Data From: “Development of Intelligent Multimodal Traffic Monitoring using Radar Sensor at Intersections”

Authors

Siyang Cao, The University of Arizona
Yao-Jan Wu, The University of Arizona

Document Type

Dataset

Publication Date

2021

Subjects

Traffic safety, Intelligent sensors, Urban transportation -- Planning, Traffic -- Monitoring, Urban transportation

Abstract

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.

Description

These data support a final report published on NITC’s website “Development of Intelligent Multimodal Traffic Monitoring using Radar Sensor at Intersections (2021)”

The computer-aided design (CAD) files are used to manufacture the 3D framework for making the radar system prototype’s. The Robotic Operating System (ROS) [1] configurations are used for rendering the algorithm test results against the raw recordings. And the video files are presentations of the analyzed data that shows the system prototypes’ capability of detecting pedestrians and vehicle.

For more raw experimental data that has been collected in various traffic scenarios, which is not required to be included here, please refer to https://github.com/radar-lab/traffic_monitoring.

• NITC1296 Data1.mp4 -The video presentation of the post processed results that shows the system prototype’s capability of detecting one moving vehicle

• NITC1296Data2.mp4 - The video presentation of the post processed results that shows the system prototype’s capability of detecting one walking pedestrian

• NITC1296Data3.mp4 - The video presentation of the post processed results that shows the system prototype’s capability of detecting two moving pedestrians

• NITC1296Data4.stl - The CAD file of the top framework of the system prototype

• NITC1296Data5.stl - The CAD file of the bottom framework of the system prototype

• NITC1296Data6.rviz - The ROS configuration file that is used for showing clustering algorithm results

• NITC1296Data7.rviz - The ROS configuration file that is used for showing raw recordings

Rights

This work is marked with CC0 1.0 Universal

DOI

10.15760/TREC_datasets.18

Persistent Identifier

https://archives.pdx.edu/ds/psu/36945

Recommended Citation

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.18