Multimodal transportation is defined as a modern transport mode formed by multiple transport modes. The world is in a period of rapid development of multimodal transportation integration, and regional integrated transportation network is gradually developing and improving. This mode combines the advantages of various modes of transportation, which can greatly reduce the costs arising from transportation.

Therefore, the development of multimodal transport has become a necessary way to transform and upgrade the economic structure of transportation, an inevitable trend to improve logistics efficiency, and an inevitable choice to promote the development of low-carbon transportation. Travel behavior theory is one of the most important theories in traffic planning and management. By combining qualitative and quantitative methods, it can provide support for the prediction and analysis of traffic demand, to effectively formulate policies for traffic control, management, and construction, which is a quite active innovation field. In the context of multimodal transportation, residents' travel behavior will change greatly, and travel demand analysis will be more complex. At present, with the development of computer technology, the technologies related to intelligent transportation have been widely used, such as deep learning (DL), reinforcement learning (RL), artificial intelligence (AI), data mining (DM) and autonomous vehicles (AV). These emerging technologies provide new ideas for traditional traffic problems.

Therefore, how effectively using emerging technologies to solve the problem of travel behavior analysis in the context of multimodal transport is an important topic. This Special Issue welcomes original research and review articles which explore this.

Potential topics include but are not limited to the following:

• Analysis of travel behavior in the context of mobile as a service (MAAS)
• Multimodal transportation mode chain modelling
• Route choice modeling considering observable heterogeneity
• Route choice modeling for emerging travel modes
• Traffic situation awareness and deduction
• Optimization of large-scale multimodal transportation
• Optimization of urban transportation system management
• Dynamic discrete choice modelling
• Data-driven traffic behavior modelling
• Supervised / unsupervised learning in traffic behavior modeling