Contact
Mobile
(434) 924-6347
Location
Link Lab 253 (2nd floor of Olsson Hall)
Lab
Traffic Operations Laboratory, PO Box 400747

B. Brian Park

Professor, Civil and Environmental Engineering Professor, Systems and Information Engineering Director, Traffic Operations Laboratory
Contact
Mobile
(434) 924-6347
Location
Link Lab 253 (2nd floor of Olsson Hall)
Lab
Traffic Operations Laboratory, PO Box 400747
Google Scholar UVA Engineering Link Lab Center for Transportation Studies

About

Brian Park is a Professor in the Department of Civil and Environmental Engineering and the Department of Systems and Information Engineering at the University of Virginia. Prior to joining the University of Virginia, he was a Research Fellow at the National Institute of Statistical Sciences and a Post-Doctoral Research Associate at North Carolina State University. Dr. Park received the B.S. and the M.S. from the Hanyang University, Seoul, Korea, and the Ph.D. from the Texas A&M University. 

Dr. Park is a recipient of PTV America Best Paper Award, Outstanding Reviewer Award from the American Society of Civil Engineers, Jack H. Dillard Outstanding Paper Award from the Virginia Transportation Research Council and Charley V. Wootan Award (for best Ph.D. dissertation) from the Council of University Transportation Centers. He is an ASCE ExCEEd teaching fellow.

He is an Editor in Chief of the International Journal of Transportation, an Associate Editor of the American Society of Civil Engineers Journal of Transportation Engineering, Journal of Intelligent Transportation Systems and the KSCE Journal of Civil Engineering, and an editorial board member of the International Journal of Sustainable Transportation. Furthermore, he is a member of TRB (a division of the National Academies) Artificial Intelligence and Advanced Computing Applications Committee, and past chair of Simulation subcommittee of Traffic Signal Systems Committee. He Chaired the Advanced Technologies Committee of ASCE Transportation and Development Institute.

Education

B.S. ​Urban Engineering, Hanyang University

M.S. ​Urban Engineering, Hanyang University

Ph.D. Civil Engineering, ​Texas A&M University

Research Fellow, National Institute of Statistical Sciences

My research develops and implements technology solutions to improve efficiency and safety of surface transportation system.

B. Brian Park Professor

Research Interests

Cybersecurity
Internet of Things
Intelligent Transportation Systems
Smart Buildings/Cities
Human Machine Interface
Computational Statistics and Simulation/Statistical Modeling
Optimization Models and Methods
Machine Learning
Infrastructure Engineering/Transportation Studies

Selected Publications

"Development and Evaluation of Surrounding Vehicle Identification System for Mixed Traffic Cooperative Platooning,” ASCE Journal of Transportation Engineering, in press Z. Mu, M.A. Imran, S.S. Avedisov, and B.B. Park
"A Human-centric Machine Learning based Personalized Route Choice Prediction in Navigation Systems,” Journal of Intelligent Transportation Systems, 2023, Vol. 24, pp. 523-545 Sun, B., L. Gong, J. Shim, K. Jang. B.B. Park, H. Wang and J. Hu
“Utility-based Route Choice Behavior Modeling using Deep Sequential Models,” Journal of Big Data Analytics in Transportation, Vol. 4, 119–133, 2022, https://doi.org/10.1007/s42421-022-00058-3 Dong, G., Y. Kweon, B.B. Park and M. Boukhechba
“Design and Evaluation of a Human-in-the-loop Connected Cruise Control,” IEEE Transactions on Vehicular Technology, vol. 71, no. 8, pp. 8104-8115, Aug. 2022 Chen, Z., B.B. Park, and J. Hu
“Connected Preceding Vehicle Identification for Enabling Cooperative Automated Driving in Mixed Traffic,” ASCE Journal of Transportation Engineering, Vol. 148, No. 5, May 2022 Chen, Z. and B.B. Park
"Development of a Robust Cooperative Adaptive Cruise Control with Dynamic Topology," IEEE Intelligent Transportation Systems Transactions, Vol. 23, May 2022, pp. 4279-4290 L. Cui, Z. Chen, A. Wang, J. Hu, and B.B. Park
"Development of a Robust Cooperative Adaptive Cruise Control with Dynamic Topology," IEEE Intelligent Transportation Systems Transactions, Vol. 23, May 2022, pp. 4279-4290 L. Cui, Z. Chen, A. Wang, J. Hu, and B.B. Park
"Cooperative Adaptive Cruise Control with Unconnected Vehicle in the Loop," IEEE Intelligent Transportation Systems Transactions, IEEE Transactions on Intelligent Transportation Systems, Vol. 23, No. 5, May 2022, pp. 4176-4186 Z. Chen and B.B. Park

Courses Taught

Traffic Operations (CE 5400)
Discrete Event Stochastic Simulation (SYS 6034)
Civil Engineering Systems Analysis (CE 3000)
Transportation Infrastructure Design (CE 3400)
Transportation Safety (CE 6450)
Advanced Integrated Transportation Systems Models (CE 7460)
Integrated Transportation Systems Models (CE 6460)

Awards

The 2014 George N. Saridis Best Transactions Paper Award for Outstanding Research (Published in IEEE T-ITS) 2020
PTV Group Best Paper Award 2016, 2012 and 2008
ASCE Outstanding Reviewer 2010
American Society of Civil Engineers’ Excellence in Civil Engineering Education (ExCEEd) Teaching Fellow 2004
Jack H. Dillard Outstanding Paper Award, Virginia Transportation Research Council 2004
Council of University Transportation Centers C.V. Wootan Award in the Recognition of Outstanding Ph.D. Dissertation 1999

Featured Grants & Projects

Cooperative Platooning in Mixed Traffic of Connected, Automated, and Human-Driven Vehicles This research project will develop cooperative platooning algorithms for mixed traffic of connected automated vehicles and conventional human-driven vehicles. Recent development of connected and automated vehicle technology allows a group of such vehicles to travel closely one after another in a safely manner (known as, cooperative platooning), which greatly improves mobility and energy efficiency. However, when a human-driven vehicle exists within the group, the cohesion of vehicle platoon is not possible, due to uncertain human driver behavior. The novel cooperative platooning algorithms developed in this project will enable connected and automated vehicles to safely follow human-driven vehicles at shorter headways while mitigated traffic disturbances. These algorithms will also be able to assist a human driver in connected-but-not-automated vehicle by complementing human’s imperfect behaviors. As such, the cooperative platooning can be efficiently operated at low market penetration of connected automated vehicles. This research will benefit national economic welfare and public health with improved surface transportation mobility and reduced greenhouse gas emissions. This research will enable multi-disciplinary education and collaboration in transportation engineering, cyber-physical systems, control theory, and mechanical engineering. The research team will encourage participation from diverse and underrepresented groups in the education and research. Connected automated vehicle (CAV) has been enabled to stably travel as a platoon with short headways, which leads to improvements in mobility and energy efficiency. However, it fails to work effectively in mixed traffic where CAVs are interacting with non-CAVs. The goal of this research is to develop and validate Cooperative Adaptive Cruise Control in mixed traffic (CACC-MT) that can safely and efficiently stabilize the mixed traffic including CAV, traditional vehicles and connected human-driven vehicles. CACC-MT makes the CAV capable of performing feedforward control or model predictive control using the received information from a further preceding vehicle, when the immediately preceding vehicle is unconnected. This allows CAV to closely follow an unconnected vehicle. CACC-MT also includes a human-in-the-loop CACC algorithm that enables co-piloting the human driver based on received information from preceding connected vehicle and help the vehicle behave more smoothly and safely in the traffic turbulence. As CACC-MT adopts robust control strategies to handle uncertainties of human driver’s behavior, it ensures cooperative platooning for mixed traffic safely and efficiently without requiring any prior knowledge of the human drivers, even at the early stage of CAV deployment.
Development of Safety Evaluation Technologies and Test-beds for Automated Vehicles In order to determine factors affecting safety of automated vehicles within a virtual testbed, this project develops a PreScan software based testbed and assess safety impacts of sensor accuracies, communication delays, weather conditions, roadway geometries, etc.
Meso Simulation Modeling Guidance To support Virginia Department of Transportation in selecting a proper modeling tool, this project reviews existing off-the-shelf mesoscopic simulation modeling tools including AIMSUN and VISSIM, and evaluates their pros and cons using a case study.
Development and Verification of Signal Operation Algorithms in Local Intersection Network utilizing V2X Communication Infrastructure This project develops and evaluates traffic signal control algorithms under vehicle to vehicle and vehicle to infrastructure communication environment.
Improving Energy Efficiency by Leveraging Connected Vehicle Technology This project develops and evaluates speed harmonization algorithm that minimizes each vehicle's changes in acceleration. Unlike state of the art approach requiring model predictive control.
WiFi Reader for passenger waiting times and origin-destination estimation This research focuses on estimating passenger waiting time at bus stop, and origin-destination by tracking a unique identifier of WiFi device (i.e., smartphone) without requiring user's permission.