Metro's Research and Modeling Services Program is responsible for the development, maintenance, and application of travel demand models for application in long-range planning efforts in the Portland metropolitan region.
Representation of traffic—both vehicular and transit—plays an integral role in the travel demand modeling process. Complex software is required to assign vehicles and transit users to transportation networks to determine viable options available to travelers, costs associated with those options, and sets of routes by which travelers might navigate their trips.
Metro's current static assignment model has traditionally sufficed for use with Metro's four-step travel demand model. However, static assignments have well-documented limitations that preclude the ability of the analyst to answer complex policy questions, especially those related to greenhouse...Read more
Although an increasing number of separated bicycle facilities have been appearing across the US over the last few years, the majority of bicyclists are still traveling on roadways shared with motorized vehicles.
As a result, bicycles are essentially double exposed to safety risk, due to their interactions with both motorized vehicles and other bicycles. In addition to this double exposure, data challenges–such as a lack of continuous counts and bicycle crash data—complicate the assessment of bicycle safety further.
This research presents a bicycle crash analysis framework for estimating bicycle crash rates accounting for both bicycle and motorized vehicle exposure as well as overcoming the lack of bicycle count data.
First, a novel seasonal bicycle demand model is presented that is capable of estimating monthly average daily bicyclists (MADB) and annual average daily bicyclists (AADB) using an area-specific calibration factor. This factor can...Read more
New technologies such as smart phones and web applications constantly collect data on individuals' trip-making and travel patterns. Efforts at using these "Big data" products, to date, have focused on using them to expand or inform traditional travel demand modeling frameworks; however, it is worth considering if a new framework built to maximize the strengths of big data would be more useful to policy makers and planners.
In this presentation Greg Macfarlane will present a discussion on elements of travel models that could quickly benefit from big data and concurrent machine learning techniques, and results from a preliminary application of a prototype framework in Asheville, North Carolina.
Dr. Macfarlane is an analyst in the Systems Analysis Group of WSP | Parsons Brinckerhoff, developing and applying advanced travel demand models. His research and expertise includes trip-based models, activity-based models, integrated land-use/transport models, and micro-simulation of both travel...Read more
Exploring the Positive Utility of Travel and Mode Choice
Civil & Environmental Engineering: Patrick Singleton
Why do people travel? We traditionally assume traveling is a means to an end, travel demand is derived (from the demand for activities), and travel time is to be minimized. Recently, scholars have questioned these axioms, noting that some people may like to travel, use travel time productively, enjoy the experience of traveling, or travel for non-utilitarian reasons. The idea that travel can provide benefits and may be motivated by factors beyond reaching activity destinations is known as...Read more
Car crashes are still a leading cause of death in the United States, with vulnerable road users like bicyclists and pedestrians being injured or killed at rates that outpace their mode share.
Planners, engineers, and advocates are increasingly adopting Vision Zero and Tactical Urbanism approaches and trying to better understand the underlying causes of dangerous roadway interactions. However, existing research into crash causation has focused on instrumental factors (e.g. intersection type, vehicle speed) while little research has probed the role of attitudes or socio-cognitive mechanisms in interactions between roadway users.
Social psychology suggests that attitudes and social cognitions can play a role in conflict. Drivers’ attitudes toward bicyclists, and how those attitudes may affect drivers’ behavior, are a largely unexplored area of research, particularly...Read more
Planners and policymakers are often faced with the need to make decisions about issues for which there is uncertainty and limited data. For example, transportation planners are now faced with the prospect that new transportation technologies such as autonomous vehicles could greatly alter future transportation system needs. Decisions about these types of issues are difficult to reason about and consequently are likely to be ignored or made on the basis of simplistic logic. Although modeling could be helpful, especially for issues involving complex systems, it is rarely used because models usually require large amounts of data and and handle uncertainty poorly.
This presentation is about how a fuzzy systems dynamic model (FSDM) may be used to model policy issues involving uncertainty and limited data. The FSDM is a type of fuzzy cognitive...Read more
Location: Engineering Building 315
The Portland State University Department of Civil and Environmental Engineering is pleased to announce Steven Gehrke's PhD Dissertation Defense: "Land Use Mix and Pedestrian Travel Behavior: Advancements in Conceptualization and Measurement."
Adviser: Dr. Kelly Clifton
Urban policies encouraging pedestrian travel are often rooted in land development strategies, which are intended to promote greater efficiencies in the built environment. Land use mix, an important smart growth tenet, is one such strategy beholding of lasting urban planning and public health benefits. Still, no consensus exists about the conceptualization and measurement of land use mix or the magnitude of its connection with pedestrian travel. This dissertation is comprised of three empirical studies that explore this topic in detail.
NITC dissertation fellow Steven Gehrke is a Ph.D. candidate and graduate research assistant in the department of civil & environmental engineering at Portland State University. Steven's doctoral research centers on an improved understanding of the relationships between nonautomotive travel behaviors and the temporal mixing of activity locations. He previously received a master’s degree in community planning from the...Read more
The empirical evaluation of complex decision support systems is often limited to the self-reported satisfaction of the systems’ users.
Such an approach is problematic due to the conflation of the user's satisfaction related to the decision support system and the decision making process and its outcomes.
In addition, it bears limitations that are common among most techniques that solicit participant-stated feedback.
In this talk, based on data that was gathered by a web-based participatory system for transportation planning in the Puget Sound region, I present analytical methods for the empirical evaluation of decision support systems based on human-computer interaction. In addition, I discuss the extent to which self-centered and selfless decision making expressed itself in the transportation project choices of the users of the participatory system.
The observed...Read more
Smart Growth America hosted a webinar Jan. 31 on NITC research finding that standard guidelines lead to a drastic oversupply of parking at transit-oriented developments. That restricts the supply of housing, office and retail space while driving up the price.
The webinar marks the release of Smart Growth America's lay summary of the NITC report, called "Empty Spaces," which will be available to webinar attendees.
Watch the recorded webinar here.
The research, led by Reid Ewing of the University of Utah, is one of the first comprehensive data-driven reports to estimate peak parking and vehicle trip generation rates for transit-oriented development projects, as well as one of the first to estimate travel mode shares for TODs. Ewing analyzed data on actual parking usage and total trip generation near five transit stations: Redmond, Washington; Rhode Island Row in Washington, D.C.; Fruitvale Village in Oakland, California; Englewood, Colorado; and Wilshire/Vermont in Los...Read more
Civil & Environmental Engineering: Travis Glick
Estimating Reliability Indices and Confidence Intervals for Transit and Traffic at the Corridor Level
As congestion worsens, the importance of rigorous methodologies to estimate travel-time reliability increases. Exploiting fine-granularity transit GPS data, this research proposes a novel method to estimate travel-time percentiles and confidence intervals. Novel transit reliability measures based on travel-time percentiles are proposed to identify and rank low-performance hotspots; the proposed reliability measures can be utilized to distinguish...Read more