Distribution optimization model for passenger departure via multimodal transit

Abstract

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.