Lee, I.; Cho, S.-H.; Kho, S.-Y.; Kim, D.-K. A Travel Behavior-Based Skip-Stop Strategy Considering Train Choice Behaviors Based on Smartcard Data. Sustainability 2019, 11, 2791.

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