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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 858641, 10 pages
Research Article

Distribution Optimization Model for Passenger Departure via Multimodal Transit

Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University School of Transportation Engineering, Tongji University, Room No. A628, Cao’an Road No. 4800, Shanghai 201804, China

Received 30 October 2014; Accepted 9 February 2015

Academic Editor: Francesco Tornabene

Copyright © 2015 Yuchuan Du et al. 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.


International airports in China have become a complex hub between airport and multimodal transit stations. Dissimilar passenger departure demands in different transit mode cause wide gaps among departure times from airport to these modes. In this context, hub managers need to balance the distribution of air passengers to transit modes in order to reduce departure delays and alleviate the congestion in transit stations, even though they cannot change the operating plan of airport or transit stations. However, few research efforts have addressed this distribution. Therefore, we developed a distribution optimization model for passenger departure that minimizes the average departure time and is solved by Genetic Algorithm. To describe differences in passenger choices, without taking into consideration the metropolitan transportation network outside the airport, we introduced the concept of rigid and elastic departures. To reflect the tendency of elastic passengers to choose different transit modes, we assume that the passengers change to other modes in different proportions. A case revealed that the presence of rigid passengers allows managers to partly balance the distribution of passengers and improve the average departure time. When the volume of passengers approaches the peak volume, the optimized distribution significantly improves the departure time.