Data Science and Machine Learning in Logistics and Transport
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".
Deadline for manuscript submissions: 10 January 2025 | Viewed by 11064
Special Issue Editors
Interests: data science; interaction design; service design; intelligent transport systems; logistics; sustainable mobility
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; data mining; machine learning; pattern recognition; simulation; intelligent transport systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, there have been important societal, environmental and economic developments that have posed challenges to the logistics and transport sectors. In terms of logistics, there has been a generalized increase in demand and for faster deliveries, posing challenges to the management of deliveries in urban centers and to the capacity and resilience of supply chains. On the other hand, the transport sector is undergoing important transformations, ranging from changing citizens' mobility habits, increasing offer of multimodal solutions and the emergence of connected and autonomous vehicles.
All of this has been accompanied by a growing digitalization and sensorization of transport and cities that generate large amounts of data on a daily basis. This has led to a paradigm shift where logistics and transport management is increasingly relying on strong data analytics to respond to strategic, tactical and operational planning challenges. For this, data science and machine learning play key roles in extracting meaningful insights, understanding patterns and predicting future trends.
This Special Issue welcomes articles in the areas of data science and machine learning, conveying new advances and developments in theory, modeling, simulation, prediction, testing, case studies, as well as large-scale deployment, with a focus on cutting-edge applications in logistics and transport.
Topics of interest for this Special Issue include, but are not limited to:
- Behaviours and mobility patterns recognition and classification;
- Connected and automated multimodal mobility;
- Innovation in the use of data and machine learning in logistics and transport;
- Intelligent transport systems;
- New business models;
- Innovative hubs;
- Digitalization in logistics and transport;
- E-commerce, ticketing and payment systems;
- Intelligent logistics, transport services and sustainable cities;
- Automation in urban logistics and transport;
- Intelligent, inclusive and cooperative logistics and transport;
- Environmental impacts of intelligent logistics and transport;
- Safety and security in intelligent logistics and transport;
- Intelligent logistics and transport planning and policy for recovery and resilience;
- Simulation in logistics and transport;
- Solutions of data science and machine learning in logistics and transport.
Dr. Marta Campos Ferreira
Prof. Dr. João Manuel R. S. Tavares
Dr. Teresa Galvão Dias
Guest Editors
Manuscript Submission Information
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Keywords
- big data
- data data mining
- data analytics
- artificial intelligence
- deep learning
- pattern recognition
- classification
- simulation
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