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Chapter

Are Cities’ Delivery Spaces in the Right Places? Mapping Truck Load/Unload Locations

 
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Publication: City Logistics 2: Modeling and Planning Initiatives (Proceedings of the 2017 International Conference on City Logistics)
Volume: 2
Pages: 351-368
Publication Date: 2018
Summary:

Two converging trends – the rise of e‐commerce and urban population growth – challenge cities facing competing uses for road, curb and alley space. The University of Washington has formed a living Urban Freight Lab to solve city logistics problems that cross private and public sector boundaries. To assess the capacity of the city’s truck load/unload spaces, the lab collected GIS coordinates for private truck loading bays, and combined them with public GIS layers to create a comprehensive map of the city’s truck parking infrastructure. The chapter offers a practical approach to identify useful existent urban GIS data for little or no cost; collect original granular urban truck data for private freight bays and loading docks; and overlay the existing GIS layers and a new layer to study city‐wide truck parking capacity. The Urban Freight Lab’s first research project is addressing the “Final 50 Feet” of the urban delivery system.

Recommended Citation:
Goodchild, Anne, Barb Ivanov, Ed McCormack, Anne Moudon, Jason Scully, José Machado Leon, and Gabriela Giron Valderrama. Are Cities' Delivery Spaces in the Right Places? Mapping Truck Load/Unload Locations: Modeling and Planning Initiatives. City Logistics 2: Modeling and Planning Initiatives (2018): 351-368. 10.1002/9781119425526.ch21
Special Issue

The Curb Lane

Publication: Transportation Research Part A: Policy and Practice
Publication Date: 2021
Summary:

Efforts to regulate the curb also suffer from a lack of publicly accessible data on both the demand and supply of curb space. Cities often do not have the technical expertise to develop a curb data collection and data-sharing strategy. In addition, the private individuals and companies that generate most of the curb-use data often withhold them from public use to protect proprietary information and personal user data.

However, new uses of data sources, such as the Global Positioning System (GPS) and cellular networks, as well as the implementation of wide networks of IoT devices, are enabling the “digitization” of the curb, allowing cities to gain a better understanding of curb use as well as ways to change their approach toward curb space management.

In a way, the revolution in curb space management has already started. Many cities are re-inventing their role from passively regulating on-street parking to dynamically allocating and managing the curb, both physically and digitally, to serve many different users. Geofencing and time-dependent allocation of curb space facilitate efficient passenger pickup and drop off. Parking information systems and pay-for-parking apps enable dynamic parking allocation and pricing. We believe this is the right time for scientific research to “catch up” with current changes and to develop new analytical tools for curb space management. Such efforts are the focus of this special issue on curb lane analysis and policy.

Authors: Dr. Anne GoodchildDr. Giacomo Dalla ChiaraDr. Andisheh Ranjbari, Susan Shaheen (University of California, Berkeley), Donald Shoup (UCLA)
Recommended Citation:
Special Issue: The Curb Lane. Transportation Research Part A: Policy and Practice | ScienceDirect.com by Elsevier.
Presentation

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
Summary:

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)
Paper

Providing Curb Availability Information to Delivery Drivers Reduces Cruising for Parking

 
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Publication: Scientific Reports
Volume: (2022) 12:19355
Publication Date: 2022
Summary:

Delivery vehicle drivers are experiencing increasing challenges in finding available curb space to park in urban areas, which increases instances of cruising for parking and parking in unauthorized spaces. Policies traditionally used to reduce cruising for parking for passenger vehicles, such as parking fees and congestion pricing, are not effective at changing delivery drivers’ travel and parking behaviors.

Intelligent parking systems that use real-time curb availability information to better route and park vehicles can reduce cruising for parking, but they have never been tested for delivery vehicle drivers.

This study tested whether providing real-time curb availability information to delivery drivers reduces the travel time and distance spent cruising for parking. A curb parking information system deployed in a study area in Seattle, Wash., displayed real-time curb availabilities on a mobile app called OpenPark. A controlled experiment assigned drivers’ deliveries in the study area with and without access to OpenPark.

The data collected showed that when curb availability information was provided to drivers, their cruising for parking time significantly decreased by 27.9 percent, and their cruising distance decreased by 12.4 percent. These results demonstrate the potential for implementing intelligent parking systems to improve the efficiency of urban logistics systems.

Recommended Citation:
Dalla Chiara, G., Krutein, K.F., Ranjbari, A. et al. Providing curb availability information to delivery drivers reduces cruising for parking. Sci Rep 12, 19355 (2022). https://doi.org/10.1038/s41598-022-23987-z
Article

Giving Curb Visibility to Delivery Drivers

 
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Publication: American Planning Association | 2022 State of Transportation Planning
Pages: 134-143
Publication Date: 2022
Summary:
At the time we are writing this article, hundreds of thousands of delivery vehicles are getting ready to hit the road and travel across U.S. cities to meet the highest peak of demand for ecommerce deliveries during Thanksgiving, Black Friday, and the Christmas holiday season. This mammoth fleet will not only add vehicle miles traveled through urban centers but also increase parking congestion, battling with other vehicles for available curb space.
While the integration of road traffic data with modern navigation systems has seen huge developments in the past decade, drastically changing the way we, and delivery vehicles, navigate through cities, not as much can be said when it comes to parking. The task of finding and securing parking is still left to drivers, and largely unsupported by real-time information or app-based solutions.
Delivery vehicle drivers are affected by curb parking congestion even more than any other driver because delivery drivers have to re-park their vehicles not once or twice, but 10, 20, or even more times during a delivery route.
Our work, discussed in this article, focuses on improving delivery drivers’ lives when it comes to finding available curb space, improving the delivery system, and reducing some of the externalities generated in the process. We first describe what types of parking behaviors delivery drivers adopt when facing a lack of available curb parking, then we will quantify the cost of lack of available parking, estimating how much time delivery drivers spend circling the block and searching for parking. We then discuss how we can improve on that by creating the first curb availability information system – OpenPark.

 

Recommended Citation:
Dalla Chiara, Giacomo and Anne Goodchild. Giving Curb Visibility to Delivery Drivers. Intersections + Identities: State of Transportation Planning 2022, 134-143.
Paper

Modeling the Competing Demands of Carriers, Building Managers, and Urban Planners to Identify Balanced Solutions for Allocating Building and Parking Resources

 
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Publication: Transportation Research Interdisciplinary Perspectives
Volume: 15
Publication Date: 2022
Summary:

While the number of deliveries has been increasing rapidly, infrastructure such as parking and building configurations has changed less quickly, given limited space and funds. This may lead to an imbalance between supply and demand, preventing the current resources from meeting the future needs of urban freight activities.

This study aimed to discover the future delivery rates that would overflow the current delivery systems and find the optimal number of resources. To achieve this objective, we introduced a multi-objective, simulation-based optimization model to define the complex freight delivery cost relationships among delivery workers, building managers, and city planners, based on the real-world observations of the final 50 feet of urban freight activities at an office building in downtown Seattle, Washington, U.S.A.

Our discrete-event simulation model with increasing delivery arrival rates showed an inverse relationship in costs between delivery workers and building managers, while the cost of city planners decreased up to ten deliveries/h and then increased until 18 deliveries/h, at which point costs increased for all three parties and overflew the current building and parking resources. The optimal numbers of resources that would minimize the costs for all three parties were then explored by a non-dominated sorting genetic algorithm (NSGA-2) and a multi-objective, evolutionary algorithm based on decomposition (MOEA/D).

Our study sheds new light on a data-driven approach for determining the best combination of resources that would help the three entities work as a team to better prepare for the future demand for urban goods deliveries.

Authors: Haena KimDr. Anne Goodchild, Linda Boyle
Recommended Citation:
Kim, H., Goodchild, A., & Boyle, L. N. (2022). Modeling The Competing Demands Of Carriers, Building Managers, And Urban Planners To Identify Balanced Solutions For Allocating Building And Parking Resources. In Transportation Research Interdisciplinary Perspectives (Vol. 15, p. 100656). Elsevier BV. https://doi.org/10.1016/j.trip.2022.100656
Presentation

Investigation of Private Loading Bay Operations in Seattle’s Central Business District

 
Publication: 9th International Urban Freight Conference, Long Beach, May 2022
Publication Date: 2022
Summary:

Cities need new load/unload space concepts to efficiently move freight, particularly as autonomous vehicles (both passenger and freight) become feasible. This research aims to: understand the importance of off-street commercial parking, understand how off-street facilities are managed, and determine whether off-street commercial parking is an underutilized resource for urban goods delivery.

Researchers determined the locations of commercial and residential buildings in Seattle’s Central Business District with off-street delivery infrastructure, established communication with property management or building operators, and conducted interviews regarding facility management, usage, roadblocks in design/operations, and utilization.

This research finds that overbooking of off-street space is infrequent, most facility management is done by simple tenant booking systems, buildings relying primarily on curb space notes that infrastructure and operations were hindered by municipal services — especially when connecting to alleyways.

Recommended Citation:
Griffin Donnelly and Anne Goodchild. Investigation of Private Loading Bay Operations in Seattle's Central Business District. 9th International Urban Freight Conference (INUF), Long Beach, CA May 2022.
Presentation

Can Real-Time Curb Availability Information Improve Urban Delivery Efficiency?

 
Publication: 9th International Urban Freight Conference, Long Beach, May 2022
Publication Date: 2022
Summary:

Parking cruising is a well-known phenomenon in passenger transportation, and a significant source of congestion and pollution in urban areas. While urban commercial vehicles are known to travel longer distances and to stop more frequently than passenger vehicles, little is known about their parking cruising behavior, nor how parking infrastructure affects such behavior.

In this study, we propose a simple method to quantitatively explore the parking cruising behavior of commercial vehicle drivers in urban areas using widely available GPS data, and how urban transport infrastructure impacts parking cruising times.

We apply the method to a sample of 2900 trips performed by a fleet of commercial vehicles, delivering and picking up parcels in downtown Seattle. We obtain an average estimated parking cruising time of 2.3 minutes per trip, contributing on average for 28 percent of total trip time. We also found that cruising for parking decreased as more curb-space was allocated to commercial vehicles load zones and paid parking and as more off-street parking areas were available at trip destinations, whereas it increased as more curb space was allocated to bus zone.

Recommended Citation:
Giacomo Dalla Chiara, Klaas Fiete Krutein, and Anne Goodchild (2022). Can Real-Time Curb Availability Information Improve Urban Delivery Efficiency? 9th International Urban Freight Conference (INUF), Long Beach, CA May 2022.
Technical Report

Managing Increasing Demand for Curb Space in the City of the Future

 
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Publication Date: 2022
Summary:

The rapid rise of on-demand transportation and e-commerce goods deliveries, as well as increased cycling rates and transit use, are increasing demand for curb space. This demand has resulted in competition among modes, failed goods deliveries, roadway and curbside congestion, and illegal parking. This research increases our understanding of existing curb usage and provides new solutions to officials, planners, and engineers responsible for managing this scarce resource in the future. The research team worked with local agencies to ensure the study’s relevance to their needs and that the results will be broadly applicable for other cities. This research supports the development of innovative curb space designs and ensures that our urban streets may operate more efficiently, safely, and reliably for both goods and people.

The research elements included conducting a thorough scan and documenting previous studies that have examined curb space management, identifying emerging urban policies developed in response to growth, reviewing existing curb management policies and regulations, developing a conceptual curb use policy framework, reviewing existing and emerging technologies that will support flexible curb space management, evaluating curb use policy frameworks by collecting curb utilization data and establishing performance metrics, and simulating curb performance under different policy frameworks.

Recommended Citation:
Chang, K., Goodchild, A., Ranjbari, A., and McCormack, E. (2022). Managing Increasing Demand for Curb Space in the City of the Future. PacTrans Final Project Report.
Technical Report

Transit Corridor Study

 
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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