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

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 the authors' 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 National Institute for Transportation and Communities' (NITC’s) sub-themes for the goal of improving multi-modal planning and shared use of infrastructure. The 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.

  • Record URL:
  • Dataset URL:
  • Summary URL:
  • Supplemental Notes:
    • The dataset supports report: Development of Intelligent Multimodal Traffic Monitoring using Radar Sensor at Intersections, available at the URL above. This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
  • Corporate Authors:

    University of Arizona, Tucson

    Department of Civil & Architectural Engineering & Mechanics
    Tucson, AZ  United States 

    National Institute for Transportation and Communities

    Portland State University
    P.O. Box 751
    Portland, OR  United States  97207

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Authors:
  • Publication Date: 2021

Language

  • English

Media Info

  • Media Type: Dataset
  • Dataset publisher:

    PDX Scholar

    Portland State University
    Portland, Oregon  United States 

Subject/Index Terms

Filing Info

  • Accession Number: 01838704
  • Record Type: Publication
  • Contract Numbers: NITC 1296
  • Files: UTC, TRIS, ATRI, USDOT
  • Created Date: Mar 16 2022 10:19AM