Here’s a small snapshot of some of the past research and projects that my team has completed. If you wish to discuss any of these pieces of work, feel free to drop me an email at rbm@mail.utexas.edu.
Driver Yielding Behavior
The CoA is developing a pedestrian crossing program that builds on the success of the Pedestrian Hybrid Beacon program, focusing on low-cost crossing treatments that have been successful in similar locations across the US. In consultation with the CoA Area Engineers and CoA Active Transportation staff, we will design experiments to evaluate the impact of various pedestrian crossing treatments in Austin. The methodology will likely consist of before-and-after analyses conducted using video data and data from fixed traffic sensors. In collaboration with the CoA, we will select and compute appropriate performance metrics, considering both driver yield compliance and speed, and the behavior of pedestrians.
Forecasting Fracking Truck Traffic in Tamaulipas
One of the valuable lessons learned in Texas is the fact that adequate early warning of fracking activity in a particular area, can allow highway pavement strengthening to withstand the accelerated fracking-related pavement loading for about ten percent of the total rebuild cost resulting in a 90 percent cost saving. In many Texas cases, fracking truck traffic literally destroyed thin rural road pavements requiring total reconstruction with associated large costs. The team has developed for the Austin TxDOT District a GIS-based system for forecasting expected fracking truck traffic including trip origins, destinations, likely path choices, and total pavement loading. A similar system was partially developed for Tamaulipas during Phase 1 of this work. However, the completion of the system was limited by available information. The updated system will be designed to allow efficient updating as drilling locations, disposal sites and processes and product delivery locations change with time. energy sector grows and matures, the transportation system will need to mature as well, and this system will enable that process.
Evolution of Advanced Transit Signal Priority with Gap-Based Signal Recovery Strategy
Improving travel time has been one of the primary objectives used to attract more passengers to public transportation. Transit Signal Priority (TSP) can decrease transit travel times by extending green signals or truncating red signals when buses approach intersections. The greatest obstacle to implementing TSP is the fact that effective TSP tends to bring adverse effects on automobile travel times creating adverse public reactions. The work proposed here is designed to reduce adverse effects on automobile travel times through improved information provision to TSP and imposition of constraints on how the TSP system operates. The objective of this project is to develop and test advanced transit signal priority systems that take into account the cross-street traffic volumes as part of the signal recovery (“payback”) strategy. These systems will be designed to improve transit travel time as well as minimize automobile delay while providing minimal negative impacts to the cross-streets.
adaptive signal Control Prioritization
The adaptive signal technology development addressed in previous work offers the potential for reducing traffic delay and improving travel times within the typical urban network. However, implementation of the technology in a large urban signal network usually cannot be accomplished in one step and adaptive technology may actually not be appropriate for every signal. For example, the City of Austin Transportation Department is responsible for installing and maintaining over 1000 signals and although they are interested in adaptive control they wish to have guidance regarding priorities for adaptive control implementation. Therefore, this work will develop a methodology for prioritizing the implementation process. Since adaptive technology provides enhanced responsiveness to changes in traffic demand, methodological development will consider traffic demand changes across a variety of time frames including hours, days, weeks and longer time durations as potential indicators of implementation priority. Another consideration, related to time-based coordination, is the characteristics of signal groups in which prospective adaptive controllers are located. Generally, for coordination purposes, signals in each area-wide group have common cycle lengths and constraining an adaptive controller to maintain an existing group cycle length could reduce the potential efficiency. The desired product of this task is a methodology for developing a priority order for adaptive control implementation.
