Research Interests

• Traffic Safety

• Traffic Operations

• Naturalistic Driving Studies

• Human Factors

• Big Data Analytics

• Statistical Modeling of Traffic Safety Data

• Microsimulation Modeling

• Traffic Flow Theory

• Connected and Autonomous Vehicles

• Cooperative Automated Systems

Research Projects

NCHRP 07-30: Methods for Assigning Short-Duration Traffic Volume Counts to Adjustment Factor Groups for Estimating AADT, Funded by National Academies of Sciences, Engineering, and Medicine (2022 - 2023)

Research Tasks:

• Developing rational methods for estimating Annual Average Daily Traffic (AADT) by assigning short-duration counts to adjustment factor groups concerned with all functional classes of roadways and traffic volumes.

• Preparing technical reports, guides, and other deliverables for the implementation of the project to improve the current practice of AADT estimation.

NCHRP 03-144: Leveraging Existing Traffic Signal Assets to Obtain Quality Traffic Counts and Enhance Transportation Monitoring Programs, Funded by National Academies of Sciences, Engineering, and Medicine (2022 - 2023)

Research Tasks:

• Determining the feasibility of using existing or enhanced traffic equipment to collect, store, and disseminate data for purposes other than traffic operations, particularly for traffic monitoring programs as well as determining the suitability of using traffic count data from existing traffic assets for this purpose. 

• Developing effective practices for obtaining and integrating traffic counts from existing traffic assets.

• Preparing technical reports, guides, and other deliverables to disseminate the research results to the transportation community.

Integrating Human Behavior Toward the Development of Safer Cooperative Automated Transportation: Implementation of SHRP2 Naturalistic Driving Study, Funded by USDOT-FHWA and WYDOT (2021- 2022)

Research Tasks:

• Leveraging the SHRP2 NDS data to gather an in-depth understanding of human behavior and analyze different human actions, namely driving patterns leading to crashes/near-crashes, deviation from normal driving, and kinematics profiling for achieving proper behavior cloning.

• Integrating the findings toward the development of realistic Cooperative Automated Transportation and analyzing their ability to ensure proper safety and operations at different levels of market penetration rates. 

• Publishing and presenting academic papers and preparing reports for the implementation of the study.

Below are several journal publications related to this project: 

Automated Real-Time Weather Detection System Using Artificial Intelligence, Funded by USDOT-FHWA and WYDOT (2020 - 2022)

Research Tasks:

• Extracting and annotating images of weather and surface conditions in adverse weather from the Webcams installed in the Wyoming roadways.

• Acquiring trajectory-level raw datasets of video images from in-vehicle video cameras in snow weather conditions, processing the video data, and annotating image datasets in different snowy weather.

• Developing automatic real-time weather and surface detection algorithms as well as developing and evaluating trajectory-level snow detection systems using various Machine Learning and Artificial Intelligence algorithms.

• Publishing and presenting academic papers and preparing reports for the implementation of the study.

Below is the journal publication related to this project: 

Driver Performance and Behavior in Adverse Weather Conditions: An Investigation Using the SHRP2 Naturalistic Driving Study Data,   Funded by USDOT–FHWA and WYDOT (2016 - 2021)

Research Tasks:

• • Involved in improving safety in adverse weather conditions through an in-depth investigation of driver behavior and performance using trajectory-level data.

  • Provided recommendations to enhance Advanced Driver Assistance Systems, Advanced Traveler Information System, and updating weather-based microsimulation models and Variable Speed Limit algorithms through analyzing big trajectory-level naturalistic driving data.

  • Published and presented academic papers and prepared reports for the implementation of the study.

Below are several journal publications related to this project: 

Involvement in Other Funded Research Projects

Evaluate Accuracy of Probe-Based Traffic Volume Estimates Provided by Data Vendors, Funded by Maryland Department of Transportation (MDOT) (2022 - 2023)

• Determining the accuracy of probe-based volume estimates developed and provided by up to two data vendors including StreetLight Inc. and INRIX Inc.

Integrating Probe-Based Traffic Count Estimates in the Statewide Traffic Monitoring Program, Funded by Texas Department of Transportation (TxDOT) (2022 - 2023)

• Validating probe/CV-based volume estimates developed by two or more data vendors such as Replica, INRIX, and Wejo for different functional classes.

• Comparing the accuracy of probe/CV estimates vs. TxDOT’s default AADT values for lower functional classes.

Application Development and Participant Training for Wyoming Connected Vehicle Pilot Deployment Program, Funded by USDOT–FHWA (2018 - 2019)

• Operated the driving simulator lab (WyoSafeSim).

• Conducted different study sessions at the simulator lab, ran the driving simulator experiments for the recruited participants, and collected the data from the driving simulator. 

Hazardous Materials Commodity Flow Study in Wyoming, Wyoming State Emergency Response Commission (SERC) and Wyoming Office of Homeland Security (WOHS) (2017 - 2019)

• Participated in designing the plan for data collections.

• Collected traffic data on different locations of Natrona, Sweetwater, Goshen, Niobrara, and Sheridan counties in Wyoming.