Projects
Ongoing and Past Projects
Smart Infrastructure for Sustainable Roads
Approach-Phase I
September 2024 ~ June 2025
PI: Abolfazl Karimpour, Ph.D.
In phase one of this project, we aim to leverage Virtual Reality (VR) environments to proactively mitigate risks associated with navigating through work zones. The primary objective is to develop and implement a proactive risk mitigation framework utilizing VR technology. This involves monitoring and analyzing driving behaviors, including speeding and subconscious indicators, through VR simulations. By collecting comprehensive behavioral data, such as speed patterns, reaction times, and subconscious prompts, a robust classification system will be established to identify risky driving attributes like fatigue, speeding, and inattention. Through a feedback loop mechanism, personalized messages will be delivered to drivers via Variable Message Signs (VMS) before entering work zones, aiming to mitigate identified risks. The effectiveness of this approach will be validated through extensive VR simulations, allowing for scenario replication and observation of driver behavior.
Enhancing Safety for Vulnerable Road Users: A Data-Driven and Community-Focused
Approach
NYSDOT & US-DOT UTC Region 2
September 2024 ~ September 2025
PI: Abolfazl Karimpour, Ph.D.
Co-PI: Andrew Wolfe, Ph.D., Asif Ahmed, Ph.D.
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 project will initiate by investigating current state policies and regulations related to VRUs and reviewing existing metrics and interventions. A data scan will be conducted to identify data needs for analyzing factors contributing to VRU crashes. The research will also examine how socioeconomic disparities and underserved communities influence VRU safety. Finally, advanced data analytics will be employed to mathematically identify the risks and factors most significantly contributing to VRU crashes, aiming to inform targeted safety improvements.
Diversity, Equity, Inclusion, and Belonging (DEIB) Research Center
A Seed Grant Center at SUNY Polytechnic Institute
September 2024 ~ September 2027
PI: Byeongdon (Don) Oh, Ph.D.
Co-PI: Abolfazl Karimpour, Ph.D.
The Diversity, Equity, Inclusion, and Belonging (DEIB) Research Center spearheads SUNY Poly’s research, teaching, and outreach efforts for DEIB excellence. As described in Figure 1, the DEIB Research Center (1) cultivates and integrates SUNY Poly’s DEIB resources and network with academic, civic, and industrial partners, and (2) empowers STEAM students, faculty, and staff to achieve DEIB excellence within the campus and surrounding communities. Eventually, the DEIB Research Center creates critical impacts on improving the representation of traditionally marginalized groups at SUNY Poly and within the US STEAM workforce by advancing SUNY Poly’s missions in three thematic areas: Healthcare & Well-being, Smart Infrastructure & Sustainability, and Artificial Intelligence & Information Technology.
Evaluating the Impact of Data-Driven Traffic Signal Optimization on Traffic Operations and Safety
US-DOT UTC Region 2 | Monroe County DOT, NY
September 2023 ~ December 2024
PI: Abolfazl Karimpour, Ph.D.
Co-PI: Andrew Wolfe, Ph.D., Michael Fancher
This study will employ a comprehensive approach by leveraging multiple sources of traffic data and data-driven methodologies. It will integrate well-established and state-of-the-art signal timing tools, specifically Synchro® Studio and WaySync, to effectively address the goals of signal optimization and coordination. These tools will be synergistically combined with advanced statistical modeling techniques to attain optimal coordination outcomes within the defined study corridor located in Monroe County.
Photo By Leslie Tomaino | 210801-N-NO226-0001 NOROLK, Va. (Aug. 1, 2021)
Investigating Road User’s Compliance of Yellow and Clearance Time Intervals for Signal Timing Design
City of Phoenix, AZ
May 2022~ April 2023
PI: Yao-Jan Wu, Ph.D., P.E.
Co-PI: Abolfazl Karimpour, Ph.D.
Project Manager: Simon Ramos
In this project, the City of Phoenix (COP) has teamed up with the University of Arizona (UArizona) and the State University of New York (SUNY), Polytechnic Institute to 1) identify the RLR hotspots by implementing smart sensors at 12 intersections, 2) examine whether the new ITE guideline on yellow change and red clearance intervals can enhance the safety of signalized intersections, and 3) evaluate the potential countermeasures for reducing the frequency of RLR.
Image Source: https://conjointly.com/kb/the-road-map/
Research Roadmap: Multimodal Data and Modelling
National Institute for Transportation & Communities (USDOT National University Transportation Center)
Aug. 2021 ~ May 2022
PI: Abolfazl Karimpour, Ph.D.
This research roadmap shares common concerns between researchers and practitioners about current data collection and archiving strategies, with an emphasis on a lack of standardization and validation. In addition, it provides a guide to understanding the gaps between current state-of-art and state-of-practice of multimodal data and modeling strategies and provides potential research opportunities and prospects to fill the identified gaps. This task was particularly challenging because data collection and modeling are cross-cutting topics, and there is not really an end (or beginning) to any review effort related to this area.
Automated Data Collection and Analysis for Arterial Traffic Operations: Multiple Phase Project
Town of Marana, AZ
Nov. 2019 ~ July. 2022
PI: Yao-Jan Wu, Ph.D., P.E.
Co-PI: Abolfazl Karimpour, Ph.D.
Project Manager: Diahn Swartz, P.E.
The overall goal of this multi-year collaboration was to facilitate data collection and analytics and develop data-driven solutions that provide efficient traffic signal operations and optimal progression along key corridors within the entire Town of Marana. Efficient traffic operations save time and money, and positions the Town for future investment in business and technology.
Statistical Comparisons of Traffic Data for Traffic Signal Re-Timing
City of Phoenix, AZ
March 2021~ May 2022
PI: Yao-Jan Wu, Ph.D., P.E.
Co-PI: Abolfazl Karimpour, Ph.D.
Project Manager: Simon Ramos
In this project, the research team provided a statistical comparison of multiple traffic data sources for network performance evaluation for conducting traffic signal retiming. The study included the investigation of more innovative signal re-timing strategies that will help improve the efficiency of traffic signal operations and optimal progression along key corridors. This will ultimately save money and resources within the City of Phoenix.
Technical Support for Transportation Network Management System (TNMS) – Phase 2
Pima County Department of Transportation, AZ
April 2021~ Aug. 2021
PI: Yao-Jan Wu, Ph.D., P.E.
Co-PI: Abolfazl Karimpour, Ph.D.
Project Manager: Rich.Franz-Under
The project team worked closely with Pima County IT and PCDOT to provide input to the design and implementation of complex spatial-analytical models, products, and services, including web map, web app, and reporting. There is also the intention to perform independent and cooperative complex analysis as well as evaluate management problems and recommend decisions regarding the proper course of action.
Evaluation of Emerging Transportation Technologies
Maricopa Association of Governments, AZ
Dec. 2019 ~ Jan. 2021
PI: Yao-Jan Wu, Ph.D., P.E.
Co-PI: Abolfazl Karimpour, Ph.D.
Project Manager: Shuyao Hong
Two study corridors have been selected for the smart traffic sensor implementation: Glendale Avenue in the City of Phoenix and Chandler Blvd in the City of Chandler. This vendor will install smart sensors at five intersections on Glendale Avenue, Phoenix, and 11 intersections on Chandler Blvd in the City of Chandler. This pilot study will focus on the evaluation of the operational and safety effectiveness of the smart sensors in the area of traffic coordination and progression through the corridor as well as traffic optimization.