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Safety and Security

Using Lighting to Alter Driver Behavior

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.

Enhancing Safety for Vulnerable Road Users: A Data-Driven and Community-Focused Approach

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.

Exploring Drive-by Sensing for Urban Truck Characterization using Google Street View Imagery

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

Investigation of Emerging Sensing and AI/ML Technologies to Enhance the Safety of Vulnerable Roadway Users at Signalized Intersection

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).

Protection Technologies for Bridges Against Overheight Impacts

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.  

Connected and autonomous systems Safety and security

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.

Dr. Alain Kornhauser

Dr. Alain Kornhauser is Professor of Operations Research & Financial Engineering at Princeton University. He studied Aerospace Engineering at Penn State earning a BS and MS and Princeton, earning a PhD. In 1971 he joined the Aerospace Engineering faculty at U of Minnesota where he applied automation, network analysis and optimal control to the design of Personal Rapid Transit (PRT) Systems. He returned to Princeton in 1972 extending his pivotal work to more conventional forms of transportation. In 1979 he founded ALK Technologies, Inc.

Performance Evaluation of Asphalt Mixtures Statewide

Currently, asphalt mixtures are design using volumetric concepts to determine optimum asphalt content levels with no means of verifying mixture performance prior to field production and placement. A new design methodology called Balanced Mixture Design (BMD) promotes the use of evaluating and design asphalt mixture using rutting and fatigue cracking methods and criteria to achieve an optimum asphalt content that will result in an asphalt mixture performing well in rutting and fatigue cracking scenarios – thereby “balancing” the asphalt mixture performance.

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