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Urban Freight Lab

Research Topic: Surface Transportation

Surface transport is the movement of people or goods across the surface, including road, rail, water, and pipeline, and excluding air.

West Seattle Bridge Case Study (Phase II)

This project is a continuation of the West Seattle Bridge Case Study Phase I.

Background: 
West Seattle (WS) is an area of the city of Seattle, Washington, located on a peninsula west of the Duwamish waterway and east of the Puget Sound. In March 2020, the West Seattle High Bridge (WSHB), the main bridge connecting WS to the rest of the city, was closed to traffic due to its increasing rate of structural deterioration.

The Seattle Department of Transportation (SDOT) has engaged the Supply Chain Transportation and Logistics Center (SCTL) at the University of Washington, to conduct research to understand current freight movements and freight demands in WS and identify challenges related to the bridge closure to inform data-driven mitigation strategies.

In project Phase 1 the research team performed a freight trip generation (FTG) estimation and conducted interviews with local business establishments, carriers, and the Port of Seattle. As a result of the FTG modeling, the research team estimated that 94 percent of the freight trips generated by WS are destined to residential buildings. Moreover, the interviews identified disruptions in the supply chains of small and medium-size local businesses as well as carriers facing longer travel times to access the peninsula.

Research Objectives: 
In Phase 2 of the project, the research team will shift the focus from business establishments to consumers. In particular, we will explore consumer behavior, defined as how people choose to buy goods and services and where they buy them, to better understand residential demand and accessibility of goods for WS residents.

This study will make use of a consumer survey for Seattle residents to:

  • Describe consumer behavior and buying habits for Seattle residents, in particular, we will address how (online vs. in-person and with which travel mode), where (locally or not-locally), and how often people shop.
  • Better understand what drives consumer behavior, in particular how consumer behavior is impacted by urban form (transport infrastructure available, land uses, urban density, etc.), access to transportation, local access to stores, and socioeconomic characteristics.

Tasks:

  1. Gather public datasets and review previous consumer surveys: The research team will review and summarize publicly available datasets that contain information on consumer behaviors and urban form for Seattle residents, for instance, the Puget Sound Regional Council (PSRC) data, the National Household Travel Survey (NHTS), the Freight Trip Generation (FTG) estimates from Phase 1, the Google Maps APIs and the publicly available Seattle Department of Transportation (SDOT) GIS layers. The research team will also scan the scientific literature and reports to inform the design of the survey on consumer behavior.
  2. Survey Design: The research team will design a consumer survey and a method of survey distribution. The survey will include socioeconomic data (e.g. age, gender, income, education, household composition, car ownership), geographical location (where the interviewee lives), consumer behavior (e.g. types of goods purchased, the amount spent, where goods are purchased, mode of travel, whether goods were purchased online or in-person, how often the purchases take place). SDOT will be provided the opportunity to review and give comments on the draft survey before the survey roll-out.
  3. Survey roll-out: The approved survey will be distributed to residents of the agreed study area. The survey will be drafted as an online survey. SDOT will reserve the option to further expand the survey reach, for instance by creating and distributing a paper version of the survey, translating the survey to other languages, use SDOT channels to distribute the survey.
  4. Analysis of survey data: Data from the survey will be analyzed. A descriptive statistical analysis will be performed, addressing questions such as how people consume, how far people travel to purchase goods, what is the preferred mode of transportation for shopping trips, and how frequently people purchase things online vs. in person. A second part of the analysis will focus on understanding the relationship between socioeconomic variables and urban form variables with consumer behavior variables.
  5. Reporting: A final report will be drafted reporting on the survey design and method, a data description, and data analysis addressing the project goals. SDOT will review and confirm the final report before publication on the SCTL website.

Deliverables: Final project report and executive summary

Budget: $60,000
Timeline: January to December 2022

Article

A Framework to Assess Pedestrian Exposure Using Personal Device Data

 
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URL: https://doi.org/10.1177/1071181322661319
Publication: Human Factors and Ergonomics Society
Volume: 66 (1)
Pages: 320 - 324
Publication Date: 2022
Summary:

Capturing pedestrian exposure is important to assess the likelihood of a pedestrian-vehicle crash. In this study, we show how data collected on pedestrians using personal electronic devices can provide insights on exposure. This paper presents a framework for capturing exposure using spatial pedestrian movements based on GPS coordinates collected from accelerometers, defined as walking bouts. The process includes extracting and cleaning the walking bouts and then merging other environmental factors. A zero-inflated negative binomial model is used to show how the data can be used to predict the likelihood of walking bouts at the intersection level. This information can be used by engineers, designers, and planners in roadway designs to enhance pedestrian safety.

Authors: Haena Kim, Grace Douglas, Linda Ng Boyle, Anne Moudon, Steve Mooney, Brian Saelens, Beth Ebel
Recommended Citation:
Douglas, G., Boyle, L. N., Kim, H., Moudon, A., Mooney, S., Saelens, B., & Ebel, B. (2022). A Framework to Assess Pedestrian Exposure Using Personal Device Data. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. https://doi.org/10.1177/1071181322661319
Technical Report

Transit Corridor Study

 
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URL: https://mic.comotion.uw.edu/wp-content/uploads/2021/07/TransitCorridorStudy_InterimReport_June2021.pdf
Publication Date: 2021
Summary:

This study is sponsored by Amazon, Bellevue Transportation department, Challenge Seattle, King County Metro, Seattle Department of Transportation, Sound Transit, and Uber, with support from the Mobility Innovation Center at UW CoMotion.

Population and extended economic growth in many Seattle neighborhoods are driving increased demand for private car travel along with transportation services such as ridehailing and on-demand delivery. Together, these trends are adding to existing demand for loading and unloading operations throughout the city, and exacerbating traffic congestion. Anecdotal evidence indicates that passenger/delivery vehicle stops at or next to transit stops can interfere with bus operations, causing longer or more volatile delays. The increased travel times and reduced reliability further erode the attractiveness of transit to travelers. Thus, it is important to understand how transit, ridehailing, and goods delivery vehicles interact in terms of both operations and travel demand.
This project focuses on the analysis of open-source transit data to screen for locations with slow and/or unreliable bus travel times, and couple that data with interference observation, environmental, and traffic-related data to potentially predict the likely causes. We have developed tools to identify transit corridors with high levels of interference from other road users, including passenger cars, ridehailing vehicles and goods delivery vehicles. These tools are applied to transit corridors in Seattle and Bellevue, and methods have been developed to identify likely sources of interference from available data.
We drew on multiple data sources for identifying high-interference corridors in the region, including:
  • a virtual workshop with participants from beneficiary agencies and stakeholders to solicit input;
  • an online crowdsourcing survey to engage the community and gather feedback from all road users;
  • route-level ridership data from King County Metro; and
  • aggregated pick-up/drop-off data on ridehailing activities from SharedStreets.
Data was consolidated and 10 corridors were selected based on their likelihood of containing interference between buses and other road users, transit ridership levels, and stakeholder and community feedback.
In addition, we have developed a tool for identifying corridors with slow and/or unreliable bus travel times from open-source real-time transit data. We implemented a pipeline for ingesting and analyzing King County Metro’s real-time Generalized Transit Feed Specification data (GTFS-RT) at 10-second intervals. Using this pipeline, active bus coordinate and schedule adherence data has been scraped and stored to an Amazon Web Services (AWS) server since September 2020. We developed efficient methods to aggregate tracked bus locations and assign them to roadway segments, and quantified delays in terms of schedule deviation and ratio of median to free-flow speeds, among other metrics. We have developed a web based visualization tool to display this data, and it is being updated daily with aggregated performance metrics from our database.
To collect ground truth validation data along selected corridors, we implemented an online data collection tool for field observations, and recruited research assistants to observe bus operations along the study corridors and record information on bus traversals and instances of interference. This dataset is analyzed alongside the GTFS-RT data, environmental, and traffic related data to identify instances of delay and predict the likely causes.
Field data was collected for three weeks along eight of the selected corridors in March 2021, but was later paused due to depressed levels of transportation activity during the COVID-19 pandemic and the current unstable condition of travel choices and city traffic (and thus interferences). Preliminary analysis on the collected data revealed that there is not a substantial effect shown in the GTFS-RT data when a bus is interfered with; however, there were not a lot of interference observations in the collected field data. So, it remains to be seen whether the lack of an identifiable effect is due to the lack of ground truth data, lack of precision in the automatic vehicle location system, or the relatively low impact of an interference when compared to the effects of general traffic congestion, signals, and other roadway conditions. A linear regression model was also generated to determine the extent to which roadway characteristics can predict segment performance, which produced mildly predictive results.
As businesses and transit services continue to reopen, there will likely be an increase in the amount of transit interference experienced between buses and other roadway users, which will potentially allow for the gathering of more ground truth validation data. Field observations will resume in late Summer/early Fall 2021 and will continue until enough data is collected to either (1) model connections between observed interference and bus delays in the GTFS-RT data; or (2) determine whether significant delays cannot be linked to observed instances of interference in the study corridors. The GTFS-RT data scraping will continue daily, and summarized in the developed interactive visualization tool.
The major anticipated benefits of the project can be summarized as follows:
  • This work will help identify network-wide road and route segments with slow and/or unreliable bus travel times. We may also be able to identify main causes of delay in the study corridors.
  • Moreover, we expect that this work will generate reusable analytical tools that can be applied by local agencies on an ongoing basis, and by other researchers and transportation agencies in their own jurisdictions.
  • The outcomes of this work will enable identifying corridors with slow and/or unreliable bus travel times as candidates for specific countermeasures to increase transit performance, such as increased enforcement, modified curb use rules, or preferential bus or street use treatments. Targeting such countermeasures towards priority locations will result in faster and more reliable bus operations, and a more efficient transportation network at a lower cost to transit agencies.
Authors: Dr. Andisheh Ranjbari, Zack Aemmer, Borna Arabkhedri, Don MacKenzie
Paper

The Impact of Weather on Bus Ridership in Pierce County, Washington

 
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URL: http://doi.org/10.5038/2375-0901.15.1.6
Publication: Journal of Public Transportation
Publication Date: 2012
Summary:
A factor that influences transit ridership but has not received much attention from researchers is weather. This paper examines the effects of weather on bus ridership in Pierce County, Washington, for the years 2006–2008. Separate ordinary least squares regression models were estimated for each season, as weather conditions may have different effects depending on the time of year. Four weather variables were considered: wind, temperature, rain, and snow. High winds negatively affected ridership in winter, spring, and autumn. Cold temperatures led to decreases in ridership in winter. Rain negatively affected ridership in all four seasons, and snow was associated with lower ridership in autumn and winter. These results suggest that adverse weather conditions can have a negative effect on transit ridership.

 

 

Authors: Dr. Ed McCormack, Victor W. Stover
Recommended Citation:
Stover, V. W., & McCormack, E. D. (2012). The Impact of Weather on Bus Ridership in Pierce County, Washington. Journal of Public Transportation, 15(1), 6.

Developing Better Curb Management Strategies through Understanding Commercial Vehicle Driver Parking Behavior in a Simulated Environment

Project Budget: $180,000 (UW amount: $80,000)

Lead Institution:

  • University of Washington, Urban Freight Lab (UFL)

Partner Institutions:

  • Oregon State University

Summary:

This study will use a driving simulator to design a simulation experiment to test the behavior of commercial vehicle drivers under various parking and delivery situations and to analyze their reactions. The ability to modify the simulator’s environment will allow the researchers to relatively easily test a range of scenarios that correspond to different delivery and parking situations.

The simulation experience will be designed in a quarter-cab truck simulator at Oregon State University’s Driving and Bicycling Simulator Laboratory. Various simulation environments will be defined by changing road characteristics (such as land use, number of travel lanes, nearby signals, traffic in adjacent lanes), curb allocations (such as paid parking, commercial vehicle loading zones, and passenger load zones, as well as the size of the loading zones and their availability at the time of the vehicle arrival at the blockface), and other road users (passenger cars, ridehailing vehicles, bikes). Drivers from various categories of age, gender, experience level (less experiences vs. seasoned) and goods type (documents, packages, or heavy goods) will be invited to operate the simulator and make a parking decision in a few simulated environments. The simulator can also monitor distraction (through eye tracking) and the stress level of drivers (through galvanic skin response) when making these decisions and interacting with other road users.

Analyzing parking decisions and driver stress levels based on roadway and driver characteristics will provide insights on travel behaviors and the parking decision-making process of commercial vehicle drivers, and will help city planners improve street designs and curb management policies to accommodate safe and efficient operations in a shared urban roadway environment.

The unique needs of delivery trucks and commercial vehicles are not acknowledged in current design practices. This study is intended to fill these gaps and serve as a valuable resource for policy makers, transportation engineers and urban planners.

Student Thesis and Dissertations

Optimization Modeling Approaches to Evacuations of Isolated Communities

URL: http://hdl.handle.net/1773/49047
Publication Date: 2022
Summary:

Isolated communities are particularly vulnerable to disasters caused by natural hazards. In many cases, evacuation is the only option to ensure the population’s safety. Isolated communities are becoming increasingly aware of this threat and demand solutions to this problem. However, the large body of existing research on evacuation modeling usually considers environments where populations can evacuate via private vehicles and by using an existing road infrastructure. These models are often not applicable to remote valleys and islands, where road connections can be disrupted or do not exist at all. The use of external resources is therefore essential to evacuate the population. How to systematically evacuate an isolated community through a coordinated fleet of resources has not yet been researched. This dissertation thesis addresses this knowledge gap by designing a new routing problem called the Isolated Community Evacuation Problem (ICEP) that optimally routes recovery resources between evacuation pick-up points and shelter locations to minimize the total evacuation time. The research presents derivations of the initial model for (a) emergency planning and (b) response purposes to give emergency planners and researchers tools to prepare for and react to an evacuation of an isolated community. For (a), a scenario-based two-stage stochastic program with recourse considers different emergency scenarios to select the optimal set of recovery resources to hold available for any evacuation emergency. Furthermore, the dissertation explores efficient structure-based heuristics to solve the problem quickly. For (b), the assumption of certainty over the size of the affected population at the time of evacuation is relaxed. Approaches from robust and rolling-horizon optimization are presented to solve this problem. Moreover, meta-heuristics are explored to solve the problem to optimality while overcoming the complexity of the problem formulation. Finally, an in-depth, real-world case study that was conducted in collaboration with first responders and emergency authorities on Bowen Island in Canada is presented to test and evaluate the applicability of the proposed models. This case study further informed the official evacuation plan of the island. This collaboration demonstrates the potential of full integration of the research approach with local emergency expertise from the affected area and highlights the data requirements that need to be met to maximize the use of the model.

Authors: Fiete Krutein
Paper

Site-Specific Transportation Demand Management: Case of Seattle’s Transportation Management Program, 1988–2015

 
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URL: https://doi.org/10.1177/03611981211035765
Publication: Transportation Research Record: Journal of the Transportation Research Board
Publication Date: 2021
Summary:

A central theme of U.S. transportation planning policies is to reduce single-occupancy vehicle (SOV) trips and promote transit and non-motorized transportation by coordinating land-use planning and transportation demand management (TDM) programs. Cities often implement TDM programs by intervening with new development during the municipal permit review process. Seattle’s Transportation Management Program (TMP) under a joint Director’s Rule (DR) requires a commitment from developers to adopt select strategies from six TDM element categories: program management, physical improvements, bicycle/walking programs, employer-based incentives, transit and car/vanpooling, and parking management. TMP targets new developments and requires some TDM elements, recommends others, and leaves the rest to negotiation. The result is an individualized TMP agreement that is site-specific, reflecting both city policy and developer needs. This case study presents a qualitative analysis of the guiding eight DRs and 41 site-specific TMP agreements in Seattle’s Downtown and South Lake Union (SLU) area since 1988. Overall, a content analysis of TMP documents reveals that the average number of elements adopted in an agreement falls short of requirements set by DRs (34%–61%). Major findings include developer preference toward non-traditional TDM measures such as physical improvement of frontage and urban design features, as well as parking management. High-occupancy vehicle (HOV) elements showed higher adoption rates (59%–63%) over biking/walking programs (1%). It is concluded that future TDM policies could benefit if future research includes examining the effectiveness of the range of management options stemming from the real estate trends toward green buildings, tenants’ values in sustainability, and city policy to reduce automobile trips.

Authors: Dr. Ed McCormack, Mairin McKnight-Slottee, Chang-Hee Christine Bae
Recommended Citation:
McKnight-Slottee, Bae, C.-H. C., & McCormack, E. (2022). Site-Specific Transportation Demand Management: Case of Seattle’s Transportation Management Program, 1988–2015. Transportation Research Record, 2676(1), 573–583. https://doi.org/10.1177/03611981211035765. 
Student Thesis and Dissertations

Seattle Bicycle Share Feasibility Study

 
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URL: https://nacto.org/wp-content/uploads/2015/07/2011_McCormack-et-al_Seattle-Bicycle-Share-Feasibility-Study.pdf
Publication: University of Washington, College of Built Environment, Department of Urban Planning and Design
Publication Date: 2011
Summary:

This report assesses the feasibility of a public use bike-share system for Seattle, Washington. Colloquially referred to as “bike-share” or “bike-sharing,” such systems are considered a form of public transportation. Bike-share bicycles are intended for short-term use and are accessible via automated check-out systems. An important benefit of bike-share systems is the flexibility to return rented bicycles to any station within the system, thereby encouraging use for one-way travel and the “final mile” of a trip.

The four major chapters of this report represent the organization of our research and analysis. The topic areas are:

  • Introduction: Bike-share history and the structure of our study
  • Demand Analysis: Our analytic and forecast methodologies along with results of their application
  • Policy Framework: Consideration of governance institutions and their effects on system implementation
  • Bike-Share Program Recommendations: Summation of our findings and recommendations for how Seattle should proceed

During our analysis, we looked at demand for bike-share in Seattle. We have concluded that demand is sufficient to support a program. Our final recommendation includes three implementation phases, beginning with the downtown and surrounding neighborhoods.

However, despite anticipation of program demand, there are institutional policy challenges that must be addressed before successful implementation. Prominent among these are:

  • The King County helmet law
  • City of Seattle sign codes
  • Policies that affect station design and use of curbspace

In the case of the latter two, individual neighborhoods and districts may each have their own, unique impacts. Fortunately, Seattle has the flexibility to address these issues, and there are systems in place to overcome these challenges. Once addressed, we recommend the City move forward with implementing a bikeshare program.

Authors: Dr. Ed McCormack, Jennifer Gregerson, Max Hepp-Buchanan, Daniel Rowe, John Vander Sluis, Erica Wygonik, Michael Xenakis
Recommended Citation:
Gregerson, J., Hepp-Buchanan, M., Rowe, D., Vander Sluis, J., Wygonik, E., Xenakis, M., & McCormack, E. (2011). Seattle bicycle share feasibility study. University of Washington, College of Built Environment, Department of Urban Planning and Design.

A Data-Driven Simulation Tool for Dynamic Curb Planning and Management

Project Budget: $2.9M (UW amount: $500k)

Lead Institution:

  • Pacific Northwest National Lab (PNNL)

Partner Institutions:

  • Urban Freight Lab (UFL), University of Washington
  • Lawrence Berkeley National Laboratory (LBNL)
  • Lacuna Technologies, Inc. (Lacuna)
  • National Renewable Energy Laboratory (NREL)

Summary:

Curbs are a critical interfacing layer between movement and arrival in urban areas—the layer at which people and goods transition from travel to arrival—representing a primary point of resistance when joining and leaving the transportation network. Traditionally, curb spaces are statically supplied, priced, and zoned for specific usage (e.g., paid parking, commercial/passenger loading, or bus stops). In response to the growing demand for curb space, some cities are starting to be more intentional about defining curb usage. Examples of curb demand include not only traditional parking and delivery needs, but today include things like curb access requirements generated by micro delivery services, active transportation modes, and transportation network companies. And now due to the pandemic, increased demand comes from food/grocery pick-up/drop-off activities, as well as outdoor business use of curb space (e.g., outdoor restaurant seating).

Heightened demand and changing expectations for finite curb resources necessitates the implementation of new and dynamic curb management capabilities so that local decision-makers have the tools needed to improve occupancy and throughput while reducing the types of traffic disruptions that result from parking search and space maneuvering activities.

However, municipalities and cities currently lack tools that allow them to simulate the effectiveness of potential dynamic curb management policies to understand how the available control variables (e.g. price or curb space supply) can be modified to influence curb usage outcomes. On the other hand, transportation authorities and fleet managers lack the needed signage or communication platforms to effectively communicate the availability of curb space for a specified use, price, and time at scales beyond centralized lots and garages.

This project aims to develop a city-scale dynamic curb use simulation tool and an open-source curb management platform. The envisioned simulation and management capabilities will include dynamically and concurrently controlling price, number of spaces, allowed parking duration, time of use or reservation, and curb space use type (e.g., dynamic curb space rezoning based on supply and demand).

Project Objectives:

Project objectives include the following:

  • Objective 1:  The team will develop a microscale curb simulation tool to model behavior of individual vehicles with different purposes at the curb along a blockface over time of day, accounting for price, supply, function, and maximum parking time.
  • Objective 2: The team will integrate the microscale simulation tool with the LBNL’s mesoscale (city-scale) traffic simulation tool, BEAM, for simulating traffic impacts of alternative curb management strategies and their effects on citywide and regional traffic, in terms of (1) travel time, (2) throughput (people and goods) into and out of urban centers, (3) reduced energy use and emissions (from parking search and congestion), and (4) curb space utilization.
  • Objective 3: The team will develop a dynamic curbspace allocation controller for various curb users, either municipal or commercial, for the purpose of a demonstration and pilot.
  • Objective 4: The team will design, implement and test a curbside resource usage platform for fleet vehicles communications at commercial vehicle load zones (CVLZs), passenger load zones (PLZs), and transit stops.
  • Objective 5: The team will perform demonstrations with stakeholder agencies and provide pathways to practice for promising curb allocation policies.
Paper

Analyzing the Shift in Travel Modes’ Market Shares with the Deployment of Autonomous Vehicle Technology

 
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URL: http://hdl.handle.net/1773/45288
Publication Date: 2020
Summary:

It is generally accepted that automation as an emerging technology in transportation sector could have a potential huge effect on changing the way individuals travel. In this study, the impact of automation technology on the market share of current transportation modes has been examined. A stated preference (SP) survey was launched around the U.S. to ask 1500 commuters how they would choose their commute mode if they had the option to choose between their current mode and an autonomous mode. The survey included five transportation modes: car, transit, transit plus ride-sourcing for the first/last mile, solo ride-sourcing, and pooled ride-sourcing. Each of these modes could be presented as regular or autonomous in the choice scenarios. Then, a mixed logit model was developed using the collected data. Results from the analysis of the model showed that applying the automation in ride-sourcing services to decrease the fare, has the largest effect on the market share of transit ride-sourcing. Also, it was found that measures such as deploying more frequent services by ride-sourcing operators to minimize the waiting time of the services could lead to an increase in the market share of transit plus ride-sourcing but it might not improve the market share for solo and pooled ride-sourcing. Furthermore, it was concluded that if the ride-sourcing market share does not move toward the automation, the mode that will lose the market share is the transit plus ride-sourcing mode for which the market share will be decreased as a consequence of the high decrease in the cost of riding an autonomous private car.

Authors: Dr. Andisheh Ranjbari, Moein Khaloei, Don MacKenzie
Recommended Citation:
Khaloei, M., Ranjbari, A. and MacKenzie, D. (2020) Analyzing the Shift in Travel Modes’ Market Shares with the Deployment of Autonomous Vehicle Technology. Transportation