Vulnerable road users (VRUs)—comprising pedestrians, cyclists, and motorcyclists—account for over half of all global road traffic fatalities. Despite a general decline in traffic deaths, the World Health Organization (WHO) reports an alarming rise in VRU fatalities. This upward trend in VRU fatalities in the United States underscores the urgent need for effective safety measures. According to the National Highway Traffic Safety Administration (NHTSA), 38,824 lives were lost in traffic crashes nationwide in 2020, with a substantial portion being VRUs.

Trucks are vital to urban economies by facilitating the movement of goods and services, but their
presence can significantly affect urban infrastructure, traffic congestion, air quality, and noise pollution.
Effective urban planning and policymaking depend on a comprehensive understanding of truck activity.
However, urban truck data collection remains limited. This study will make the first attempt to address this
urban freight data gap by leveraging drive-by sensing technology, specifically leveraging data from Google

Accurately identifying and analyzing vulnerable roadway users (VRUs) such as pedestrians, bicyclists, and other non-vehicle occupants, are a crucial yet difficult undertaking. VRUs’ behavior is influenced by localized factors such as land use, and their movements are not confined to predefined paths. This study will investigate the use of emerging technologies such as LiDAR, network cameras, and AI/ML algorithms to capture the movements and behaviors of vulnerable road users (VRUs).

Impact by overheight trucks on highways bridges has been identified to be a serious problem by numerous studies in the past, including a detailed study by the PI in 2011.  Most of the countermeasures for preventing impact of trucks on bridges utilize monitoring for truck heights to warn truck drivers.  However, despite these systems being installed, bridges are still being impacted and some bridges suffer serious damage, particularly to fascia girders and decks.  

Safety and traffic flow issues are related to drivers selecting inappropriate speeds when driving. For example, at some curved interchange exit ramps, drivers may go too fast, increasing the risk of rollover crashes, especially for heavy trucks. At other locations, perceived 'bottlenecks' in roadway geometry may cause some drivers to slow down more than is appropriate, resulting in variations among vehicle speeds, and increasing the likelihood of traffic congestion, delays and rear-end crashes.