Transportation Systems Research

The Traffic Optimization for Signalized Corridors system combines cooperative adaptive cruise control with eco-approach and departure strategies. Researchers evaluate the vehicle and infrastructure architecture in simulation to study mobility, environmental benefits, and deployment risks.

Lead faculty: Guoyuan Wu and Matthew Barth.

Researchers developed a cooperative ramp-merging prototype using the Unity game engine, vehicle-to-cloud communication, and field experiments near CE-CERT. The work connects simulation with real vehicles to evaluate connected transportation applications.

Lead researcher: Guoyuan Wu.

This work models and evaluates connected vehicle applications such as lane-speed monitoring, emergency brake alerts, lane-hazard prediction, and traffic-jam prevention using roadway networks and microscopic traffic simulation.

Research team included Guoyuan Wu, Kanok Boriboonsomsin, Matthew Barth, and Mike Todd.

Researchers connect vehicle emissions model estimates with near-roadway measurements by studying how gasoline exhaust changes during dilution and cooling. The work supports improved estimation of particulate matter near heavily traveled roads.

Lead researchers included Matthew Barth and collaborating air quality faculty.

This study combines real-time traffic characteristics, weather, and ambient air quality data to quantify how congestion contributes to near-road pollution and to develop models that support transportation and public health decisions.

Lead researcher: Guoyuan Wu, with collaborating air quality researchers.

Researchers develop and apply tools for microscopic traffic simulation, multi-agent modeling, vehicle simulation, virtual reality, safety assessment, and emissions modeling to evaluate connected and automated transportation technologies.

Lead faculty included Matthew Barth, Kanok Boriboonsomsin, and Guoyuan Wu.

The Comprehensive Modal Emissions Model estimates second-by-second fuel use and tailpipe emissions across light-duty and heavy-duty vehicle technologies and operating conditions.

Lead faculty included Matthew Barth and Kanok Boriboonsomsin.

CE-CERT adapted its emissions modeling capability to operate onboard and in real time using information from a vehicle engine control module, creating a lower-cost platform for estimating energy use and emissions during operation.

Lead researcher: Matthew Barth, with collaborating CE-CERT faculty.

This project connects large-scale simulation with real-world autonomous vehicle deployment. It combines high-performance computing, edge artificial intelligence, communication systems, and vehicle platforms to support cooperative driving.

Lead researcher: Hang Qiu.

Researchers integrate intelligent powertrain control, vehicle dynamics, LiDAR-enabled mapping, and crowdsourced data to improve how vehicles perceive road conditions and make efficient operating decisions.

Research team: CE-CERT Transportation Systems Research.

Following the EcoCAR EV Challenge, UCR’s reengineered Cadillac LYRIQ returned to CE-CERT as a working platform for research in electric vehicle systems, connected and automated technologies, and energy-efficient transportation.

Research and student training platform: UCR EcoCAR and CE-CERT.

This research combines real-world and simulated data to evaluate how connected and automated vehicles and shared mobility may affect travel behavior, traffic performance, vehicle miles traveled, and energy efficiency across California.

Lead researchers included Matthew Barth and Guoyuan Wu.

The project develops an analysis framework for shared bikes, e-bikes, and e-scooters, examining system benefits, safety, asset management, equity, and opportunities for coordinated public-agency management.

Research team: CE-CERT Transportation Systems Research.

Researchers built a small-scale connected and automated vehicle environment using onboard computers, LiDAR, cameras, robot operating systems, and vehicle-to-vehicle communications to test cooperative driving in freeway and urban scenarios.

Faculty advisors included Guoyuan Wu, Kanok Boriboonsomsin, and Matthew Barth.

This work uses aerial imagery, digital roadway networks, and machine learning to identify existing and missing sidewalks and create connected sidewalk inventories more efficiently.

Lead researcher: Guoyuan Wu, with collaborating faculty.

Researchers use traffic microsimulation to evaluate alternative high-occupancy vehicle lane configurations that improve throughput, reduce delay, and support more efficient freeway operations.

Lead researcher: Kanok Boriboonsomsin.

This project evaluates the technical potential for hybrid and fully electric construction and agricultural equipment to reduce fossil fuel use and emissions.

Lead researchers: Kanok Boriboonsomsin and Guoyuan Wu.

Researchers evaluate energy and emissions benefits from battery-electric and plug-in hybrid trucks combined with connected vehicle applications such as eco-approach and departure.

Research team included Kanok Boriboonsomsin and Alexander Vu.

CE-CERT and regional partners deployed connected traffic signals near the Port of Los Angeles to provide real-time signal information and support eco-driving applications on freight corridors.

Lead faculty: Kanok Boriboonsomsin, Guoyuan Wu, and Matthew Barth.