March 22, 2023 | 12:00 – 1:00 PM (ET)
About the Webinar:
Implementation of access management projects has historically caused conflict between practitioners who support the safety and mobility benefits of access management and business/property owners who are concerned about the potential negative impacts of reduced access to adjacent properties. Elected officials who are responsible for supporting or approving project funding find themselves in a delicate balancing act between safety, economy, and mobility. Past evaluations of access management projects have been limited to manual traffic data collection, simulations, and evaluation of crash data many years after the project is implemented. In addition, evaluation of sales tax receipts before and after implementation of the project is challenging due to privacy concerns. The purpose of this project is to test the use of innovative data sources to evaluate the safety, mobility, and economic effects of access management projects by comparing trips and other performance measures into adjacent development before and after implementation of the project.
About the Presenters:
Ms. Debbie Albert is a traffic engineer with over 20 years experience. Ms. Albert supports TxDOT, regional, and local jurisdictions on access enhancement and roadway construction projects and assists with transportation operations projects for Texas A&M University. Her research interests also include safety evaluations for large construction projects in Texas and transportation equity. Prior to becoming a member of TTI’s Mobility Analysis Program, she held various traffic engineering and transportation planning positions with the City of Glendale in Arizona. Most recently, she served as the City Traffic Engineer providing management and direction for all traffic engineering and operations functions as well as coordinating regional transportation projects affecting the city.

Mr. Kartik Jha has been involved in topical research in areas of urban mobility measurement, roadway travel reliability, freight mobility, traffic operations, and special event traffic management for 8 years at TTI in College Station, Texas. He has helped with research involving exploration and application of travel time databases, system performance measurement for FHWA pooled fund study, developing port-level multi-modal freight fluidity performance management concept, using FAF database to determine value of truck freight flow between urban regions and on US roadways, as well as utilizing NPMRDS for MAP-21 performance monitoring. Mr. Jha has been extensively involved in mobility performance measurement research for over 8 years where he has contributed to the Urban Mobility Report which utilizes performance measures to assess areawide traffic congestion levels and congestion costs for all urban areas in the U.S. In addition to this national work, Kartik has also helped perform congestion and bottleneck analyses for several state DOTs. He has also contributed to the Texas 100 Most Congested Road Sections report for the Texas Department of Transportation where over 1,800 road sections in Texas (about 10,000 miles) are monitored annually for traffic congestion. Prior to joining TTI, Mr. Jha worked as a highway design engineer with a design consultancy in India. During this time, he also gained some experience in projects in the areas of transportation planning, survey optimization and city transit planning.

Mahin Ramezani is a research data scientist in the Center for Transportation Safety at the Texas A&M Transportation Institute (TTI). She received her Master of Science in Computer Science from Texas A&M University. During her study, she worked as a Graduate Research Assistance at Population Informatics Lab, School of Public Health, Texas A&M Health Science Center. Mahin’s research interests include data science, data mining, and machine learning. She joined TTI in 2021, where she focuses on analyzing Crash data and CV data which contains billions of vehicle movement and driver event points from connected vehicles to extract patterns that can be used to make and improve safety decisions.
