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Analyzing the Effect of Autonomous Ridehailing on Transit Ridership: Competitor or Desirable First-/Last-Mile Connection?

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Publication: Transportation Research Record
Volume: 2675 (11)
Pages: 1154-1167
Publication Date: 2021

Ridehailing services (e.g., Uber or Lyft) may serve as a substitute or a complement—or some combination thereof—to transit. Automation as an emerging technology is expected to further complicate the current complex relationship between transit and ridehailing. This paper aims to explore how US commuters’ stated willingness to ride transit is influenced by the price of ridehailing services and whether the service is provided by an autonomous vehicle. To that end, a stated preference survey was launched around the US to ask 1,500 commuters how they would choose their commute mode from among choices including their current mode and other conventional modes as well as asking them to choose between their current mode and an autonomous mode. Using a joint stated and revealed preference dataset, a mixed logit model was developed and analyzed.

Authors: Dr. Andisheh Ranjbari, Moein Khaloei, Ken Laberteaux, Don MacKenzie
Recommended Citation:
Khaloei, M., Ranjbari, A., Laberteaux, K., & MacKenzie, D. (2021). Analyzing the Effect of Autonomous Ridehailing on Transit Ridership: Competitor or Desirable First-/Last-Mile Connection? Transportation Research Record, 2675(11), 1154–1167.
Student Thesis and Dissertations

Ridehail and Commercial Vehicles Access in Urban Areas: Implications for Public Infrastructure Management

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Publication Date: 2022

As urbanized populations and concentrations of activities increase, there is growing pressure in dense and constrained urban areas to unlock the potential of every public infrastructure element to address the increasing demand for public space. Specifically, there is a growing demand for space for parking operations related to the access to land use by people and goods. On one side, ridehailing services, such as those provided by Uber and Lyft, are on the rise and with them the associated passenger pick-up/drop-off (PUDOs) operations. On the other side, freight and servicing trips require a supply of adequate infrastructure to support vehicle access and load/unload activities and final delivery/service to customers. This dissertation aims to provide insights based on real-world datasets and tests to support the management of two key public infrastructure that provides access to land uses: alleys and curb lanes. To achieve this goal, first, this dissertation will investigate what roles alleys play in cities and inspect alleys’ physical characteristics and vehicle parking operations in these spaces. Secondly, this research will examine factors of PUDO dwell time and evaluate the impact of adding curb lane PUDO zones and geofencing ridehailing vehicles to these zones using a hazard-based duration modeling approach. Finally, this dissertation will analyze the impact of different ridehailing curb management strategies on curb lane utilization based on simulation.

Recommended Citation:
León, J., Luis Machado. (2022). Ridehail and Commercial Vehicles Access in Urban Areas: Implications for Public Infrastructure Management (Order No. 10827973). University of Washington Doctoral Dissertation.
Technical Report

Transit Corridor Study

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Publication Date: 2021

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

Challenges in Credibly Estimating the Travel Demand Effects of Mobility Services

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Publication: Transport Policy
Volume: 103
Pages: 224-235
Publication Date: 2021

Mobility services including carsharing and transportation network company (TNC) services have been growing rapidly in North America and around the world. Measuring the effects of these services on traveler behavior is challenging because the results of any such analysis are sensitive to how (1) outcomes are measured and (2) counterfactuals are constructed. The lack of good control groups or randomization of assignment leaves lingering uncertainty over the contributions of selection bias and treatment effects to reported differences in travel behavior between users and non-users of these services. This paper reports on two approaches for measuring the effects of mobility service adoption on travel rate and car ownership. We first tried a pretest-posttest randomized encouragement experiment to deal with the shortcomings of poor control groups. Then, we turned to the approach of self-reported effects based on hypothetical controls to investigate whether variations in survey question presentation could influence respondents’ answers and thus lead to changes in estimated effects. The data to conduct this study came from two sources: a panel survey administered by the authors at the University of Washington (UW), and a survey by Populus Technologies, Inc. (Populus). Various statistical tests were applied to analyze the data, and the results highlight the pivotal role that the research design plays in influencing the outcomes, and manifest the fundamental challenge of establishing credible estimates of the causal effects of adopting mobility services on travel behaviors.

Authors: Dr. Andisheh Ranjbari, Xiao Wen, Fan Qi, Regina R. Clewlow, Don MacKenzie
Recommended Citation:
Xiao Wen, Andisheh Ranjbari, Fan Qi, Regina R. Clewlow, Don MacKenzie. Challenges in credibly estimating the travel demand effects of mobility services. Transport Policy, (103:224-235) 2021.

An Empirical Analysis of Passenger Vehicle Dwell Time and Curb Management Strategies for Ride-Hailing Pick-Up/Drop-Off Operations

Publication: Transportation
Publication Date: 2023

With the dramatic and recent growth in demand for curbside pick-up and drop-off by ride-hailing services, as well as online shopping and associated deliveries, balancing the needs of roadway users is increasingly critical. Local governments lack tools to evaluate the impacts of curb management strategies that prioritize different users’ needs. The dwell time of passenger vehicles picking up/dropping off (PUDO) passengers, including ride-hailing vehicles, taxis, and other cars, is a vital metric for curb management, but little is understood about the key factors that affect it. This research used a hazard-based duration modeling approach to describe the PUDO dwell times of over 6,000 passenger vehicles conducted in Seattle, Wash. Additionally, a before-after study approach was used to assess the effects of two curb management strategies: adding PUDO zones and geofencing. Results showed that the number of passenger maneuvers, location and time of day, and traffic and operation management factors significantly affected PUDO dwell times. PUDO operations took longer with more passengers, pick-ups (as opposed to drop-offs), vehicle´s trunk access, curbside stops, and in the afternoon. More vehicles at the curb and in adjacent travel lanes were found to be related to shorter PUDO dwell times but with a less practical significance. Ride-hailing vehicles tended to spend less time conducting PUDOs than other passenger vehicles and taxis. Adding PUDO zones, together with geofencing, was found to be related to faster PUDO operations at the curb. Suggestions are made for the future design of curb management strategies to accommodate ride-hailing operations.

Authors: José Luis Machado LeónDr. Anne Goodchild, Don MacKenzie (University of Washington College of Engineering)
Recommended Citation:
Machado-León, J.L., MacKenzie, D. & Goodchild, A. An Empirical Analysis of Passenger Vehicle Dwell Time and Curb Management Strategies for Ride-Hailing Pick-Up/Drop-Off Operations. Transportation (2023).

Growth of Ecommerce and Ride-Hailing Services is Reshaping Cities: The Urban Freight Lab’s Innovative Solutions

Publication: California Transportation Commission (August 15, 2018)
Publication Date: 2018

A 20% e-commerce compound annual growth rate (CAGR) would more than double goods deliveries in 5 years. If nothing changes, this could double delivery trips in cities; thereby doubling the demand for load/unload spaces.

Innovation is needed to manage scarce curbs, alleys, and private loading bay space in the new world of on-demand transportation, 1-hour e-commerce deliveries, and coming autonomous vehicle technologies.

The Urban Freight Lab at the University of Washington (UW), in partnership with the City of Seattle Department of Transportation (SDOT), is using a systems engineering approach to solve delivery problems that overlap cities’ and businesses’ spheres of control.

The Urban Freight Lab is a living laboratory where potential solutions are generated, evaluated, and pilot-tested inside urban towers and on city streets.

Recommended Citation:
Goodchild, Anne. Growth of Ecommerce and Ride-Hailing Services is Reshaping Cities: The Urban Freight Lab’s Innovative Solutions. California Transportation Commission (August 15, 2018)

Growth of Ecommerce and Ride-Hailing Services is Reshaping Cities Innovative Goods Delivery Solutions for Cities of the Future

Publication: Eno Transportation (August 9, 2018 Webinar)
Publication Date: 2018
Authors: Barbara Ivanov

Growth of Ecommerce and Ride-Hailing Services is Reshaping Cities Connecting State and City DOTs, and Transit Agencies for Innovative Solutions

Publication: AASHTO 2018 Joint Policy Conference: Connecting the DOTs
Volume: 19-Jul-18
Publication Date: 2018

There is not enough curb capacity, now.

A recent curb parking utilization study in the City of Seattle indicated 90% or higher occupancy rates in Commercial Vehicle Load Zones (CVLZs) for some areas for much of the workday.

The Final Fifty Feet is a new research field.

The Final 50 Feet project is the first time that researchers have analyzed both the street network and cities’ vertical space as one unified goods delivery system. It focuses on:

  • The use of scarce curb, buildings’ internal loading bays, and alley space
  • How delivery people move with handcarts through intersections and sidewalks; and
  • On the delivery processes inside urban towers.
Authors: Barbara Ivanov