modeling | National Institute for Transportation and Communities

Student presentations from TRB, week 1

View Kristi Currans's slides

View Steve Gehrke's slides

Steven Gehrke, Ph.D. Candidate, Portland State University

Topic: An Activity-related Land Use Mix Construct and Its Connection to Pedestrian Travel

Land use mix is a central smart growth principle connected to active transportation. This presentation describes the indicators of local land use mixing and their association with pedestrian travel in Oregon’s Willamette River Valley. It argues that land use mix is a multidimensional construct reflected by the complementarity, composition, and configuration of land use types, which is positively linked to walk mode choice and home-based trip frequency. Findings from this study underline the conceptual and empirical benefit of analyzing this transportation-land use interaction with a landscape pattern measure of activity-related composition and spatial configuration.

Steven Gehrke is a Ph.D. candidate in Civil and Environmental Engineering at Portland State University. His research examines the transportation-land use interaction,...

Schedule-based Public Transportation Planning Model and Integration with Other Transportation Planning Models

The video begins at 0:47.

Topic: Schedule-based Public Transportation Planning Model and Integration with Other Transportation Planning Models

Speaker Hyunsoo Noh, a PhD Candidate from the University of Arizona, will discuss the integration of schedule-based public transportation and other transporation planning models.

Identifying Market Segments based on Observed Travel and Activity Patterns

The video begins at 1:47.

Abstract: In transportation planning and engineering, market segments or groups of individuals with varying attitudes and travel behavior are often identified in order to define a set of policies and strategies targeted at each segment. Examples include residential location choice studies, electric vehicle adoption and the marketing of public transit options. Defining market segments is common in the marketing literature, typically based on observed socioeconomic characteristics, such as gender and income. However, in addition to these characteristics, travelers may also be segmented based on variations in their observed travel and activity patterns. The activity-based approach to travel demand analysis acknowledges the need to analyze the travel patterns of individuals, conceptualized as a trip chain or tour, as opposed to individual trip segments. This has implications for identifying markets segments based on travel patterns which needs to distinguish between the sequencing and timing of travel choices and activities, in addition to the actual travel choices and activities. One approach that holds promise is pattern recognition theory which has wide applications in image analysis, speech recognition and physiological signal processing. In this study, pattern recognition methods are applied to observed daily travel and activity patterns from Oregon to identify travel market...

Using a GPS Panel to Evaluate Travel Behavior Changes

The video begins at 5:40.

Current research at the Institute of Transport and Logistics Analysis of the University of Sydney, under Professor Peter Stopher, has been concentrating on using personal GPS devices to collect travel behaviour data of individuals. In this seminar, Professor Stopher will outline the several projects that have been conducted and are currently underway that are using GPS. He will describe the survey procedures, and then provide an overview of some of the results emerging from collection of such data. Of particular interest is that the GPS surveys are being conducted in most cases by using a panel, with at least two waves of data collection, and that panel members carry the GPS devices for anywhere from one week to one month. Initial studies of the variability in daily travel, where there are no fatigue effects from recording multiple days in a diary, are showing some interesting patterns and leading to some important conclusions.

Peter Stopher is Professor of Transport Planning at the Institute of Transport and Logistics Studies of the University of Sydney, a position he has held since the beginning of 2001. Previously he held academic positions and also worked as a full-time consultant in the USA since 1968. He obtained his B.Sc. (Eng) and Ph.D. from University College London in the 1960s. He has more than 40 years of experience as an educator and consultant in transport...

Student presentations from TRB, week 2

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View Nicholas Stoll's presentation slides

View Nicholas Kobel's presentation slides

Nicholas Stoll, Graduate Research Assistant, Portland State University

Topic: Utilizing High Resolution Bus GPS Data to Visualize and Identify Congestion Hot-spots in Urban Arterials

The research uses high resolution bus data to examine sources of delay on urban arterials. A set of tools were created to help visualize trends in bus behavior and movement, which allowed for larger traffic trends to be visualized along urban corridors and urban streets. By using buses as probes and examining aggregated bus behavior, contoured speed plots were used to understand the behavior of roadways outside the zone of influence of bus stops. These speed plots can be utilized to discover trends and travel patterns with only a few days’ worth of data. Congestion and speed variation can be viewed by time of day and plots can help indicate delays caused by intersections, crosswalks, or bus stops.

This type of information is important to transit authorities looking to improve bus running times and reliability. Congested areas can be detected and ranked. Speed plots...

Pedestrians in Regional Travel Demand Forecasting Models: State-of-the-Practice

The video begins at 0:49.

It has been nearly 25 years since non-motorized modes and non-motorized-specific built environment measures were first included in the regional travel demand models of metropolitan planning organizations (MPOs). Such modeling practices have evolved considerably as data collection and analysis methods improve, decisions-makers demand more policy-responsive tools, and walking and cycling grow in popularity. Many models now explicitly consider the unique characteristics of walking travel, separate from travel by bicycle. As MPOs look to enhance their models’ representations of pedestrian travel, the need to understand current and emerging practice is great.

This project presents a comprehensive review of the practice of representing walking in MPO travel models. A review of model documentation determined that – as of mid-2012 – 63% (30) of the 48 largest MPOs included non-motorized travel in their regional models, while 47% (14) of those also distinguished between walk and bicycle modes. The modeling frameworks, model structures, and variables used for pedestrian and non-motorized regional modeling are described and discussed. A survey of MPO staff members revealed barriers to modeling non-motorized travel, including insufficient travel survey records, but also innovations being implemented, including smaller zones and non-motorized network assignment. Finally, best practices in...

How Do People Choose a Travel Mode? Factors Associated with Routine Walking & Bicycling

The video starts at 0:58.

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Abstract: Walking and bicycling are being promoted as transportation options that can increase the livability and sustainability of communities, but the automobile remains the dominant mode of transportation in all United States metropolitan regions. In order to change travel behavior, researchers and practitioners need a greater understanding of the mode choice decision process, especially for walking and bicycling.

This presentation will summarize dissertation research on factors associated with walking and bicycling for routine travel purposes, such as shopping. More than 1,000 retail pharmacy store customers were surveyed in 20 San Francisco Bay Area shopping districts in fall 2009, and 26 follow-up interviews were conducted in spring and summer 2010. Mixed logit models showed that walking was associated with shorter travel distances, higher population densities, more street tree canopy coverage, and greater enjoyment of walking. Bicycling was associated with shorter travel distances, more bicycle facilities, more bicycle parking, and greater enjoyment of bicycling. Respondents were more likely to drive when they perceived a high risk of crime, but automobile use was discouraged by higher employment densities, smaller parking lots,...

Development of the Bellevue Real Time Arterial Traffic Flow Map

The video begins at 5:10.

Modeling Dependency with Copula: Implications to Engineers and Planners

The video begins at 0:47.

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The modelling of dependence relations between random variables is a typically studied subject in probability theory and statistics. In the recent decade, the concept of copula gained enormous success in finance and economics in the risk management and analysis context. Engineers started investigating the applications of copula in recent years; it has been widely used in Hydrology and climate studies to model rainfall and overspill risk. As a powerful tool to model dependence, copula has been applied to travel behavior modeling and model choice by several researchers. Dr. Wang will share his understanding of copula and its implications to engineers and planners in a more general uncertainty modeling framework. Some of the ongoing research efforts regarding how copula is being applied to transportation network entrance-ramp flow dependency and spatial-temporal travel time reliability study at Oregon State University.

Speaker Bio: Dr. Wang recently joined OSU from the Trine University in Angola, Indiana where he worked as assistant professor with the Reiners Department of Civil and Environmental Engineering. Before joining Trine University, he spent a short time as a research associate with Institute for Multimodal Transportation at...

Modeling Safety and Urban Form

Kristie Gladhill, Transportation Modeler, on Modeling Safety and Urban Form.

The video begins at 1:57.

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