A Travel Behavior-Based Skip-Stop Strategy Considering Train Choice Behaviors Based on Smartcard Data
| Autor: | Dong-Kyu Kim, Seung-Young Kho, Eun Hak Lee, Shin-Hyung Cho, Inmook Lee |
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| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
Computer science Geography Planning and Development TJ807-830 smartcard data 010501 environmental sciences Management Monitoring Policy and Law TD194-195 01 natural sciences Renewable energy sources Reduction (complexity) greenhouse gas emission travel behavior Transfer (computing) urban railway 0502 economics and business Genetic algorithm skip-stop strategy GE1-350 0105 earth and related environmental sciences Multinomial logistic regression 050210 logistics & transportation Environmental effects of industries and plants Renewable Energy Sustainability and the Environment 05 social sciences Energy consumption Mixture model Environmental sciences Travel behavior Gaussian mixture model Train |
| Zdroj: | Sustainability, Vol 11, Iss 10, p 2791 (2019) Sustainability Volume 11 Issue 10 |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su11102791 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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