Scope:
In the era of automated and shared mobility, technological developments provide great potentials for advancing the modeling and control of multimodal transport systems. Innovative methodologies, algorithms, and frameworks are necessary to model, optimize, and analyze complex transport systems to improve the efficiency of all modes. Furthermore, novel sensing and new data resources enable applications of data-driven approaches to model and analyze multimodal mobility. The overall objective of this special issue is to collect original and innovative contributions focused on modeling and control methods/tools for supporting model- and data-driven design, monitoring, and management of the next-generation multimodal transport systems. Potential topics include but are not limited to the following categories:
Category one: Novel data-driven and empirical approaches
- Data fusion techniques for sensing and monitoring
- AI-enabled travel behavior analysis and prediction in multimodal environment
- Empirical analysis of traffic flow characteristics and mobility patterns in cities
Category two: Novel modeling approaches and theories
- Traffic flow theories for mixed traffic flow at multiple modeling scales
- Modeling multimodal interactions and operations in shared transportation system
- Modelling uncertainties in multimodal transport system operations
Category three: Novel design, control and optimization techniques
- Advanced real-time control and demand management strategies for large-scale networks
- Advanced traffic flow control with connected and automated vehcles
- Advanced infrastructure and policy designs for future mobility systems
