Abstract Data science in transportation networks (DSTNs) refers to using diverse types of spatio-temporal data for various transportation tasks, including pattern analysis, traffic prediction, and traffic controls. Graph neural networks (GNNs) are essential in many DSTN problems due to their capability to represent spatial correlations between entities. Between 2016 and 2024, the notable applications of GNNs in DSTNs have extended to multiple fields, such as traffic prediction and operation. However, existing revi…

Data mining in transportation networks (DMTNs) refers to using diverse types of spatio-temporal data for various transportation tasks, including pattern analysis, traffic prediction, and traffic controls. Graph neural networks (GNNs) are essential in many DMTN problems due to their capability to represent spatial correlations between entities. Between 2016 and 2024, the notable applications of GNNs in DMTNs have extended to multiple fields such as traffic prediction and operation. However, existing reviews have pr…

Abstract The progression toward automated driving and the latest advancement in vehicular networking have led to novel and natural human-vehicle-road systems, in which affective human-vehicle interaction is a crucial factor affecting the acceptance, safety, comfort, and traffic efficiency of connected and automated vehicles (CAVs). This development has inspired increasing interest in how to develop affective interaction framework for intelligent cockpit in CAVs. To enable affective human-vehicle interactions in CA…

The preprocessing and main analysis codes (Python scripts) are summarized in this directory. All results in the technical verification section can be copied using these scripts. For more details, please read the instructions in the README.md file.

40 sub-folders are further divided in this directory, each sub-folder contains the data of body gesture per participant. These sub-folders were named after the participant ID and include multiple MP4 files. Each MP4 files file corresponds to valid emotional driving. The body gesture data of a participant’s emotional driving record is shown in the Fig.5.