Friday Transportation Seminar: PSU Student Research from the TRB 2022 Annual Meeting

Friday, January 21, 2022, 11:30am to 12:30pm PST
Darshan Chauhan and Sangwan Lee, PSU
PDH: 1 | AICP: 1

Friday Transportation Seminars at Portland State University have been a tradition since 2000. You can join us online at 11:30 AM. All presentations are recorded and shared on the event page afterwards.



Join us for a two-part seminar diving into research that was presented by Portland State University students at the annual meeting of the Transportation Research Board (TRB) in January 2022.

Drone Facility Location Considering Coverage Reliability: Application to Emergency Medical Scenarios
Darshan Chauhan, Civil & Environmental Engineering

Access the TRB Poster: Robust Multi-Period Maximum Coverage Facility Location Problem Considering Coverage Reliability (PDF)

Public service agencies like hospitals, fire, rescue, and police departments are required to maintain a high level of service. These service standards often come as reliability constraints. For example, fire-related incidents require a 90% response rate in 4 minutes. We consider a case study of tackling out-of-hospital cardiac events using AED-enabled drones in Portland, OR. Environmental factors, majorly wind speed and direction, significantly impact drone performance. We formulate the drone location problem as a robust multi-period maximum coverage facility location problem. We model the coverage reliability constraint as a chance constraint on failure probabilities. For our context, multiple periods translate to periods with different wind speeds and distributions. The results show that extending to a multi-period formulation, rather than using average information in a single period, is particularly beneficial when either response time is short or uncertainty in failure probabilities is not accounted for. Accounting for uncertainty in decision-making improves coverage significantly while reducing variability, especially when response times are longer. Using multiple periods and accounting for uncertainty in failure probabilities boosts the simulated coverage values by 57%, on average.

Key Learning Outcomes

  • Learn how to model service reliability constraint as a chance-constraint.
  • Learn how to develop a Monte-Carlo Simulation scheme to evaluate model performance.
  • Learn how to make inferences from model results.

Effect of COVID-19 on Property Value Premium of Light Rail Transit

Sangwan Lee, Urban Studies & Planning 

Access the TRB poster: Effect of COVID-19 on Residential Property Value Premium of Light Rail Transit: A Case Study of the Portland Metropolitan Area (PDF)

The COVID-19 pandemic has impacted health and transformed many aspects of our lives, such as travel behavior and residential location preference. For instance, since the outbreak, there was a sharp decline in the ridership of public transportation. Moreover, since the pandemic, a shifting preference toward suburban and exurban areas from denser urban areas like Central Business Districts has been reported. Accordingly, a question arises whether the plummeted transit ridership, alongside the anecdotal and empirical evidence that some households prefer to disperse away from the cities, could combine to transform the long-standing housing price mechanism related to transit proximity. Thus, we asked whether the preference for proximity to a light rail transit (LRT) station in residential location choice remained during the pandemic and whether/how the potentially changing preference was reflected in property value in the Portland Metropolitan Area.

Key Learning Outcomes

  • While homebuyers still value single-family homes proximate to a LRT station, multi-family homes within a service are of a LRT station lost their premium during the pandemic.
  • The price appreciation rate was 1.7% higher for single-family homes located within a half-mile of an LRT station in the Portland metropolitan area, Oregon, compared to otherwise similar homes.
  • Conversely, multi-family homes within an LRT service area showed a 3.2% lower price appreciation rate than those with similar characteristics.


Darshan Chauhan, Civil & Environmental Engineering, Portland State University

Darshan is a Ph.D. Candidate in Civil and Environmental Engineering and Graduate Research Assistant at Portland State University (PSU). His doctoral work with Dr. Avinash Unnikrishnan is on planning and real-time resource allocation in freight logistics systems using robust optimization and reinforcement learning. He is also interested in data analytics and its applications to transportation safety using econometric modeling. He has served as the Treasurer of STEP, PSU’s ITE student chapter.


Sangwan Lee, Urban Studies & Planning, Portland State University

Sangwan Lee is a graduate research assistant in the Nohad A. Toulan School of Urban Studies and Planning at Portland State University. He has also served as a teaching assistant at Rutgers, The State University of New Jersey; and as an assistant research fellow in urban engineering at Chonbik National University. Sangwan is currently a Ph.D. student in urban studies at PSU with a focus on regional science. He earned his Masters of City and Regional Planning at Rutgers in 2019 and his Masters of Urban Engineering at Chonbuk in 2014. He also earned a BS in Architecture and Urban Engineering from Chonbuk in 2012.


This 60-minute seminar is eligible for 1 hour of professional development credit for AICP (see our provider summary). We can provide an electronic attendance certificate for other types of certification maintenance.


Photo by Jennifer Dill

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The Transportation Research and Education Center (TREC) at Portland State University is home to the National Institute for Transportation and Communities (NITC), the Initiative for Bicycle and Pedestrian Innovation (IBPI), and other transportation programs. TREC produces research and tools for transportation decision makers, develops K-12 curriculum to expand the diversity and capacity of the workforce, and engages students and professionals through education.