Manuscript Topics

We have been witnessing the remarkable evolution and progress of urban transportation systems over the past few decades, and now we are experiencing a new era of intelligent, connected, autonomous, electric, sustainable, and reliable transportation systems empowered by artificial intelligence (AI) techniques. Recent developments in AI have contributed to significant advances in urban transportation systems; for example, AI-based techniques have proven their powerful ability in travel demand prediction, traffic state prediction, traffic signal control, and autonomous driving, just to name a few. The goal of this special issue is to address the challenges and opportunities in modeling, planning, optimization, and management of urban transportation systems with state-of-the-art AI-enabled solutions, and provide an academic platform to publish high-quality research papers on the applications of innovative data-driven approaches and machine learning (ML) algorithms, particularly deep learning (DL) and reinforcement learning (RL), to urban transportation systems. Potential topics include but are not limited to:

• Modeling and optimization of urban transportation systems
• Ride-sourcing and ride-sharing services in urban transportation systems
• Public/railway transportation and demand responsive transportation systems
• Bike-sharing systems and electric bike-sharing systems
• Electrification in urban transportation systems
• Traffic flow modeling with mixed human-driven vehicles and connected automated vehicles
• Innovative parking management systems with intelligence
• Mobility-as-a-service (MaaS) and smart mobility solutions in urban transportation
• Multi-source big data analytics in road safety
• Interpretable ML/DL/RL in urban transportation systems
• Other intelligence-enabled urban transportation systems

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