Transit Connectivity Evaluation of Hub Airports Considering Passenger Path Choice and Air–Rail Intermodality
Source abstract
Abstract
Transit connectivity is a critical indicator for evaluating the transfer efficiency and network performance of hub airports within integrated transport systems. However, conventional connectivity models primarily rely on flight frequency and schedule coordination, while passenger path choice behavior and multimodal competition effects are often overlooked. To address this limitation, this study develops an enhanced transit connectivity evaluation framework that incorporates passenger path choice preferences and air–rail intermodal effects. A novel air–rail intermodal gain coefficient is introduced to capture the context-dependent interplay between aviation and high-speed rail, quantifying synergistic effects when HSR complements air transfer and substitution effects when it competes with it. The proposed model integrates direct transfer connectivity (Cd) and indirect transfer connectivity (Cind) within a unified quantitative framework, embedding transfer time compliance and detour factor constraints to improve behavioral realism and operational applicability. A case study of Xi’an Xianyang International Airport demonstrates that the introduction of the intermodal gain mechanism increases overall transit connectivity from 3606.3 to 3664.1, with the gain concentrated in the 500 to 800 km distance band where HSR journey times are most competitive with door-to-door air travel. The results reveal strong polarization in direct transfer connectivity and the limited effectiveness of indirect transfer routes due to transfer time constraints. The proposed framework offers a replicable assessment tool for hub airport network connectivity and multimodal transport planning, with potential for broader application across hub airports operating within integrated air–rail networks.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Aviation Industry Analysis and Trends · Air Traffic Management and Optimization · Transportation and Mobility Innovations
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Air Transportation, Shanghai University of Engineering Science, Shanghai 201620, China
References (31)
Show source-provided references
1. A connectivity-based methodology for new air route identification10.1016/j.tra.2023.103715 ↗
2. Cheng, K. (2012). Research on the Evolution Mechanism of China’s Route Network Based on Link Prediction. [Master’s Thesis, Nanjing University of Aeronautics and Astronautics].
3. New centrality and causality metrics assessing air traffic network interactions10.1016/j.jairtraman.2020.101801 ↗
4. Pastorino, L., and Zanin, M. (2023). Local and network-wide time scales of delay propagation in air transport: A Granger causality approach. Aerospace, 10.10.3390/aerospace10010036 ↗
5. The worldwide air transportation network: Anomalous centrality, community structure, and cities’ global roles10.1073/pnas.0407994102 ↗
6. Connectivity in air transport networks10.3828/jtep.2013.47.1.35 ↗
7. Flight schedule optimization considering transfer time and transfer service preferences of connecting passengers
8. Modeling transfer behavior of connecting passengers between urban hubs
9. A model of intercity passenger travel mode choice behavior for medium and long distances: A case study of high-speed rail and civil aviation
10. Flight schedule optimization at hub airports considering connecting passengers
11. Measuring onward connectivity with a prescriptive approach10.1177/03611981221123245 ↗
12. A study on flight time management methods aimed at reducing actual transfer time
13. Optimization of flight transfer itineraries based on reducing passenger delays
14. Zhou, F., Yao, J., and Yin, H. (2025). Identifying the Passenger Transport Corridors in an Urban Rail Transit Network Based on OD Clustering. Sustainability, 17.10.3390/su17209127 ↗
15. Du, Z., Yang, W., Yin, Y., Ma, X., and Gong, J. (2024). Improved Long-Term Forecasting of Passenger Flow at Rail Transit Stations Based on an Artificial Neural Network. Appl. Sci., 14.10.3390/app14073100 ↗
16. Fan, Q., Yu, C., and Zuo, J. (2025). The Nonlinear Causal Effect Estimation of the Built Environment on Urban Rail Transit Station Flow Under Emergency. Sustainability, 17.10.3390/su17135829 ↗
17. Access to and competition between airports: A case study for the San Francisco Bay area10.1016/S0965-8564(02)00007-1 ↗
18. Airport, airline and access mode choice in the San Francisco Bay area10.1111/j.1435-5957.2006.00097.x ↗
19. Air–Rail Connectivity Index: A comprehensive study of multimodal journeys
20. Jiang, F., Wang, L., and Huang, S. (2022). Analysis of the transfer time and influencing factors of air-rail integration passengers: A case study of Shijiazhuang Zhengding International Airport. Sustainability, 14.10.3390/su142316193 ↗
21. Yang, Y., Xie, X., and Wang, Z. (2024). Optimization of high-speed rail express transportation scheduling based on spatio-temporal-state network modeling. Appl. Sci., 14.10.3390/app142210456 ↗
22. Optimization of crew routing plan for high-speed railway based on spatio-temporal state network
23. Structural characteristics of a two-layer weighted air-rail intermodal network based on a multi-layer network
24. A hub route network optimization model for air-rail intermodal transport
25. Design of one-way ‘rail-to-air’ connection schemes for air-rail intermodal transport
26. Wang, Y., Li, Q., Han, Z., and Zhang, J. (2025). Coordinated Optimization of Feeder Flex-Route Transit Scheduling for Urban Rail Systems. Appl. Sci., 15.10.3390/app15084342 ↗
27. Airline and railway integration10.1016/j.tranpol.2006.02.001 ↗
28. Air–rail intermodal agreements: Balancing the competition and environmental effects10.1016/j.jairtraman.2012.01.012 ↗
29. Measurement of the substitution effect of high-speed rail on domestic air passenger transport
30. Share shift and airport substitution in origin-destination markets with low-cost entrants10.1504/IJRM.2008.019794 ↗
31. Du, Q. (2020). Research on the Measurement of Connectivity Capacity of Regional Airports Considering Passenger Transfer. [Master’s Thesis, Civil Aviation University of China].