Characterizing Traffic under Uncertain Disruptions: An Experimental Approach
Dr. Song Gao, Principal Investigator
Assistant Professor of Civil and Environmental Engineering
University of Massachusetts/Amherst
Sponsor: New England University Transportation Center, Region 1
The objective of the research is to study long-term traffic patterns under uncertain disruptions using data collected from human subjects who simultaneously make route choices in controlled PC-based laboratory experiments. Uncertain disruptions to a traffic system usually include incidents, bad weather and work zones which result in uncertain travel times. Meanwhile, real-time information is and will be available to travelers so they can adapt to actual traffic conditions and reduce the negative effects of uncertainties. In conventional traffic prediction models, these disruptions are excluded and travelers are assumed to face a deterministic network. However, as uncertain disruptions account for a significant portion of the total traffic delays on the road, it is imperative to incorporate them in a traffic prediction model. In a New England UTC Year 21 project, we developed an individual behavioral model of route choice in an uncertain network with real-time traveler information. This project builds on the behavioral model and considers the collective congestion effects of many individual drivers’ route choices. Two central research questions are to be answered: 1) Is there a steady traffic pattern in terms of probability distributions of traffic variables under uncertain disruptions, with and without real-time traveler information? 2) Can we build a model to characterize traffic patterns under such situations? The research will contribute to the state of the art by providing laboratory evidence of the steady traffic patterns (or the lack thereof) under uncertain disruptions and by validating a novel traffic prediction model that considers both uncertain network travel times and travelers’ route choices with real-time information.