Traffic forecasting has been an important research topic in the intelligent transportation system for smooth and safe transportation. Among research directions, spatiotemporal-graph-based traffic forecasting has been known as one of the popular directions. Unfortunately, many requirements, such as lightweight architecture, low resource consumption, and flexible transfer learning, have not had satisfactory answers. Hence, we propose a new method called 3D Spatial Temporal Graph Point-wise Convolutional Network (3D-STGPCN) to address these requirements. Differing from others, we proceed with spatial and temporal information simultaneously (i.e., wrap them into one structure). We evaluate and compare our model on three public traffic network datasets, METR-LA, PEMS-BAY, and STREETS, with different methods in the same domain. The experimental results show the advantage of our model over others. We publish our source code at https://github.com/dophanh-26/3D-STGPCN.

Dashcam video has become popular recently due to the safety of both individuals and communities. While an individual can have undeniable evidence for legal and insurance, communities can benefit from sharing these dashcam videos for further traffic education and criminal investigation. Moreover, relying on recent computer vision and AI development, few companies have launched the so-called AI dashcam that can alert drivers to near-risk accidents (e.g.., following distance detection, forward collision warning), forwarding to improving driver’s safety. However, even though dashcam videos create a driver’s travel log (i.e., traveling diary), little research focuses on creating a valuable and friendly tool to find any incident or event with few described sketches by users. Besides, most incident detection models have been built using a traditional supervised learning approach (i.e., collecting and labeling data for a new incident class). That prevents the quick and customized development of a new incident class. Inspired from these observations, we introduce an interactive incident detection and retrieval system for first-view travel-log data, namely MM-trafficEvent, that can (1) online defined-incident detection, (2) offline fine-grained incident retrieval for both defined and undefined incidents, (3) offline automatically new incident class creating using user’s queries. Moreover, the system gives promising results when being evaluated on several public datasets.