Data-Driven Vehicle Routing and Driving Optimization with Multi-Source Spatio-Temporal Constraints
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 16 June 2026 | Viewed by 75
Special Issue Editors
Interests: computer vision; machine learning; intelligent driving; intelligent perception based on domestically produced high-performance chips
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; multimedia signal processing
Interests: social robotics; multimodal human–robot interaction; UX design; affective computers; metaphors and creativity
Special Issues, Collections and Topics in MDPI journals
Interests: high-performance nanogenerator development; Schottky sensor performance enhancement; cognitive and brain science research; self-powered devices for myocardial tissue repair
Interests: machine learning; computer vision; natural language processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.
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.
Keywords
- vehicle routing optimization
- spatio-temporal data analytics
- multi-source data fusion
- intelligent transportation systems
- data-driven decision making
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