Plenary Session 1 (9:30 – 11:15 am)
Urban Growth and Renewal, Planning and Engineering Perspectives
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Moderator: Scott Nelson, Texas Department of Transportation
- Growth in the ETJ and Lubbock County Road Bond, Jennifer Davidson, Lubbock County
- Unified Development Code and Downtown Renewal, Victor Escamilla, City of Lubbock
- US 62 (19th St) & US 84 (Ave Q) Reconstruction and Loop 88 Construction Updates, Mike Wittie, Texas Department of Transportation
- City of Lubbock 2022 Road Bond, Mike Keenum, City of Lubbock
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Kickoff Luncheon (11:30 – 1:15 pm)
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Keynote Speakers:
- Mayor Tray Payne, City of Lubbock: Lubbock Road Improvement Bond
- Michael Chacon, TxDOT: A Word on TxDOT Agency Membership
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Concurrent Sessions 2A and 2B (1:30 – 3:00 pm)
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2A: Probe-Based Traffic Data: What is it, How is it Used, and Where Can I Get it?
Moderator: Shawn Turner, Texas A&M Transportation Institute
- Probe-Based Traffic Data Services, David Freidenfeld, Texas Department of Transportation
- Use of Connected Vehicle Trajectory Data to Measure Traffic Signal Performance in Frisco, Texas, Brian Moen, City of Frisco
- How to Get Cooking with Connected Vehicle Data, Michael Martin, Texas A&M Transportation Institute
- Understanding Origin Destination Travel Patterns Using High Fidelity Probe Data, Rick Ayers, University of Maryland
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2B: Improving Safety in Work Zones
Moderator: Jason Crawford, Texas A&M Transportation Institute
- Increasing Driver Awareness with Connected Work Zones, Scott Heydt, Horizon Signal
- Accommodating Pedestrians and Bicyclists during Highway Construction, John Habermann, Texas A&M Transportation Institute
- AI-Based Traffic Data Collection and Analytics for Work Zone Traffic Management Planning, Do Nam, WSB
- Performance-based Maintenance of Traffic for Highway Construction Projects using Real-time Data, Scott Carlson, Iteris
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Student Poster Session (3:00 – 3:30 pm)
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- A New Framework for Regional Traffic Volumes Estimation with Large-scale Connected Vehicle Data and Deep Learning Methods, Swastik Khadka, The University of Texas at Arlington
- Artificial Neural Network-based Dynamic Modulus Prediction Models for Colombian Mixtures Similar to Witczak and Hirsch Models, Prashanta Kumar, The University of Texas at Tyler
- Automatic Identification of Partial Occlusion in Data Collected by Roadside LiDAR Sensors, Yibin Zhang, Texas Tech University
- Comparison of Machine Learning Models to Predict Bike Lane Crash Severity in Texas, Pratik Lama, University of Texas at Tyler
- Comparison of Machine Learning Models to Predict Crash Severity Under Adverse Road Weather Conditions: A Case Study in Tyler, Texas, Rami Khalifah, The University of Texas at Tyler
- Development of a Model to Predict Bleeding Areas in Asphalt Pavement Using an Artificial Neural Network, Rami Khalifah, The University of Texas at Tyler
- Effects of Illumination on Crashes in a Midsize Town: A Case Study in Tyler, Texas, Raja Daoud, The University of Texas at Tyler
- Prediction of International Roughness Index of Flexible Pavement using Artificial Network Modeling, Pratik Lama, The University of Texas at Tyler
- Proactive Safety Analysis using Roadside LiDAR based Vehicle Trajectory Data: A Study on Rear-end Crashes, Nischal Bhattarai, Texas Tech University
- Real-Time Intersection Analysis: Evaluating LiDAR Sensor Latency for Capturing Near Misses in the Field, Peirong (Slade) Wang, The University of Texas at Arlington
- Review of Risk Assessment Frameworks for Cybersecurity Attacks on Road Network and Intelligent Transportation Systems (ITS) Applications, Natchaphon Leungbootbak, Texas A&M University
- Use of Galvanic Skin Response in a Stated Preference Survey to Assess Factors Influencing College Students use of Transit, Hardik Gupta, Texas A&M University
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Concurrent Sessions 3A and 3B (3:30 – 5:00 pm)
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3A: Improving Signal Operations
Moderator: John Denholm, Lee Engineering
- On-Demand Traffic Signal Retiming: The Need in a Changing World, Olivia Babcock, Miovision
- A $3 Million Dollar Bond for ATMS Improvements, James Robertson, Lee Engineering
- Signalized Intersection Performance Measures WITHOUT Hardware, Rick Ayers, University of Maryland
- I Have Signal Performance Measures, Now What?, Shaun Quayle, Inrix
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3B: Emerging Technologies
Moderator: Courtney Clark, Pape-Dawson Engineers
- Infrastructure for Emerging Technologies, Akila Thamizharasan and Megan Dutton, Texas Department of Transportation
- What a Million CVs and 20,000 Signals Reveal About Changing Demand Patterns and System Performance in Texas, Rick Schuman, Inrix
- Emerging Opportunities for CAV in Texas - A Perspective from the CAV Task Force, Beverly Kuhn, Texas A&M Transportation Institute
- How Artificial Intelligence is Changing ITS: Examining Practical Use Cases of AI Implementation in Traffic Management, Alex Colosivschi , Currux Vision LLC
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