Data-Driven Vehicle Routing and Driving Optimization with Multi-Source Spatio-Temporal Constraints

Dear Colleagues,

The rapid development of intelligent transportation systems, IoT sensing, and data-driven logistics has enabled new applications in vehicle routing optimisation. Traditional routing models—often built on static or simplified assumptions—struggle to capture the complexity of real-world constraints, such as dynamic traffic flows, temporal delivery windows, energy limitations, environmental impacts, and stochastic demand patterns.

For this Special Issue, we invite high-quality original research that addresses data-driven methods for vehicle routing and scheduling under multi-source spatio-temporal constraints. We welcome interdisciplinary contributions that integrate operations research, artificial intelligence, data science, and transportation engineering to advance both theory and application.

Topics of Interest

Submissions may include, but are not limited to, the following themes:

• Data-driven and learning-based vehicle routing models;
• Multi-source data fusion for routing and logistics optimisation;
• Spatio-temporal modelling of traffic, demand, and resource availability;
• Dynamic and stochastic vehicle routing with real-time updates;
• Multi-objective optimisation (e.g., cost, time, emissions, service quality);
• Integration of routing with supply chain, mobility-on-demand, or fleet management;
• Applications in urban logistics, public transport, ride-sharing, or autonomous vehicles;
• Reinforcement learning, graph neural networks, and metaheuristic approaches for routing;
• Benchmark datasets, simulation frameworks, and evaluation methodologies.

Dr. Cong Yang
Prof. Dr. John See
Prof. Dr. Bipin Indurkhya
Dr. Luming Zhao
Dr. Zeyd Boukhers
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.