The seminars are supported by the Toulan School of Urban Studies and Planning and the Department of Civil and Environmental Engineering, and the National Institute for Transportation and Communities (NITC).
Part of the Student Presentations from TRB
Pedestrians and bicyclists are the most vulnerable road users and suffer the most severe consequences when crashes take place. An extensive literature is available for crash severity in terms of driver safety, but fewer studies have explored non-motorized users’ crash severity. Furthermore, most research efforts have examined pedestrian and bicyclist crash severity in urban areas. This study focuses on state roads (mostly outside major urban areas) and aims to identify contributing risk factors of fatal and severe crashes involving pedestrians and bicyclists in state roads. The results seem to suggest that besides improvements in roadway characteristics, additional countermeasures to reduce crash severity for vulnerable users should include educational campaigns, more strict control of alcohol intoxicated drivers, and protection strategies of senior pedestrians.
Travis Bradley Glick
Congestion and travel delay on urban roadways can influence operating costs and service attractiveness. This research uses high resolution bus data to examine sources of delay on urban arterials. A set of tools was created to help visualize trends in bus behavior and movement; this allowed 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 can be used to understand the behavior of roadways outside the zone of influence of bus stops. 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 can be utilized to reevaluate bus stop locations, e.g. near-side vs. far-side, and to identify locations where improvement are needed, e.g. queue jump lanes. Transportation agencies can also benefit from this type of information because arterial performance measures are difficult to estimate.
Development of origin-destination demand matrices is crucial for transit planning. The development process is facilitated by transit automated data, making it possible to mine boarding and alighting patterns on an individual basis. This research proposes a novel stochastic trip chaining method which uses Automatic Fare Collection (AFC) and General Transit Feed Specifications (GTFS) data to infer an origin-destination (O-D) matrix.
Jenny H. Liu
As urban areas across the country are investing in bicycle and pedestrian infrastructure to promote environmentally sustainable transportation and to develop livable communities, many have pointed to improvements in environmental quality, economic development and public health as potential positive outcomes. While these outcomes of active transportation infrastructure are relatively well documented, it is also known that both transportation and environmental amenities are typically unevenly distributed in the urban context. Studies show that those who are the most socioeconomically disadvantaged (i.e. low income, people of color, etc.) are also those who disproportionately experience transportation disadvantages.
This study contributes to the existing literature by specifically linking bicycle accessibility to spatial equity analysis, using both an existing 2016 Baseline Scenario and a 2035 City Greenways Scenario in Portland, Oregon to illustrate. Two distinct types of bicycle accessibility measures are calculated: a distance-based measure (based on proximity to bicycle facilities) and a gravity-based opportunities measure (based on accessible opportunities and destinations). Improvements in bicycle accessibility are then spatially analyzed within communities identified as historically marginalized, across quintiles of identified neighborhoods and between identified communities and other areas.
Our findings suggest that although bicycle infrastructure investments generally provide greater proximity for all residents, accessibility improvements are not quite as apparent when considering access to opportunities and destinations using the second bicycle accessibility measure. The results of the various spatial equity analyses underscore the importance of integrating land use factors into transportation accessibility measures, particularly in the context of equitable access to opportunities for everyone.
Walkability and walking are being intensively researched today and the literature provides a wealth of references and examples on how to measure walkability of the built environment. IAAPE is one method that was developed at the Instituto Superior Técnico (Lisbon) to measure walkability at the micro-scale, bringing solutions that were disregarded in two aspects: it is a participatory process; and it provides different evaluations for different population segments (adults, children, seniors, impaired) or for different trip motivations.
Presentation on Urban Transportation Planning and Transit-oriented development (TOD) Research in Japan.
Density Differences: Exploring Built Environment Relationships with Walking Between and Within Metropolitan Areas
Part of the Student Presentations from TRB
To explore the relationships between measures of density and walking within and between urban areas, we present an analysis of the travel survey data from six different cities from the US and Santiago, Chile. The analysis of aggregate and disaggregate pedestrian trips presented here examine the potential consistency of relationships between walking and density within and across different regions, with a specific focus on population density. Our findings illustrate a relationship between population density and walk mode shares that is roughly linear and of nearly equal magnitude across US regions in densities below 20 persons/acre. As work in this area matures, fine-grained built environment measures should be complemented with constructs that describe the metropolitan structure, including density distributions and gradients, poly-centricity, and spatial extent of the urban area.
Part of the Student Presentations from TRB
Researchers have been parsing which components of the built environment contribute to outcomes of interest and to what degree, particularly the effects on vehicle use and walking. Increasingly, researchers and practitioners recognize that the type of neighborhood may affect individual travel behaviors. These bundle of various land use and transportation system characteristics can be constructed as different neighborhood or place types. But not all place types are constructed with the same use, purpose, or methods. This presentation will review three classifications of place typologies to better understand their purpose and appropriate application as well as introduce an online transportation platform that will incorporate aspects of place type.
Investments into active transportation infrastructure are often promoted as a strategy for sustainable transportation, better public health, environmental quality, and economic development. Although empirical evidence generally points toward positive property value impacts of off-street greenways and trails, few focus on whether households might have different willingness-to-pay for different types and levels of bicycle infrastructure. This paper aims to fill research gaps in understanding consumer preferences for different types of bicycle facilities by examining property value impacts of four bicycle facility types: on-street advanced bike facilities and bike lanes; and off-street regional multi-use paths and local multi-use paths. Using Portland, Oregon as a case study, this paper applies spatial hedonic pricing models, and characterizes each facility type by both ease of access (distance) and extensiveness of bike network (density) within a range of buffer zones.
We find strong evidence that households prefer to be located close to advanced bike facilities and enjoy a denser network. However, these impacts are not consistent across all types of bicycle facilities. Bike lanes tend to contribute negatively to property values. Model estimations also indicate some positive consumer preference for proximity to local multi-use paths, generally located within urban greenspaces. In addition, extensiveness of on-street bicycle facilities show positive and statistically significant impacts on property values, with diminishing effects as the buffer zone radius is increased. The results of this study should provide practical evidence for planners and policy makers in understanding the range of consumer preferences for various types of bicycle infrastructure investments.
How can we go one step, or one lane, further than the standard road diet? Roundabouts allow a road diet to reduce the final number of lanes from three to two.
Questions arise when roundabouts are used with a road diet. What traffic volumes are supportable? Will the roundabouts fit within existing intersections? What does current guidance tell us about this approach?
Michael Williams will present his work on creating a sequel to FHWA’s Road Diet Informational Guide. This work is intended to provide a feasibility determination tool for the application of this approach to existing corridors. Data from Bird Rock Boulevard in La Jolla, CA is presented as an example from which important lessons are drawn.
Part of the Student Presentations from TRB
Integrated land use and transportation models have evolved along a spectrum with simplistic sketch planning models on one end and sophisticated microsimulation models on the other. While each type of these models has its niche, they are largely unable to balance the flexibility and realism of microsimulation and the speed and interactiveness of simple models. The Regional Strategic Planning Model (RSPM) aims to fill this gap by taking a microsimulation approach but making other simplifications, to model first-order long-term outcomes of land use and transportation quickly. It takes into consideration the underlying uncertainties of long-term modeling by accepting a broad range of policy inputs and technology assumptions while allowing rapid simulations of hundreds of scenarios. The RSPM is one of a few operational modeling packages (along with EERPAT and RPAT) that have evolved from GreenSTEP, a microsimulation modeling package for state-level evaluation of strategies for reducing transportation energy consumption and greenhouse gas (GHG) emissions. Several ongoing projects are aiming to develop a common software framework for the family of strategic modeling tools and improve the policy sensitivity of multi-modal travel. In this study, we introduce the RSPM framework, and then primarily focus on the new development of a multi-modal travel demand module that links various policy inputs to households’ multi-modal travel and further to aggregate transportation outcomes (e.g. GHG emissions, traffic fatalities). We discuss our choice of model structures and specifications and then estimate the models utilizing a unique US nationwide dataset combining the 2009 US National Household Travel Survey (NHTS), EPA’s Smart Location Database, and the National Transit Database. This comprehensive dataset provides a rich set of variables capturing household social-demographics, multi-modal travel, built environment, and transportation supply. We conclude the paper with the results of validation and sensitivity tests, and a discussion of future work.
E-hailing plays a key role in emerging transportation services such as ridesourcing, ridesharing and taxis, among others. This seminar will present a general economic model to analyze the congestion effect of e-hailing services in a transportation network.
The model can help analyze customers’ choices of different modes, based on their value of time and the charging schemes of different services, as well as the overall impact of the services to network level congestion.
Providing affordable housing and reducing greenhouse gases are common goals in cities worldwide. Transit-oriented development (TOD) can provide an opportunity to make incremental progress on both fronts, by building affordable housing near transit and by providing alternative transport modes such that households reduce driving. While the existing literature has focused on the relationship between TOD and housing and TOD and greenhouse gas emission reduction as separate issues, it has seldom touched on the possibility that TOD could address both goals jointly. We provide evidence to show that focusing on either housing affordability or greenhouse gas emission reduction in isolation can lead to strategies that achieve one goal to the detriment of the other. Using the case of Los Angeles, we develop a scenario planning model that allows simultaneous consideration of housing and transportation goals, and illustrates the tradeoffs of different policy approaches. The results show that larger increases in residential densities combined with a small inclusionary housing requirement yields greater benefits, in terms of both reduced driving and more affordable housing, than would a higher inclusionary percentage with smaller increases in density.
Peter G. Bosa
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 gas emissions, congestion, and transportation network reliability. In addition, static assignments cannot fulfill a need for small-duration travel time increments required by the next generation activity-based models.
The shortcomings of the static assignment necessitates Metro’s development and application of regional dynamic traffic assignment (DTA) models. The resolution of these models allows for continuous modeling of traffic over an analysis period, which allows the analyst to capture temporally-based traffic events such as the building and dissipation of queues, measurement of the duration of congestion, and high fidelity speed profiles for use in emissions analysis.
This presentation will focus on why Metro has developed a DTA, how DTA compares with other models—specifically macro-scale static assignments and micro-simulations—and how DTA has been applied in Metro's modeling process.
Measuring Stress Levels for Real-World On-Road Cyclists: Do Bicycle Facilities, Intersections and Traffic Levels Affect Cyclists' Stress?
This research effort presents a novel approach to measure cyclists’ stress: real-world, on-road measurements of physiological stress as cyclists travel across different types of bicycle facilities in various traffic volumes. This study addresses the question of how the characteristics of a bicycle trip affect stress levels using physiological data, specifically GSR. As detailed in the next section, GSR-based studies have been successfully employed for many years in the psychological field to recognize and associate emotions and behaviors to physiological responses. The three research questions examined in this study are: i) Does peak traffic impact cyclists’ stress levels? ii) Do intersections impact cyclists’ stress levels? and iii) Does facility type impact cyclists’ stress levels?
The video of the presentation is located here: https://echo360.pdx.edu/ess/echo/presentation/3b8a6987-6c8e-4407-bace-e4e627520a97
E-bikes, E-Cars, Carshare, Bikeshare, and Micro-EVs in China have shaken up the traditional motorization pathways that have occurred in developing countries in the past. The combination of emerging vehicle technologies, urban and environmental constraints, and heavy-handed policy make China's motorization processes unique in the world—but how China motorizes has far-reaching impacts based on sheer volume of vehicles and population.
This seminar discusses the results of a six-year NSF CAREER project to explore China's motorization processes, combining behavioral and environmental modeling approaches to assess the impacts of emerging vehicle technologies on motorization and ultimately environmental sustainability. The focus is mostly on emerging lightweight EVs that have surprisingly surpassed all other modes of personal mobility in annual sales and hold great promise across different shared and personal vehicle technologies.
Emerging probe data sources from smartphones on on-board devices are able to measure behavior of cyclists with very high resolution. From this, for the first time, we are able to measure relatively precise behavior that allows new insights into exposure, route choice, safety behavior, or technology choice. Probe data, merged with other data sources, can begin to develop a more complete picture of cyclists on-road behavior.
This presentation will offer examples of analyses done to investigate cyclists behavior using app-based and on-board GPS data in the context of individual cyclists behavior (i.e., app users) and behavior of bikeshare users (i.e., on-board GPS fleet tracking devices). The applications will cover route choice, travel patterns, surrogate safety behaviors like wrong-way riding, and will investigate differences between conventional- and electric-bike users.
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 be estimated using a minimum of two short-term counts or one full year of a continuous count.
The proposed sinusoidal model has been developed and validated using bicycle count data from a total of 47 permanent bicycle counters in six cities and four bike-sharing systems in North America.
Next, a corridor-based crash and AADB assignment is performed to relate crash with volume data. These data are then used in parallel with motorized vehicle counts in a crash rate equation that accounts for exposure of bicyclists to both vehicles and other bicyclists.
Results show that the proposed “double exposure” crash rate for bicyclists unveils high risk locations for bicyclists that would have been obscured by the high vehicle volumes if the typical crash rate per AADT or AADB were used.
A Conceptual Framework for Understanding Latent Demand: Accounting for Unrealized Activities and Travel
Latent demand—the activities and travel that are desired but unrealized because of constraints—have been historically examined from the standpoint of understanding the impacts of proposed capacity or service improvements on travel demand.
Drawing on work from a variety of theoretical perspectives, this paper presents a broader conceptual view of latent demand that provides a useful framework for researching and understanding these unmet needs. This is important from an equity standpoint, as it provides insights into to questions of transport disadvantage, social exclusion and poverty.
The framework presented here is theoretical in nature and untested empirically. This study aims to promote discussion and ultimately a more developed theory that can inform transportation planning and forecasting. A better definition and quantification of latent (or induced) demand can aid transportation planners to better predict the impacts of future transportation investments and other social, economic and technological changes.
Getting to Know the Data: Understanding Assumptions, Sensitivities, Uncertainty, and Being "Conservative" While Using ITE's Trip Generation Data in the Land Development Process
Kristina Marie Currans
Many agencies rely on trip generation estimates to evaluate the transportation impacts of land development in urban and suburban areas alike. Over the past decade, substantial attention has been paid to one national set of guidelines—the Institute of Transportation Engineers (ITE) Trip Generation Handbook (2014) and corresponding Manual (2012)—focusing in particular to improve the use of these data and supplementary methods for urban contexts.
The purpose of this study is to explore the typical data provided in the Handbook, within the context of these new improved state-of-the-art methods. As ITE’s describes, “an example of poor professional judgment is to rely on rules of thumb without understanding or considering their derivation or initial context” (Institute of Transportation Engineers, 2014, p. 3). This research aims to improve the understanding of these data—still ubiquitously used across the US—to encourage increased engagement with their meaning, and following, to provide the users (e.g., engineering, planners, agencies, and developers) with the landscape from which these data were collected and for which they represent. From here, more informed decisions can be made about whether these data provide an adequate or accurate estimation transportation impacts within varying contexts and applications.
Steven R. Gehrke and Kelly J. Clifton
Land development patterns, urban design, and transportation system features are inextricably linked to pedestrian travel. Accordingly, planners and decision-makers have turned to integrated transportation-land use policies and investments to address the pressing need for improvements in physical activity levels via the creation of walkable communities. However, policy questions regarding the identification of smart growth indicators and their connection to walking remain unanswered, because most studies of the built environment determinants of pedestrian travel: (a) represent the built environment with isolated metrics instead of as a multidimensional construct and (b) model this transportation-land use relationship outside of a multidirectional analytic framework. Using structural equation modeling, this Portland, Oregon study identifies a second-order latent construct of the built environment indicated by land use mix, density, and urban design and transportation system features. Study findings suggest this construct has a strong effect on the household-level decision to walk for transport and discretionary travel.
The video of the presentation is located here: https://echo360.pdx.edu/ess/echo/presentation/3b8a6987-6c8e-4407-bace-e4e627520a97
Metro, Portland's regional governing agency, conducts annual two-hour counts along its regional trail every September. This upcoming fall (2017) will be the 10th year that the counts have been held, which means we at Metro can finally start seeing noticeable, long-lasting trends in the regional trail network. Perhaps more importantly, we are seeing how these data have directly impacted investments in future trail, bicycle, and pedestrian projects.
This seminar will cover the history of the program, details of how it's conducted and why it's conducted that way, how data are used (including an inside look at future iterations of Metro's interactive trail count map), and why creating local extrapolation factors for adjusting to annual traffic is so important.
The problem of bus bunching in a high frequency service has been largely studied in the literature.
This phenomenon is produced by three main factors
(i) the variability in travel time between stops; (ii) variations in passenger demand; and (iii) drivers’ heterogeneity.
In order to tackle this phenomenon a wide range of control strategies have been proposed, however, none of them had been successfully implemented on a large transit network with high frequency services.
In this talk, we present a control scheme based on a rolling horizon optimization problem that has been successfully implemented for real-time control of two high frequency services in Santiago, Chile.
Finally, the main results and challenges on the implementation phase are discussed.
Estimating Reliability Indices and Confidence Intervals for Transit and Traffic at the Corridor Level
Travis B. Glick
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 peak-hour low performance from whole-day low performance. As a case study, the methodology is applied to a bus transit corridor in Portland, Oregon. Time-space speed profiles, heatmaps, and visualizations are employed to highlight sections and intersections with high travel-time variability and transit low performance. Segment and intersection travel-time reliability are contrasted against analytical delay formulas at intersections with positive results. If bus stop delays are removed, this methodology can also be applied to estimate regular traffic travel-time variability.
Utilizing High-Resolution Archived Transit Data to Study Before-and-After Travel-Speed and Travel-Time Conditions
Travis B. Glick
Travel times, operating speeds, and service reliability influence costs and service attractiveness. This research outlines an approach to quantify how these metrics change after a modification of roadway design or transit routes using archived transit data. The Tri-County Metropolitan Transportation District of Oregon (TriMet), Portland’s public transportation provider, archives automatic vehicle location (AVL) data for all buses as part of their bus dispatch system (BDS). This research combines three types of AVL data (stop event, stop disturbance, and high-resolution) to create a detailed account of transit behavior; this probe data gives insights into the behavior of transit as well as general traffic. The methodology also includes an updated approach for confidence intervals estimates that more accurately represent of range of speed and travel time percentile estimates. This methodology is applied to three test cases using a month of AVL data collected before and after the implementation of each roadway change. The results of the test cases highlight the broad applicability for this approach to before-and-after studies.