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Freight and Bus Lane (FAB) Data Collection and Evaluation Plan (Route 40)

The Urban Freight Lab (UFL) was approached by the Seattle Department of Transportation (SDOT) to complete a review of proposed evaluation criteria and propose a data collection plan in preparation for the implementation of a Freight and Bus Lane (FAB) Lane in Fall 2024 for King County Metro’s Bus Route 40.

This project would effectively produce the follow-on scope of work for the UFL to complete during the actual implementation (pre/post/post phase). UFL will build on the findings from the Urban Freight Lab’s Freight and Transit Lane Case Study completed in 2019. With the completion of the Route 40 TPMC project in Fall 2024, FAB lanes will be tested as a pilot in select locations and evaluated before permanent installation.

Objectives

  • Refresh literature review on freight and transit lane studies
  • Meet with key stakeholders from SDOT and Metro to understand data collection tools and methodologies
  • Propose a technical evaluation plan for this pilot that includes data collection and metrics and communication strategies
Chapter

Success Factors for Urban Logistics Pilot Studies

Publication: The Routledge Handbook of Urban Logistics
Publication Date: 2023
Summary:

The last mile of delivery is undergoing major changes, experiencing new demand and new challenges. The rise in urban deliveries amid the societal impacts of the COVID-19 pandemic has dramatically affected urban logistics. The level of understanding is increasing as cities and companies pilot strategies that pave the way for efficient urban freight practices. Parcel lockers, for instance, have been shown to reduce delivery dwell times with such success that Denmark increased its pilot program of 2,000 lockers to 10,000 over the past two years. This chapter focuses on challenges faced during those pilots from technical, managerial and operational perspectives, and offers examples and lessons learned for those who are planning to design and/or run future pilot tests. On-site management proved to be critical for locker operations.

Recommended Citation:
Ranjbari, Andisheh & Goodchild, A & Guzy, E. (2023). Success Factors for Urban Logistics Pilot Studies. 10.4324/9781003241478-27.

NYC Zero-Emission Freight and Green Loading Zone Market Research

(This project is being conducted under the Urban Freight Lab’s (UFL) Technical Assistance Program, where UFL contributes to the project by providing 1:1 match funds in terms of staff and/or research assistants to complete project tasks.)

This project is focused on conducting targeted freight industry market research to identify strategies that can support charting a pathway to zero-emission freight strategies for New York City by 2050 and identify the associated roadblocks/barriers to entry.

Partner Organization: New York City Department of Transportation

Project Goals:

  • Understand the interests and concerns of freight industry and private sector stakeholders to enable collaboration and inform the development of NYC DOT’s Green Loading Zone pilot
  • Actively engage NYC freight stakeholders to identify the greatest likelihood of accelerating the uptake and greater efficiency for zero-emission trucks.
  • Improve NYC DOT’s understanding of the obstacles and roadblocks that impact progress towards achieving zero emissions urban freight in NYC.

Summary of Project Tasks:

Task 1: Research Scan

Review national and international best practices on zero-emission urban freight, and identify new and existing strategies that support achieving zero-emission freight in NYC, with a particular focus on loading zone and curb management.

Task 2: Market Research Survey Design

Develop a short survey for stakeholders (freight industry, consumer brands and parcel carriers, etc. serving NYC area) to better understand the potential scale, siting, and contextual implementation of the Green Load Zone (GLZ) pilot. The survey will collect data on:

  • current trends and future estimations about the use of “green” vehicle fleet
  • barriers and opportunities with prevailing market conditions
  • key drivers and constraints for stakeholders
  • needs, motivation and role of each stakeholder involved

Task 3: Freight Industry Market Research and Stakeholder Engagement

Distribute the survey, and summarize and synthesize survey findings. Findings will help NYCDOT:

  • understand the interests and concerns of freight industry and other private stakeholders around the use of GLZs
  • identify potential GLZ users and accelerate the uptake of it
  • make informed decisions about implementation of the GLZ pilot
  • chart a pathway to achieving zero-emission freight in NYC

Task 4: Final Report

Provide a final report to NYCDOT.

Technical Report

Development, Deployment, and Assessment of Activity-Based Transportation Courses

 
Download PDF  (2.60 MB)
Publication: U.S. Federal Highway Administration
Publication Date: 2012
Summary:

This project developed four new activity‐based transportation courses including “Traffic Signal Systems Operations and Design”, “Understanding and Communicating Transportation Data”, “Introduction to Freight Transportation”, and “Rural Highway Design and Safety”. The courses are learner‐centered in which activities completed by students form the basis for their learning. The courses were offered fourteen times to a total of 195 students. Activity books that included 142 activities were developed for the four courses. The books and all supporting materials are available on the project web site. A number of assessments and evaluations were conducted to determine how effective the courses and materials were in meeting project objectives. The active learning style was a challenge for many students, as they were required to be prepared for class and to do “active” work during class. In general, there was an acceptance of the value of the active learning environments and how they positively contributed to student learning.

Authors: Dr. Anne Goodchild, Michael Kyte, Steve Beyerlein, Shane Brown, Chris Monsere, Kelly Pitera, Ming Le
Recommended Citation:
Kyte, Michael, Steve Beyerlein, Shane Brown, Chris Monsere, Anne Goodchild, Kelly Pitera, and Ming Lee. "Development, Deployment, and Assessment of Activity-Based Transportation Courses." (2012).

Optimization of Supply and Transportation Networks in an Epidemic Situation in Collaboration with the Seattle Flu Study

The mission of the Seattle Flu Study (SFS) is to prototype city-scale capabilities for epidemic preparedness and response. One of the aims of this study is to understand methods to implement rapid interventions outside of clinical settings and within 48-72 hours of the onset of symptoms, to enable the immediate diagnosis, treatment, or isolation of flu-positive individuals.

SFS has reached out to the Supply Chain Transportation and Logistics Center at the University of Washington to test various to develop models and perform sensitivity analyses on epidemic response scenarios via simulation and mathematical optimization. Modeling will allow SFS to measure and understand questions like, “when will our supply chain break?”, “how do you prevent it from breaking?” and “how do you get drugs and tests to people if your driver workforce gets sick?”. By modeling these types of scenarios, they will be able to assess the pros and cons of various supply chain strategies and develop multiple levers that can be pulled depending on the epidemic situation including prepositioning of orders, and leveraging in-house and supplementary private transportation alternatives (FedEx, etc.).

Report

Curbing Conflicts: Curb Allocation Change Project Report

 
Download PDF  (4.05 MB)
Publication Date: 2019
Summary:

Like many congested cities, Seattle is grappling with how best to manage the increasing use of ride-hailing services by Transportation Network Companies (TNCs) like Uber and Lyft. According to a 2018 Seattle Times analysis, TNC ridership in the Seattle region has grown to more than five times the level it was in the beginning of 2015, providing, on average, more than 91,000 rides a day in 2018. And the newspaper reports Uber and Lyft trips are heavily concentrated in the city’s densest neighborhoods, where nearly 40,000 rides a day start in ZIP codes covering downtown, Belltown, Capitol Hill and South Lake Union.

This University of Washington (UW) study focuses on a strategy to manage TNC driver stops when picking up and dropping off passengers to improve traffic flow in the South Lake Union (SLU) area. SLU is the site of the main campus for Amazon, the online retail company. The site is known to generate a large number of TNC trips, and Amazon reports high rates of ride-hailing use for employee commutes. This study also found that vehicle picking-up/dropping-off passengers make up a significant share of total vehicle activity in SLU. The center city neighborhood is characterized by multiple construction sites, slow speed limits (25 mph), and heavy vehicle and pedestrian traffic.

Broad concerns about congestion, safety, and effective curb use led to this study, conducted by researchers at the UW’s Urban Freight Lab and Sustainable Transportation Lab. Amazon specifically was concerned about scarcity of curb space where TNC drivers could legally and readily stop to pick up and drop off passengers. Without dedicated load/unload curb space, TNC vehicles stop and wait at paid parking spots, other unauthorized curb spots, or in the travel lane itself, potentially blocking or slowing traffic. To try to mitigate the impacts of passenger pick-up/drop-off activity on traffic, the city proposed a strategy of increasing passenger loading zone (PLZ) spaces while Uber and Lyft implemented a geofence, which directs their drivers and passengers to designated pick-up and drop-off locations on a block. (Normally, drivers pick up or drop off passengers at any address a rider requests via the ride-hailing app.)

By providing ample designated pick-up and drop-off spots along the curb, the thinking goes, TNC drivers would reduce the frequency with which they stop in the travel lane to pick up or drop off passengers and the time they stay stopped there. By these measures, this study’s findings show the approach was successful. But it is important to note that the strategy is not a silver bullet for solving traffic congestion—nor is it designed as such. It is also important to note that any initiative to manage use of curbs and roads (by TNCs or others) is part of a city’s broader transportation policy framework and goals.

For this study, researchers analyzed an array of data on street and curb activity along three block-faces on Boren Ave N in December 2018 and January 2019. At a minimum, data were collected during the morning and afternoon peak travel times (with some collected 24 hours a day). The research team collected data using video and sensor technology as well as in-person observation. Researchers also surveyed TNC passengers for demographic, trip-related and satisfaction data. The five Amazon buildings in the area studied house roughly 8,650 employees. Researchers collected data in three stages. Phase 1, the study baseline, was before PLZs were added and geofencing started. Phase 2 was after the new PLZs were added, expanding total PLZ curb length from 20 feet (easily filled by one to two vehicles) to 274 feet. Phase 3 was after geofencing was added to the expanded PLZs. The added PLZ spaces were open to any passenger vehicle—not just TNC vehicles—weekdays from 7am to 10am and 2pm to 7pm. (Permitted food trucks were authorized from 10am to 2pm.)

Note that while other cities can learn from this analysis, the findings apply to streets with comparable traffic speed, mix of roadway users, and street design.

The study’s main findings include:

  • A significant percentage of vehicles performing a pick-up/drop-off stop in the travel lane. Those in-lane stops appear connected to the lack of available designated curb space: Adding PLZs and geofencing increased driver compliance in stopping at the curb versus stopping in the travel lane to load and unload passengers. But it was not lack of curb space alone that influenced driver activity: Between 7 percent and 10 percent of drivers still stopped in the travel lane even when PLZs were empty. After adding PLZs and geofencing, in-lane stops fell from 20 percent to 14 percent for pick-ups and from 16 percent to 15 percent for drop-offs.
  • Adding PLZs and geofencing reduced the average amount of time drivers stopped to load and unload passengers. For example, 90 percent of drop-offs took less than 1 minute 12 seconds, 42 seconds faster than the average with the added PLZs alone.
  • While curb occupancy increased after adding PLZs and geofencing, occupancy results show the current allocation of PLZ spaces is more than what is needed to meet observed demand: Average PLZ occupancy remained under 20 percent after PLZ expansion, even during peak commute hours.
  • Vehicles picking-up/dropping-off passengers account for a significant share of total traffic volume in the study area: during peak hours the observed average percentage of vehicles performing a pick-up/drop-off with respect to the total traffic volume was 29 percent (in Phase 1), 32 percent (in Phase 2) and 39 percent (in Phase 3).
  • High volumes of pedestrians (400-500 per hour on average) cross the street at points where there was no crosswalk. Passengers picked-up/dropped-off constituted a fraction (five to seven percent) of those pedestrians, but high rates of passengers (30 to 40 percent) cross the street at non-crosswalk locations.
  • Adding PLZs and geofencing did not have a significant impact on traffic safety. Researchers found no significant change in the number of observed conflicts from baseline to the addition of PLZs and geofencing. Conflicts are situations where a vehicle, bike, or pedestrian is interrupted, forced to alter their path, or engaged in a near-miss situation. Conflicts include vehicles passing in the oncoming traffic lane. • Adding PLZs and geofencing also did not produce a significant impact on roadway travel speed.
  • Of the 116 TNC passengers surveyed in the study area:
    • Roughly 40 percent to 50 percent said their trip was work related. More than half said they used ride-hailing service at least once a week and 70 percent or more used TNC alone (versus in combination with other transportation options) to get from their origin to their destination.
    • Most responded positively to the added PLZs and geofence: 79 percent rated their pick-up satisfactory and 100 percent rated their drop-off satisfactory as compared to 72 percent and 89 percent in the baseline.
    • Nearly half said they would have taken transit and one-third would have walked if ride-hailing was not available.
    • 40 percent requested a shared TNC vehicle in Phase 1 and 47 percent in Phase 3.

The study suggests that while vehicles picking-up/dropping-off passengers account for a significant share of traffic volume in SLU, they are not the primary cause of congestion. Myriad factors impact neighborhood congestion, including high vehicle volume overall and bottlenecks moving out of the neighborhood onto regional arterials. As researchers observed in the afternoon peak, these bottlenecks cause spillbacks onto local streets. Amazon garages exit vehicles onto streets that then feed into these clogged arterials.

Regarding traffic safety in SLU, this study was not designed to assess whether TNC driver behavior on average is safer or less safe than that of other vehicles. It is important to understand the safety and speed findings in the context of the SLU traffic environment. Drivers tend to drive at relatively slow speeds, navigating around high pedestrian and jaywalking volumes, and seem relatively comfortable stopping in the middle of the street for short periods of time. Due to the nature of area traffic, this seems to have relatively little impact on other drivers. Drivers appear to anticipate both this behavior and the high volumes of vehicles moving onto/off the curb and into/out of driveways and alleys.

Whether the strategy this study analyzed is recommended depends on a city’s transportation goals and approach. The researchers found the increased PLZ allocation and geofencing strategy worked in that it improved driver compliance, reduced dwell times, and boosted TNC user satisfaction. However, this may encourage commuters to use TNC. The passenger survey clearly shows that TNC service is attracting passengers who would have otherwise walked or used transit. While in the short term the increased PLZs and geofencing had a positive effect on traffic, if this induces TNC demand, there could be larger, more negative long-term consequences. If the end goal is to reduce traffic congestion, measures to reduce—rather than encourage—TNC and passenger car use as the predominant mode of commuting will yield the most substantial benefits.


In the news:

Geekwire: As Uber and Lyft pick-ups and drop-offs clog traffic, new study calls load zones a move in right direction

The Seattle Times: Seattle Uber and Lyft drivers often stop in the street to pick up or drop off riders. Here’s a way to reduce that.

Recommended Citation:
Goodchild, Anne. Giacomo dalla Chiara. Jose Luis Machado. Andisheh Ranjbari. (2019) Curb Allocation Change Project.

Food Rescue Collaborative Research

The Supply Chain Transportation & Logistics Center (SCTL) is conducting collaborative research with Seattle Public Utilities (SPU) to explore and share innovative approaches for moving, storing, and redistributing surplus food.

Food rescue is the process of gleaning edible food that would otherwise go into the waste stream from local businesses and re-distributing it to local food programs. SPU is building partnerships to increase this practice while striving to increase the City’s greater affordability and resilience. Transportation, storage, and logistics have been key operational barriers to increasing rescued food.

SPU commissioned SCTL to create a shared data-driven understanding of the logistics of food rescue in Seattle.

The purpose of this project is to reduce waste and increase access and food quality for customers of food banks and meal programs. Research will be conducted with both businesses that donate food and the organizations that receive it. The first phase of this project will focus on identifying ways to lower transportation, cold storage, and logistics costs of securing food and evaluating these benefits.

Ultimately, research findings could lead to creating solutions that:

  • increase supply chain transportation efficiency,
  • reduce current system redundancy across food rescue operations
  • increase collaboration across independent operations serving Seattle’s food insecure residents, and
  • increase the resiliency of food rescue operations which could ultimately enhance their ability to respond to larger City disasters, disruptions and unexpected risks.
Paper

A Competitive, Charter Air-Service Planning Model for Student Athlete Travel

Publication: Transportation Research Part B: Methodological
Volume: 45 (1)
Pages: 128-149
Publication Date: 2011
Summary:

This paper presents a model for planning an air charter service for pre-scheduled group travel. This model is used to investigate the competitiveness of such an enterprise for student athlete travel in conference sports. The relevant demand subset to be served by a limited charter fleet is identified through a comparison with existing scheduled travel options. Further, the routing and scheduling of the charter aircraft is performed within the same framework. Through this modeling a method for formulating and accommodating continuous time windows and competitive market dynamics in strategic planning for a charter service is developed. Computational improvements to the basic model are also presented and tested. The model is applied to the Big Sky Conference for the 2006–2007 season, quantifying the benefits to the students from such a service and the change in expenditure associated with such a benefit for various assumptions about operations and value of time. The findings indicate the lack of spatial or sport based patterns for maximizing benefit, indicating the absence of simplistic “rules of thumb” for operating such a service, and validating the need for the model.

Authors: Dr. Anne Goodchild, Gautam Gupta, and Mark Hansen
Recommended Citation:
Gautam Gupta, Anne Goodchild, and Mark Hansen (2011). A Competitive, Charter Air-Service Planning Model for Student Athlete Travel. Transportation Research Part B, 45, 128-149.

The Route Machine: An Optimization Framework (Phase 1)

The University of Washington Department of Laboratory Medicine runs 12 routes per day moving lab specimens and conducting departmental business. These routes have been developed over time in an ad hoc fashion.

The Urban Freight Lab will primarily focus on the following objectives for optimization:

  1. Minimize expected lead time (from the time the specimens are ready for pick up to the time they are delivered to the lab for testing)
  2. Minimize the extent to which couriers work outside of their maximum shift durations

The decisions the ‘route machine’ optimization framework ideally should inform:

  1. Day-to-day (operational) decision-making: Given all of the current capacities (i.e., number of vehicles) can routes be improved through changing order of routes or destinations serviced in route?
  2. Tactical decision-making: What modifications to the current capacities (i.e., increasing the number of vehicles) will produce the greatest benefit? How will the optimal routes change if there are modifications to customer requirements?
  3. Strategic decision-making: If UW Medicine Department of Laboratory Medicine expands its operations how will routes and capacities need to change to accommodate the new situation? What should the workforce balance between full-time workers and contractors look like?

This analysis includes:

  • Phase 1: Evaluate the existing routes on a qualitative basis to judge whether there is sufficient opportunity for improvement, and strategies that show greatest opportunity for improvement
  • Phase 1: Conduct an inventory of off the shelf tools and determine their suitability for the application
  • Phase 2: Build the Route Machine tool, either using off-the shelf software tools, building the tool from scratch, or some combination of the two.

Dynamically Managed Curb Space Pilot

Transportation Network Company (TNC) usage in Seattle has been increasing every quarter since 2015 when the City of Seattle Department of Transportation (SDOT) began collecting data. TNC trips exceeded 20 million in 2017, a 46% increase from total reported trips in 2016. This has led to concerns about congestion and pedestrian safety as cars and people take risks to connect at the curb and in the right-of-way. By providing additional curb capacity through increased passenger loading zones and directing customers via in-app messaging, the City may be able to reduce congestion and unsafe vehicle/people movements during peak traffic and late-night hours.

Other cities have attempted to study the impacts of increased usage of passenger loading zones (e.g., San Francisco, Washington D.C.), with varying success, but no standard methodology exists for cities to assess the potential for reallocated curb space and the subsequent impacts of those changes. SDOT is taking a data-driven approach to curb reallocation and traffic network impacts, modeling the work SDOT has done to quantify demand in paid parking areas and set rates accordingly. The main goals of this pilot are three-fold: increase pedestrian safety, minimize congestion impacts on the larger transportation network, and build a scalable methodology for assessment and implementation of curb allocation to accommodate this new mobility service.

The Supply Chain Transportation & Logistics Center and SDOT will work in collaboration with employers, transit operators, and TNCs to test a variety of strategies to mitigate the traffic impacts of TNC pick-ups on the greater transportation network and improve safety for passengers and drivers. Strategies include increasing the number of passenger loading zones in high-traffic pick-up areas and geofenced pick-up or black-out areas. Curb and street use data will be collected under each alternative and compared to baseline data.