Capacity-matching model of landside multiple transport modes for large airports considering the impact of carbon emissions
HUANG Ailing1, WANG Zijian1, ZHANG Zhe1, LI Mingjie2, SONG Yue1
1. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China; 2. Department of Information Management, Beijing Daxing International Airport of Capital Airport Group Co., Ltd., Beijing 102600, China
Abstract：[Objective］ In the airport ground-transport system, it is operationally important to match the evacuation requirement of passengers, and the capacity of multimodal transport vehicles is crucial. Numerous studies have investigated single-mode transport capacity allocation; however, research on multimode allocation is scarce. ［Methods］ To mitigate the difficulty in realizing an exact match between the evacuation demand of passengers and the capacity of multimodal transport, a bi-level programming model for multimodal transport resource allocation is proposed according to the analysis of the interaction between capacity allocation and passenger travel choice. A utility function of multiple travel modes, including airport buses, metro, taxis, and private cars, is formulated with the following four features: travel time, travel cost, punctuality, and comfort. The upper-level objective is to minimize the total enterprise-operation cost, passenger-waiting cost, and carbon emission cost for optimizing the headway of public transit and the taxi arrival rate, which is subject to the capacity of each transit mode, the range of each public-transit headway decided by fixed equipment, and the range of taxi arrival rate decided by the capacity of boarding location. Based on the output of the transport capacity allocation scheme used by the upper level, the low-level objective is to assign the passenger flow toward multiple travel modes according to a stochastic user equilibrium-logit model with a utility function. Furthermore, an improved genetic algorithm combined with method of successive algorithm (MSA) is designed to solve the proposed bi-level programming model. To improve the solving efficiency of the algorithm, a pre-search mechanism is proposed, in which the infeasible solution is filtered out using low-precision MSA to reduce the computational cost of repeatedly calling the low-level model. ［Results］ The Beijing Daxing International Airport was considered as a case study to illustrate the efficiency and effectiveness of the proposed bi-level programming model in optimizing transport capacity allocation in airport ground-transport centers. The transport capacity allocation scheme obtained via the proposed model reduced the average passenger-waiting time and the total carbon emission of the system by 14.08% and 6.21%, respectively, while increasing the operation cost by only 1.32%. Moreover, the optimized capacity allocation scheme resulted in the switching of 6.7% of passengers who availed taxis and private cars to buses and metro, which were more environmentally friendly. The proposed solution algorithm could efficiently solve the bi-level model. Under the pre-search mechanism, the generation time of the scheme was 217.6 s, which could meet the production demand within the acceptable time. ［Conclusions］ Results show that the optimized scheme obtained from the bi-level model and algorithms is considerably better than before. The proposed scheme reduces passenger-waiting time and the carbon emissions of the multimodal transport system at a negligible cost. Using the optimized scheme, the organizers of airport ground-transport centers can coordinate the capacities of landside multiple transport modes and guide passengers reasonably. This will reduce operation costs, improve airport landside traffic structure, and encourage green and low-carbon travel.
黄爱玲, 王子吉安, 张哲, 李名杰, 宋悦. 考虑碳排放影响的大型机场陆侧多交通方式运力匹配模型[J]. 清华大学学报（自然科学版）, 2023, 63(11): 1729-1740.
HUANG Ailing, WANG Zijian, ZHANG Zhe, LI Mingjie, SONG Yue. Capacity-matching model of landside multiple transport modes for large airports considering the impact of carbon emissions. Journal of Tsinghua University(Science and Technology), 2023, 63(11): 1729-1740.
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2023, Vol. 63 
