Stochastic and Data-Driven Methodologies for Next-Generation Transportation Systems: Modeling, Optimization, and Operations

Dear Colleagues,

Rapid advances in automation, electrification, and intelligent mobility are reshaping the way passenger and freight transportation is planned and operated. Services such as robot taxis, autonomous vehicle fleets, and air-based last- and middle-mile transportation supported by vertiports, battery-swapping, and smart-charging infrastructure possess great potential for efficiency, accessibility, and decarbonization. However, these systems face high levels of uncertainty, such as fluctuating demand, stochastic travel and service times, energy price volatility, weather disruptions, and cascading failures across interdependent infrastructures.

This Special Issue aims to advance knowledge on stochastic optimization and AI-driven methodologies for the design and dynamic operation of sustainable future transportation systems. It welcomes research that integrates stochastic programming, distributionally robust and chance-constrained models, Markov decision processes, reinforcement learning, data-driven prediction, and hybrid simulation–optimization frameworks to support adaptive decisions for system design, routing, fleet sizing, refueling scheduling, vertiport siting, and coordinated dispatch across ground-air fleets.

Relevant topics include, but are not limited to, the following:

• Stochastic programming models for the design and operation of robot taxis, autonomous fleets, and eVTOL/urban air mobility services;
• Multi-stage and distributionally robust optimization for sustainable transportation planning;
• Chance-constrained and scenario-based approaches for battery swapping, charging, and vertiport network design;
• AI-enhanced forecasting and reinforcement learning for real-time operational control under uncertainty;
• Hybrid simulation–optimization frameworks for dynamic fleet management and energy scheduling;
• Risk-aware and resilience-oriented planning for extreme weather, demand surges, and energy or infrastructure disruptions;
• Data-driven techniques for adaptive routing in automated fleets.

Dr. Tanveer Hossain Bhuiyan
Dr. Wenquan Dong
Guest Editors

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• dynamic operations
• sustainable transportation
• autonomous vehicles
• multimodal transportation
• stochastic models
• AI-based prediction and decision-making models