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Appl. Sci. 2017, 7(11), 1102; https://doi.org/10.3390/app7111102

The Dynamic Optimization of the Departure Times of Metro Users during Rush Hour in an Agent-Based Simulation: A Case Study in Shenzhen, China

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1,2,* , 3
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1
School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China
2
Key Laboratory of Geographic Information Systems, Ministry of Education, Wuhan University, Luoyu Road 129, Wuhan 430079, China
3
College of Architecture and Urban Planning, Shenzhen University, Nanhai Street 3688, Nanshan District, Shenzhen 518061, China
4
Department of Geography, Kent State University, Kent, OH 44242, USA
*
Author to whom correspondence should be addressed.
Received: 13 September 2017 / Accepted: 18 October 2017 / Published: 25 October 2017
View Full-Text   |   Download PDF [3726 KB, uploaded 25 October 2017]   |  

Abstract

As serious traffic problems have increased throughout the world, various types of studies, especially traffic simulations, have been conducted to investigate this issue. Activity-based traffic simulation models, such as MATSim (Multi-Agent Transport Simulation), are intended to identify optimal combinations of activities in time and space. It is also necessary to examine commuting-based traffic simulations. Such simulations focus on optimizing travel times by adjusting departure times, travel modes or travel routes to present travel suggestions to the public. This paper examines the optimal departure times of metro users during rush hour using a newly developed simulation tool. A strategy for identifying relatively optimal departure times is identified. This study examines 103,637 person agents (passengers) in Shenzhen, China, and reports their average departure time, travel time and travel utility, as well as the numbers of person agents who are late and miss metro trips in every iteration. The results demonstrate that as the number of iterations increases, the average travel time of these person agents decreases by approximately 4 min. Moreover, the latest average departure time with no risk of being late when going to work is approximately 8:04, and the earliest average departure time with no risk of missing metro trips when getting off work is approximately 17:50. View Full-Text
Keywords: dynamic optimization; departure times; metro; rush hour; agent-based simulation; Shenzhen dynamic optimization; departure times; metro; rush hour; agent-based simulation; Shenzhen
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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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Xi, Y.; Du, Q.; He, B.; Ren, F.; Zhang, Y.; Ye, X. The Dynamic Optimization of the Departure Times of Metro Users during Rush Hour in an Agent-Based Simulation: A Case Study in Shenzhen, China. Appl. Sci. 2017, 7, 1102.

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