As people interact at a large-scale within infrastructure systems (such as roadway systems), the area of network modeling is often employed to characterize the impact of the resulting behavior. Traditionally, the concept of network equilibrium has been employed which models the long-term steady state behavior of many individuals each acting in their own self interest. While equilibrium has been critical for infrastructure planning and management, it requires several key assumptions such as familiarity and rationality which may not hold true in high stress disruptive situations. This research project addresses new models for network behavior when significant disruptions occur which upset the expected network state. The primary hypotheses of this research is that individuals can transform and adapt previous expectations based on their perception of the disruption as well as information learned en-route and that in unfamiliar cases network users place greater weight on system and context-specific characteristics such as route and road geometry, risk preference, and travel constraints (e.g., when unfamiliar with the true expected cost, users may select a longer path simply because it moves them closer to the destination initially). This research will discern these new individual behaviors through psychological experiments and then develop novel mathematical formulations for the resulting network impacts.
By adopting the new problem characteristics noted in the previous paragraph, fundamentally new mathematical system descriptions and predictions can be developed for large-scale networks subjected to disruptions. By achieving superior prediction capabilities, substantial societal improvements are achievable by being able to better prepare for disaster and evacuation possibilities. Furthermore, by better understanding non-equilibrium behavior even substantial near-daily non-extreme improvements are achievable such as mitigating the impact of non-recurrent congestion and traffic incidents (both areas which have long complicated transportation planning). Numerous broader benefits will also be seen beyond transportation systems. As this research addresses the fundamental problem of network behavior, numerous fields which employ network models can adopt aspects of the new behavioral models. Educationally, substantial benefits will result from the closer consideration of network modeling with psychological behavior. Further, outreach efforts will be conducted by both Co-PIs and in conjunction with programs sponsored by UT Austin to introduce students to research and practice, with an emphasis on recruiting a diverse mix of undergraduate and graduate students. Through such programs (including the Advanced Institute and US Intern program at UT-Austin combined with the NSF REU program) the PIs have been repeatedly successful in the past in recruiting such a diverse mix of students and are committed to forming a closely cooperative interdisciplinary research effort.
" focused on the planning and management of traffic networks and took place from 2009 until 2012. The project involved Civil Engineering researchers (specifically in the transportation engineering domain) along with Psychology researchers. The goal was to develop a new set of theories, techniques and tools for analysing and planning traffic networks in the context of disrupted conditions. Specifically, we produced new engineering methods to represent how drivers behave in disrupted conditions (such as traffic accidents, poor weather, or unexpected congestion). These new methods were the result of a range of lab experiments and field data collection which allowed for the calibration of driver behaviour functions. These functions were then included in network models which represent the decisions of many drivers simultaneously. The resulting tool is usable in a planning context which will facilitate improved traffic network design. The resulting system behaviour is consistent with traffic field data collected from sources in Maryland, Texas and California. In addition to two Professors, multiple graduate and undergraduate students were involved in the research. One M.S. thesis was completed and one Ph.D. dissertation. Two undergraduate students who were involved continued on to graduate studies in the transportation engineering area. Multiple publications and conference presentations were conducted to assist in the dissemination of the outcomes
