Pedestrian Behavior Study to Advance Pedestrian Safety in Smart Transportation Systems Using Innovative LIDAR Sensors

Taylor Li, University of Texas Arlington


  • Sirisha Kothuri, Portland State University
  • Xianfeng (Terry) Yang, University of Utah


Pedestrian safety is critical to improving walkability. Although walking trips have increased in the last decade, pedestrian safety remains a top concern. In 2018, 6,283 pedestrians were killed in traffic crashes representing the most deaths since 1990 (NHTSA). Approximately 20% of these occur at signalized intersections, where a variety of modes converge leading to increased propensity of conflicts. Current signal timing and detection technologies are heavily biased towards vehicular traffic, often leading to higher delays and insufficient walk times for pedestrians, which could result in risky behaviors such as noncompliance. Current detection systems for pedestrians at signalized intersections consist primarily of pushbuttons. Limitations include the inability to provide feedback to the pedestrian that they have been detected especially with older devices and not being able to dynamically extend the walk times if the pedestrians fail to clear the crosswalk. Smart transportation systems play a vital role in enhancing the mobility and safety and provide innovative techniques to connect pedestrians, vehicles and infrastructure. Most research on smart and connected technologies have focused on vehicles, however there is a critical need to harness the power of these technologies to study pedestrian behavior as pedestrians are the most vulnerable users of the transportation system. While a few studies have used location technologies to detect pedestrians, this coverage is usually small and favors people with smartphones. However, the transportation system must consider a full spectrum of pedestrians and accommodate everyone. 

The aim of this research proposal is to investigate the pedestrian behavior at signalized intersections using the state-of-the-art LIDAR sensing technologies and to use this data along with vehicular data to develop a more effective multimodal signal control system. Data on pedestrian behaviors such as distractions or walking speed distribution when crossing intersections are especially beneficial to improving pedestrian safety. In limited previous tests, the state-of-the-art LIDAR sensors could almost track 100% pedestrians and vehicles with 0.02 second resolution within the scope. In this study, the project team plans to collect pedestrian behavior data at signalized intersections in Texas, Oregon and Utah. The tracked pedestrian trajectories will be synchronized with high-resolution traffic signal event data to examine pedestrian crossing patterns under different scenarios, such as distraction, perception-reaction time to traffic light changes and prevailing crossing speeds. The volume of detected pedestrians using LIDAR systems will also be cross compared with other sensing techniques such as video analytics. The results from this project will be beneficial in enhancing the safety and mobility for pedestrians while also helping to design a more effective multimodal control system.

Project Details

Project Type:
Project Status:
In Progress
End Date:
June 30,2022
UTC Grant Cycle:
NITC 16 Round 4
UTC Funding:

Other Products

  • Wang, P. R., Li, P. F., & Chowdhury, F. R. (2022). Development of an Adaptive Traffic Signal Control Framework for Urban Signalized Interchanges Based on Infrastructure Detectors and CAV Technologies. Journal of Transportation Engineering Part a-Systems, 148(4). doi:10.1061/jtepbs.0000648 (PUBLICATION)
  • Not yet (PRESENTATION)