Multimodal Transport Optimization from Doorstep to Airport Using Mixed-Integer Linear Programming and Dynamic Programming

Evangelos D. Spyrou (), Vassilios Kappatos, Maria Gkemou and Evangelos Bekiaris
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Evangelos D. Spyrou: Hellenic Institute of Transport, Centre for Research and Technology Hellas, Thermi, 57001 Thessaloniki, Greece
Vassilios Kappatos: Hellenic Institute of Transport, Centre for Research and Technology Hellas, Thermi, 57001 Thessaloniki, Greece
Maria Gkemou: Hellenic Institute of Transport, Centre for Research and Technology Hellas, Thermi, 57001 Thessaloniki, Greece
Evangelos Bekiaris: Hellenic Institute of Transport, Centre for Research and Technology Hellas, Thermi, 57001 Thessaloniki, Greece

Sustainability, 2025, vol. 17, issue 17, 1-15

Abstract: Efficient multimodal transportation from a passenger’s doorstep to the airport is critical for ensuring timely arrivals, reducing travel uncertainty, and optimizing overall travel experience. However, coordinating different modes of transport—such as walking, public transit, ride-hailing, and private vehicles—poses significant challenges due to varying schedules, traffic conditions, and transfer times. Traditional route planning methods often fail to account for real-time disruptions, leading to delays and inefficiencies. As air travel demand grows, optimizing these multimodal routes becomes increasingly important to minimize delays, improve passenger convenience, and enhance transport system resilience. To address this challenge, we propose an optimization framework combining Mixed-Integer Linear Programming (MILP) and Dynamic Programming (DP) to generate optimal travel routes from a passenger’s location to the airport gate. MILP is used to model and optimize multimodal trip decisions, considering time windows, cost constraints, and transfer dependencies. Meanwhile, DP allows for adaptive, real-time adjustments based on changing conditions such as traffic congestion, transit delays, and service availability. By integrating these two techniques, our approach ensures a robust, efficient, and scalable solution for multimodal transport routing, ultimately enhancing reliability and reducing travel time variability. The results demonstrate that the MILP solver converges within 20 iterations, reducing the objective value from 15.2 to 7.1 units with an optimality gap of 8.5%; the DP-based adaptation maintains feasibility under a 2 min disruption; and the multimodal analysis yields a total travel time of 9.0 min with a fare of 3.0 units, where the bus segment accounts for 6.5 min and 2.2 units of the total. In the multimodal transport evaluation, DP adaptation reduced cumulative delays by more than half after disruptions, while route selection demonstrated balanced trade-offs between cost and time across walking, bus, and train segments.

Keywords: multimodal; MILP; dynamic programming; travel; airport (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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