Urban Studies and Planning Commons^™

Development Of Intelligent Multimodal Traffic Monitoring Using Radar Sensor At Intersections, Siyang Cao, Yao-Jan Wu, Feng Jin

TREC Final Reports

Multimodal traffic monitoring is critical for improving mobility and safety at intersections with potential conflicts among various modes of transportation. Traditional traffic monitoring approaches utilizing cameras cannot work reliably during the night and under hazardous weather conditions. We propose to build a new intelligent multimodal traffic monitoring device using the low-cost mmWave radar. The proposed device can reliably distinguish different modes (such as buses, pedestrians, bicyclists, trucks, motorcycles, etc.), and determine the counts, speed, and moving directions of every single target in an urban environment under various lighting and weather conditions. In the study, a low-cost prototype system will also …

New Radar Sensor Technology For Intelligent Multimodal Traffic Monitoring At Intersections, Siyang Cao, Yao-Jan Wu, Feng Jin

TREC Project Briefs

Intelligent Transportation Systems (ITS) need traffic data to run smoothly. At intersections, where there is the greatest potential for conflicts between road users, being able to reliably and intelligently monitor the different modes of traffic is crucial.

The Federal Highway Administration estimates that more than 50 percent of the combined total of fatal and injury crashes occur at or near intersections. For pedestrians the intersection is a particularly dangerous place: the City of Portland, OR identified that two-thirds of all crashes involving a pedestrian happen at intersections. And when darkness comes earlier in fall and winter, crashes increase dramatically. So …

Data-Driven Mobility Strategies For Multimodal Transportation, Yao-Jan Wu, Xianfeng Terry Yang, Sirisha Kothuri, Abolfazl Karimpour, Qinzheng Wang, Jason Anderson

TREC Final Reports

Multimodal transportation systems (e.g., walking, cycling, automobile, public transit, etc.) are effective in increasing people’s travel flexibility, reducing congestion, and improving safety. Therefore, it is critical to understand what factors would affect people’s mode choices. With advanced technology, such as connected and automated vehicles, cities are now facing a transition from traditional urban planning to developing smart cities. To support multimodal transportation management, this study will serve as a bridge to connect speed management strategies of conventional corridors to connected vehicle corridors. This study consists of three main components. In the first component, the impact of speed management strategies along …

Applying Data-Driven Multimodal Speed Management Strategies For Safe, Efficient Transportation, Yao-Jan Wu, Xianfeng Yeng, Sirisha Kothuri

TREC Project Briefs

How can we use a variety of data-driven speed management strategies to make transportation safer and more efficient for all modes–whether you’re driving, walking or taking transit? The project was led by Yao Jan Wu, director of the Smart Transportation Lab at the University of Arizona. Co-investigators were Xianfeng Terry Yang of the University of Utah, who researches traffic operations and modeling along with connected automated vehicles, and Sirisha Kothuri of Portland State University, whose research has focused on improving signal timing to better serve pedestrians. “We want to improve mobility for all users, be it pedestrians, vehicle drivers or …