Current Research

Here’s a snapshot of the current research and active projects under Team Machemehl. Some of these projects are happening in collaboration with more than one agency. If you wish to discuss any of these pieces of work, feel free to drop me an email at rbm@mail.utexas.edu.

Identifying Targets for Electric Vehicle Industry Improvement

This project is funded under the Center for Understanding Future Travel Behavior and Demand (TBD)  and the focus of this project is to conduct a comprehensive analysis of the electric vehicle (EV) industry addressing existing challenges exploring innovative solutions for widespread adoption such as:

    • Comprehensive EV Analysis: Identify key EV industry challenges, including investigating the availability of essential resources,
    • Collaboration: Work with EV makers for real-world EV industry insights,
    • Workforce Development: Provide experience for future EV leaders, and
    • Innovation Focus: Identify practical solutions for EV adoption hurdles.
dYNAMIC tRAFFIC aSSIGNMENT FOR qUANTIFYING iMPACTS OF cLOSURES ON mAJOR CORRIDORS

This project aims to understand the impact of main lane & ramp closures on major corridors in the Austin District, like IH-35. Using a Dynamic Traffic Assignment Model (DTA) model, this project assesses delays during different time periods and scenarios. By analyzing traffic using real data extracted from regional traffic demand models, we can quantifies potential delays and increases in vehicle-miles traveled. The insights drawn from this research are vital in formulating future transportation policies and performing infrastructure planning that will eventually contribute to a more resilient road network.

Pedestrian Fatality Analysis on I-35

The Austin District of TxDOT has a number of pedestrian fatalities occurring on controlled-access highways. Since these facilities are not at all built for pedestrians, it is not clear why pedestrians choose to cross at these locations. This works aims to investigate pedestrian fatalities incidents on I-35 during the last five years to inform crash mitigation efforts. Documentation includes crash locations, time of day, contributing factors, traffic impacts, and comprehensive public cost estimates.

Early Warning System for Pavement damage

The Early Warning System has been built to detect changes in land use and predict truck traffic increases during construction activity and post-construction traffic patterns. The idea for The System originally was conceived by Mike Murphy and Mike Arellano as a solution to an issue with excessive truck loading in Travis County. Instead of spending $10 million to fix a damaged road where a factory was built, $1 million could be spent to reinforce the pavement prepare for such heavy loading in advance of construction. These kinds of savings are significant and valuable to the taxpayer. At present, the GIS-based system is currently maintained by Team Machemehl and considered pavement development from urban growth and fracking activities in the Austin District of TxDOT.

Highway Safety Improvement Project selection tool

The Highway Safety Improvement Program (HSIP) is a competitive funding program to address critical safety needs. The HSIP requires each district within TxDOT to develop eligible safety project lists to compete for the available funding. Moreover, this program contains various funding categories each with different eligibility criteria. Therefore, this work aims to create a tool tailored to the Austin District to reduce the time needed to develop eligible project lists, distribute projects into the various funding categories, and identify high benefit-cost safety improvement projects. The tool will allow the Austin District to quickly responding to HSIP funding calls and to reduce the time required to develop the safety project lists.

Bicycle Signal Experiments

Twelve intersections are proposed for experimentation with bicycle signal faces under MUTCD Interim Approval IA-16 in the City of Austin. We will collect data at eleven intersections before and after new bicycle signals are installed. All data will be collected during the spring semester when school is in session. At three intersections, the City of Austin will collect and process data.  We will evaluate the data and include conclusions in their results. With before and after data, we will evaluate measures of effectiveness between periods including travel time, stop frequency, stopped delay, minor-street delay, traffic speed profile, cycle length, fuel consumption, and emissions to assess the traffic operational and environmental benefits of the adaptive signal system. All tasks will be conducted in close coordination with City of Austin Transportation Department (ATD).

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