Urban transport and logistics are generally characterised as the range of activities involved in routing the incoming and outgoing distribution of goods and passengers within an urban area, while reducing congestion by providing the highest quality with minimal travel cost and time to satisfy the ultimate needs of customers. There has been a significant body of research on making urban transportation more efficient, however, significant challenges remain. These challenges, such as increased travel demand for freight and passengers, must be addressed to support the deployment of well-known traditional transportation planning models in terms of computational efficiency and calibration.

The use of advanced technologies brings various large-scale multi-source data sets that hold great potential for improving traditional models, theories, and transportation systems. In addition, motivated by the emergence of new technologies such as the Internet of Things (IoT), connected and automated vehicles, shared automated mobility services, and smart cities, data-driven optimisation has become a flexible approach for modelling traffic dynamics. There is a rising need to understand the implications and possibilities for improved traffic management and to find creative ways and resources for maximising traffic quality and equity.

This Special Issue aims to lay the groundwork for state-of-the-art urban transport with simulation, optimisation, and data analytics in the fields of big data, smart cities, and smart logistics. We support the submission of outstanding research papers on new applications and methods for incorporating emerging technologies into data-driven optimisation, big data analytics, large-scale traffic simulation, and real-world case studies.

Potential topics include but are not limited to the following:

• Data-driven solutions to urban transportation problems
• Digital transformation of urban logistics
• Urban mobility modelling and analysis
• Smart city logistics
• Incorporation of emerging technologies