External Advisory Committee
Posters from the September 2022 Meeting of the CTR External Advisory Committee
Latest Advances in Resilience of Infrastructure Systems
PI: Dr. Zhanmin Zhang
Authors: Kyle Bathgate & Dr. Zhanmin Zhang
Presenter: Kyle Bathgate
This poster summarizes recent projects completed by Dr. Zhanmin Zhang’s Resilient Infrastructure and Smart Cities (RISC) Lab. Infrastructure resilience refers to a system’s ability to reduce the impact of a disruption and restore operations rapidly after experiencing an extreme event such as a natural disaster. Two recent and ongoing TxDOT projects are summarized, as are three recent journal publications, related to extreme weather infrastructure resilience and infrastructure interdependency simulation
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- TxDOT Project 0-7055, https://library.ctr.utexas.edu/Presto/project=0-7055
- TxDOT Project 0-7123 (Ongoing) https://library.ctr.utexas.edu/Presto/project=0-7123
- Jingran Sun and Zhanmin Zhang, 2020, “A post-disaster resource allocation framework for improving resilience of interdependent infrastructure networks,” https://doi.org/10.1016/j.trd.2020.102455
- Kyle Bathgate, Antonio Perez, and Zhanmin Zhang, 2022, “Quantitative Analysis of Hurricane Harvey Impacts on Texas Maritime Facilities,” https://doi.org/10.1177/03611981221078574
- Srijith Balakrishnan and Zhanmin Zhang, 2020, “Criticality and Susceptibility Indexes for Resilience-Based Ranking and Prioritization of Components in Interdependent Infrastructure Networks,” https://doi.org/10.1061/(ASCE)ME.1943-5479.0000769
Development of Crash Database Parsing Tools to Support the Highway Safety Improvement Program in Texas
PI: Randy Machemehl, Ph.D., P.E.
Authors: Dr. Zhe Han, Dr. Randy Machemehl, and Dr. Michael Murphy
Presenter: Zhe Han, Ph.D., P.E.
This study presents a set of computerized tools that parse the crash database to identify crash hotspots, suggest potential treatments, and calculate safety benefits. These tools can be used by transportation agencies to support safety-related decision-making processes. A case study was conducted that returned 6,174 candidate projects and recommended the most effective treatments.
Connected Vehicle Data Framework: A Cloud-Based Public-Private Data Exchange
PIs: Kristie Chin (UT CTR), Eric Thorn (SwRI), Nick Wood (TTI)
Authors: Anna McAuley and Kristie Chin (UT CTR), Eric Thorn (SwRI)
Presenter: Anna McAuley
Connected Vehicle (CV) technology can be used to improve safety and mobility on public roads. Data can be shared through physical CV infrastructure; however, large gaps in coverage will exist until deployment of CV equipment is more ubiquitous. To overcome this, the Texas Connected Freight Corridors (TCFC) project has developed the Connected Vehicle Data Framework (CVDF), a cloud-based data exchange that relies only on cellular coverage and existing third-party services. This poster explores both of these CV data-sharing methods that are being piloted by the TCFC project.
The Study of Human Reaction and Mathematical Psychology in Transportation Engineering Applications
Authors: Katie Asmussen, Angela Haddad, Aupal Mondal, Chandra Bhat
Presenter: Katie Asmussen and Angela Haddad
This research studies the human reaction to existing circumstances, emerging technology and the unprecedented future. Using econometric and statistical methods and applying psychological and sociological bases, this work assesses human behavior in an array of transportation applications.
Using Surveys, Statistical Modeling, and Transportation Simulations to Forecast Impacts of Emerging Technologies
PI: Dr. Kara Kockelman
Authors: Kara Kockelman, Matthew D. Dean, Fatemeh Fakhrmoosavi, Yantao Huang, Jason Hawkins, and Priyanka Paithankar
Presenter: Matthew D. Dean
A combination of surveys, statistical/econometric models, and agent-based transportation simulations are useful in forecasting how emerging technologies can impact regional mobility. This poster showcases survey results on long-distance travel with autonomous vehicles to show how fleet dispatch strategies for on-demand shared autonomous all-electric vehicles (SAEVs) can lower power costs and emissions damages.
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Modeling Impacts of COVID-19 on Capital Metro Ridership
By: Jennifer Hall, Carolina Baumanis & Dr. Randy Machemehl
PI: Dr. Randy Machemehl
Presenter: Jennifer Hall
This research examines the impacts of the COVID-19 pandemic upon bus ridership through autoregression and multi-linear regression models. The autoregression model was able to capture 75% variability throughout the pandemic (R2 = ~75%). The multi-linear regression models showed that COVID-19 confirmed cases as well as fatalities were significant toward weekday and Sunday travels, but not toward riders on Saturday.
The Texas Department of Transportation Alternative Delivery System (ADS) Decision-Support Tool V2.0
Authors: Vassiliki A Demetracopoulou, William J. O’Brien, and Nabeel Khwaja
PI: Dr. William J. O’Brien
Presenter: Vassiliki A Demetracopoulou
This poster presents the Alternative Delivery System (ADS) Decision-Support Tool V2.0, a delivery method selection tool customized for TxDOT. It was developed based on outcomes from Design-Build since it became legislatively available in 2012, V1.0 of ADS (2014), the contracting community’s experience with risk, and TxDOT’s programmatic changes for alternate delivery.
Traffic State Estimation for Connected Vehicles Using the Second-Order Aw-Rascle-Zhang Traffic Model
Authors: Suyash Vishnoi, Sebastian Nugroho, Ahmad Taha, Dr. Christian Claudel
PI: Dr. Christian Claudel
Presenter: Suyash Vishnoi
This poster addresses the problem of traffic state estimation (TSE) in the presence of heterogeneous sensors. Moving sensors like connected vehicles offer a relatively cheap alternative to fixed sensors for measuring traffic states across the network. Moving forward it is important that such models be developed to effectively use data from CVs. The nonlinear second-order Aw-Rascle-Zhang (ARZ) model is a realistic traffic model, reliable for TSE and control. A nonlinear state-space formulation is presented for the ARZ model considering junctions, and its linear approximation is investigated. A Moving Horizon Estimation implementation is presented for TSE using a linearized ARZ model and is compared to various state-estimation techniques from the literature.