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).
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.
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.
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 map (FCM) which is a directed graph that represents concepts of concern as nodes in the graph and causal relationships as edges.
The presentation will cover:
- Background on FCMs and their usefulness for modeling issues involving uncertainty;
- The mathematical formulation of an FSDM and how it differs from common FCM models;
- Open source software for building and running an FSDM; and
- Results of research with ODOT and OSU on modeling the potential effects of new transportation technologies and services using an FSDM.
The number of public bike share systems has been increasing rapidly across the United States over the past five to ten years. To date most academic research around bike share in the U.S. has focused on the logistics of planning and operationalizing successful systems. Investigations of system users and impacts on the local community are less common, and studies focused on efforts to engage underserved communities in bike share are rarer still. This paper utilizes a survey of representatives from 55 U.S. bike share systems to better understand and document current approaches toward serving low income and minority populations. The survey asked about equity policies and metrics, the degree to which equity considerations affected a variety of system practices, what the existing barriers to utilizing bike share are for target populations, and what challenges the bike share system entity faces in addressing those barriers. Results indicate that one in five systems have written policies around equity, though larger systems (over 500 bikes) were twice as likely to have such policies. However, many more systems incorporated equity into various aspects of their systems. Bike share systems incorporated equity into station siting, fee structure and payment systems, and promotion and marketing at much higher rates (68%, 72%, and 57% respectively), and into system operations and data collection and analysis to a lesser extent (42% each). Even so, the largest barriers facing systems are still cost, access, and outreach to users as well as overall funding and staff levels at the organization level.
Chris Johnson and Bud Reiff
Chris Johnson and Bud Reiff will present on a behavior-based freight model being used at Oregon Metro. This model will replace Metro’s current truck model with a hybrid freight model that both represents multi-modal freight flows through elements of national and regional supply chains and simulates the movement of individual trucks and shipments on local networks. Model estimation and calibration will also require collection of behavioral data from shippers and receivers representing a wide range of industries, common and contract freight carriers, business that operate non-freight commercial vehicles, warehouse managers, and logistics agents.
Key project objectives:
- Develop tools to enable a more comprehensive analysis of infrastructure needs and policy choices pertaining to the movements of goods.
- Develop more detailed network assignments by truck type, which support regional environmental analysis, as well as local traffic operations and engineering analysis.
- Develop freight forecasts that are responsive to changes in economic forecasts, changing growth rates among industrial sectors, and changing rates of economic exchange and commodity flows between sectors.
- Replace trip-based truck model with more realistic tour-based model.
Advances in technology such as the advent of autonomous vehicles (AV’s), the rise of E-commerce, and the proliferation of the sharing economy are having profound effects not only on how we live, move, and spend our time in cities, but also increasingly on urban form and development itself. These new technologies are changing the ease of transport, the role of transit, and the places we spend our time. These changes will have profound effects on cities including large shifts in land use, changes in street design, a potential reduction on the need for parking, a shift on where we choose to live, and challenges for urban density, the extent of sprawl, and the vitality of urban areas.
While there has been a focused effort of research on the technological aspects of autonomous vehicles and systems themselves, there has been a shortage of systematic exploration on their secondary effects on city development, form, and design, with implications for sustainability, resiliency, equity, cost, and general livability.
SCI’s Urbanism Next initiative focuses on the ramifications of changes in technology on the design and planning of our cities. This presentation will provide an overview to the initiative, research that has been done, and future objectives.
Inequities in Urban Mobility in Portland: Understanding Community Vulnerability and Prospects for Livable Neighborhoods
Gentrification and development are changing the face of many Portland neighborhoods. This talk will draw on data from focus groups and participatory mapping research with residents in SE and North Portland neighborhoods. The presentation will share findings on the patterns of movement reported by residents in gentrifying neighborhoods and will offer ideas and perspectives on how to plan for a sustainable future for all Portlanders.
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.
The presentation provides background information and illustrates driving forces of the development of municipal co-distribution of goods in Sweden. The business model is “somewhat unique” to Sweden, given the country’s comprehensive welfare sector through which local governments are often the main transport buyers in smaller municipalities without industries or commerce. In this respect, Sweden has been a pioneer in streamlining public administration at all levels replacing manual work procedures and paperwork with the use of computers and digital information, with an overall aim to allow for spending on social and political reform policies. The main business model used in municipal administration is purchases with free delivery whereby the transport of goods occurs directly from contracted suppliers to municipal receivers and where transport costs are included as a hidden surcharge in the product price. Contrary, municipal co-distribution of goods entails a physical and legal consolidation of all external purchases, where in its rudimentary form suppliers leave goods at a freight consolidation center (FCC) where goods are loaded for distribution in shared vehicles to receivers. Municipal co-distribution of goods has evolved from an isolated innovation developed in 1999 to an approach implemented in 39 municipalities of Sweden’s 290 by 2016, through which the business model and procurement practices has been refined with digital tools, first through e-commerce and then with vehicle routing software. Ultimately, the business model could be applied to non-municipal receivers but this would involve digitization and that municipalities regulate vehicle movements as done in public transport.
Over the last decade, the transportation agencies in Oregon have systematically enhanced many pedestrian crossings at mid-block locations with Rectangular Rapid Flashing Beacons (RRFBs), Flashing Yellow Beacons (Flash), and high visibility crosswalk markings (Hi-Vis). Enhancements often included the installation of refuge medians. This study explored the safety performance of these enhanced crossings, categorized by enhancement type. Data were collected on 191 crossings that included installation year, geometric features, surrounding land use, traffic volumes, and the number of crashes. Because pedestrian volume at the locations was unavailable, a pedestrian activity level variable was developed. Target crashes for analysis were identified as pedestrian and rear-end. The analysis of the before-after crash patterns showed a reduction in the pedestrian crash severity after the installation of the crosswalk enhancements. Risk ratios, calculated by the unadjusted crash frequency relative to the years of operation in each analysis category, were calculated. For pedestrian crashes, risk ratios increased with the number of lanes, posted speed, and estimated pedestrian activity level. Similar trends were observed for rear-end crashes. Due to sample size limitations, safety effectiveness was only estimated for the 19 RRFBs locations. Lack of pedestrian volumes limited the development of a safety performance function (SPF) for the pedestrian crash types. However, a rear-end crash SPF was estimated. Standard methods to estimate a crash modification factor (CMF) were attempted. The recommended CMF for pedestrian crashes is 0.64 +/- 0.26 using a simple before-after analysis and 0.93 +/- 0.22 for rear-end crashes using an empirical Bayes analysis.
The final report that the presentation is derived from is available: https://doi.org/10.15760/trec.168
While the Netherlands is known today for the highest bicycling rates in the world, this movement only began in the 1970s. Transportation policy has been one of the critical keys to reducing automobile trips in the Netherlands.
Visiting scholar Jan Nederveen will present on transportation planning in the compact, densely populated city of Delft. Delft has been a city since 1246, and the historic street pattern is still visible today. The city has grown to 100,000 residents and covers an area of 5 square kilometers. Twenty years ago, the council decided to change the transportation philosophy from a car-oriented system to a bike city with a car-free historic center. This policy has been very successful, and bikes are now the dominant mode.
Delft found a good balance in road design for both cars and bikes. Today, the bike network has reached the point of congestion. Solutions developed for cars are being introduced in the bike network. The presentation will cover the city's transport policy, road design, the concept of a car-free city, and the challenge of reducing bicycle congestion.
Washington County has 124 permanent roadside Bluetooth readers, which passively and in an anonymous fashion collect travel time, speed, and origin-destination information across the major arterials in the urban County. This presentation gives an overview of the program purpose, history, some interesting use cases, and the formation of comparative performance metrics to gauge the magnitude and duration of congestion across the County. These metrics and information will help planners improve travel demand models, consultants improve traffic analyses, operations staff prioritize timing, detection, and maintenance functions, agencies inform traveler information data, and leaders better communicate the story of traffic demand, delay, and congestion on our roadways over time.
As many cities are investing in street improvement or transportation infrastructure upgrade projects to provide better bike access or more complete bike networks, the economic value of bike infrastructure and bike facilities remains an area where many practitioners, planners and policy makers are seeking more conclusive evidence. Using residential property values as indicators of consumer preferences for bicycle infrastructure, this study focuses on advanced bike facilities which represent higher levels of bike priority or bike infrastructure investments that have been shown to be more desirable to a larger portion of the population. Estimating ordinary least squares hedonic pricing models and spatial autoregressive hedonic models separately for single and multi-family properties, we find that proximity to advanced bike facilities (measured by distance) has significant and positive effects on all residential property values, highlighting household preferences for high quality bike infrastructure. Furthermore, we also show that the extensiveness of the bike network (measured by density) is a positive and statistically significant contributor to the property prices for all residential property types, even after controlling for proximity to bike facilities and other property, neighborhood and transaction characteristics. Finally, estimated coefficients are applied to assess property value impacts of a proposed Portland “Green Loop” signature bike infrastructure concept, illustrating the importance of considering both accessibility and extensiveness of bike facility networks.
Patrick Allen 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 “the positive utility of travel” or PUT.
This study presents a conceptual and empirical look at the positive utility of travel and its influence on travel behavior. First, PUT is linked to concepts like utility, motivation, and subjective well-being, and categorized into destination activities, travel activities (multitasking), and travel experiences. Then, preliminary results from a 2016 survey of Portland-area commuters are presented. Finally, implications of the PUT concept for transportation planning and policy are discussed.