Spring 2021 Series

Student Presenter: Chen Chen, MS. PhD Candidate for Transportation Engineering at Oregon State University.

Student Advisor: Dr. Haizhong Wang. Professor in the Department of Civil Engineering at Oregon State University.

Original Air Date: March 19, 2021

Description

This research integrates social science, transportation engineering, and hazard science through an interdisciplinary agent-based modeling (ABM) framework to improve life safety in the Cascadia Subduction Zone. The agents’ protective action decisions are informed by empirical data collected from the cities of Coos Bay, OR and Crescent city, CA through household evacuation intention surveys. The incorporation of the protective action decision model (PADM) into the ABM will provide more credible scenario-based simulation results for coastal communities to assess their current evacuation plans and identify gaps for improved preparedness.

Student Presenter: Jingxing Wang, MS. PhD Candidate for Transportation Engineering at the University of Washington.

Student Advisor: Dr. Xuegang (Jeff) Ban. Professor in the Department of Civil and Environmental Engineering at University of Washington.

Original Air Date: May 4, 2021

Description

Origin-Destination (OD) demands for a city or a region are essential input to many transportation applications. For a real-world transportation network, the OD demand matrix may have certain insignificance property, i.e., the majority of the OD pairs may have small demands while only a small portion of OD pairs have large demands. We propose an Insignificance-based OD (IOD) framework to explore such insignificance property of OD demand matrices, and theoretically and numerically show that under certain conditions the estimated OD matrix shares the same insignificance feature with the prior OD matrix, and the estimated demands of most OD pairs (of a large-size network) will be equal to either their prior values or zeros. Results demonstrate that the IOD framework has the capability in keeping OD insignificance consistency and is computationally less demanding, comparing with existing OD estimation models. The practical implications of the IOD framework are also discussed.

Description

Using a survey of students, faculty and staff at Washington State University, we analyze how both fee and non-fee commute attributes affect commuters' mode and parking decisions. Results show commuters are sensitive to the price, travel time, egress time, and search time in selecting their primary commute mode, but are not sensitive to travel costs including the costs of fuel and maintenance. Using these results, we conduct policy simulations to analyze how changes in transit prices, travel times, and parking availability affect commuters' mode and parking decisions. We find raising parking permit prices and decreasing bus travel times to be effective in reducing the number of single occupancy vehicle commuters; the closure of central parking locations, however, is found to be ineffective in discouraging commuters from driving to campus as drivers are more likely to switch parking locations than they are commute modes.

Description

Origin-Destination (OD) demands for a city or a region are essential input to many transportation applications. For a real-world transportation network, the OD demand matrix may have certain insignificance property, i.e., the majority of the OD pairs may have small demands while only a small portion of OD pairs have large demands. We propose an Insignificance-based OD (IOD) framework to explore such insignificance property of OD demand matrices, and theoretically and numerically show that under certain conditions the estimated OD matrix shares the same insignificance feature with the prior OD matrix, and the estimated demands of most OD pairs (of a large-size network) will be equal to either their prior values or zeros. Results demonstrate that the IOD framework has the capability in keeping OD insignificance consistency and is computationally less demanding, comparing with existing OD estimation models. The practical implications of the IOD framework are also discussed.