Data From: “Development of Intelligent Multimodal Traffic Monitoring using Radar Sensor at Intersections”
Intelligent transportation systems (ITS) significantly change our communities by improving the safety and convenience of people’s daily mobility. The system relies on multimodal traffic monitoring, that needs to provide reliable, efficient and detailed traffic information for traffic safety and planning. Signalized traffic intersections are critical spots for collecting such mix-traffic data because the most conflicts and crash occurrences involve multiple transportation modes, such as pedestrians, bicyclists, motorcyclists, and cars. How to reliably and intelligently monitor intersection traffic with multimodal information is one of the most critical topics in intelligent transportation research.
Based on our recent study using mmWave radar to differentiate human behaviors, this proposal will investigate a low-cost, low-weight, compact size, and reliable monitoring platform. This platform that incorporates mmWave radar and the machine learning technique to collect multimodal traffic data at intersections is robust to light and adverse weather conditions. The products of this project consist of
1) a prototype of the proposed multimodal traffic monitoring platform using mmWave radar, 2) the real-world experimental dataset collected by the platform for multimodal traffic, and 3) a demo platform at a road intersection to illustrate the performance in terms of measuring multimodal traffic counts, speeds, and directions.
This research is highly matched with the NITC’s sub-themes for the goal of improving multi-modal planning and shared use of infrastructure. Our primary goal is to improve multimodal traffic monitoring at intersections. The proposed platform can play an important role in providing a reliable and accurate city-wide traffic network. In addition, the outcome of this research can provide useful insight into advanced innovations technologies for developing equitable, healthy, and sustainable communities and smart cities.
Siyang Cao & Yao-Jan Wu. Data From Development of Intelligent Multimodal Traffic Monitoring using Radar Sensor at Intersections” NITC-1296. Portland, OR: Transportation Research and Education Center (TREC), 2021. "https://doi.org/10.15760/TREC_datasets.15https://doi.org/10.15760/TREC_datasets.18
video - detecting one vehicle
NITC1296Data2.mp4 (3185 kB)
video - detecting one pedestrian
NITC1296Data3.mp4 (5565 kB)
video - detecting two pedestrians
NITC1296Data4.stl (8755 kB)
CAD file - top framework of the system prototype
NITC1296Data5.stl (1059 kB)
CAD file - bottom framework of the system prototype
NITC1296Data6.rviz (4 kB)
ROS configuration file - clustering algorithm results
NITC1296Data7.rviz (5 kB)
ROS configuration file - raw recordings