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Urban Freight Lab

Research Keyword: Transportation forecasting

Transportation forecasting is the estimate of the number of vehicles or people that will use a specific transportation facility in the future, for example, a forecast may estimate the number of vehicles on a planned road or bridge, the ridership on a railway line, the number of passengers visiting an airport, or the number of ships calling on a seaport.

Estimating and Forecasting E-commerce Last-Mile Delivery VMT in Urban Areas: A Novel Data-Driven Approach

This collaborative research project brings together the freight modeling expertise of Portland State University’s Dr. Miguel Figliozzi and the Urban Freight Lab’s experience in urban logistics research to develop new methods for estimating e-commerce-related vehicle miles traveled (VMT) in the Seattle and Portland metropolitan regions. As online shopping continues to grow, there is increasing interest in understanding how e-commerce affects freight activity, congestion, emissions, and public health.

Project Summary

The project will combine several unique data sources that have not previously been integrated for this purpose, including household e-commerce demand data, inventories of logistics facilities and delivery touchpoints, and real-world delivery routing data. Researchers will develop and test new approaches to modeling e-commerce demand, distribution networks, and delivery routes to better estimate current and future e-commerce-related VMT.

The resulting methods, datasets, and analytical tools will help transportation planners and policymakers better understand the impacts of e-commerce growth and support future freight planning and infrastructure decisions. The collaboration leverages Dr. Figliozzi’s expertise in freight transportation planning, logistics modeling, routing, and e-commerce research, together with the Urban Freight Lab’s industry partnerships and experience in urban freight, warehousing, and last-mile delivery research.

Tasks and Deliverables

Task 1: Define and Classify Logistics Touchpoints (Lead: UW)

Conduct a literature review and stakeholder interviews to identify and classify logistics touchpoints involved in e-commerce distribution, including fulfillment centers, delivery stations, parcel lockers, microhubs, curb space, and delivery destinations.

Deliverable:

  • Technical report defining logistics touchpoints, their characteristics, and examples in the Seattle and Portland regions.

Task 2: Develop Logistics Touchpoint Dataset (Lead: UW)

Compile, validate, and map a comprehensive inventory of logistics facilities and delivery-related infrastructure using commercial, public, and field-collected data sources.

Deliverable:

  • Geospatial dataset of logistics touchpoints and facility characteristics for the Seattle and Portland metropolitan areas.

Task 3: Model Household E-Commerce Demand (Lead: UW)

Develop models of household online shopping behavior using national and regional travel survey data and generate synthetic population datasets to estimate e-commerce demand across both metropolitan areas.

Deliverables:

  • Methodology report describing demand modeling approach.
  • Geolocated demand datasets for use in routing and distribution analysis.
  • Comparison of national-data-only approaches versus enhanced local-data approaches.

Task 4: Review E-Commerce Routing and Distribution Methods (Lead: PSU)

Conduct a comprehensive review of existing freight routing, distribution, optimization, and urban freight modeling approaches relevant to e-commerce deliveries.

Deliverable:

  • Technical report evaluating available routing and distribution methodologies and recommending approaches for this project.

Task 5: Develop Routing and Distribution Models (Lead: PSU)

Create and test new routing and distribution algorithms using using real-world delivery route data from major carriers and advanced optimization and machine-learning techniques. The models will be designed to better reflect real-world e-commerce delivery operations.

Deliverables:

  • Technical report documenting routing formulations and algorithms.
  • Validation and performance evaluation using real-world delivery data.
  • Recommended performance metrics and modeling framework.

Task 6: Estimate E-Commerce VMT and Evaluate Scenarios (Lead: PSU)

Apply the developed models to estimate e-commerce-related VMT and evaluate future scenarios identified in collaboration with project stakeholders.

Deliverables:

  • E-commerce VMT estimates for Seattle and Portland.
  • Maps identifying key freight corridors, delivery activity areas, and e-commerce hotspots.
  • Scenario analysis results to support policy and planning decisions.

Task 7: Final Report and Knowledge Transfer (Joint UW–PSU Effort)

Integrate project findings into a final report and present results to stakeholders.

Deliverables:

  • Draft and final project report.
  • Stakeholder review process.
  • Final webinar presenting project findings and recommendations.
Paper

Mapping Urban Freight Infrastructure for Planning: A Demonstration of a Methodology

URL: https://trid.trb.org/view/1497151
Publication: Transportation Research Record: Journal of the Transportation Research Board
Publication Date: 2018
Summary:

Urban transportation infrastructure includes facilities such as loading docks and curb space which are important for freight pick-up and delivery operations. Information about the location and nature of these facilities is typically not documented for public or private urban freight stakeholders and therefore cannot be used to support more effective private sector operations or public sector planning and engineering decisions. Consequently, there is considerable value in performing an accurate inventory and evaluation of the system. In response to this urban freight challenge, the Seattle Department of Transportation (SDOT) contracted with the Supply Chain Transportation and Logistics Center (SCTL) at the University of Washington to develop a process to address the lack of information regarding the capacity for freight and parcel load and unload operations in dense urban areas of Seattle. This works focuses on the development of a data collection method for documenting private urban freight infrastructure that does not require prior permission, is ground-truthed, and can be completed within reasonable cost and time constraints. This paper presents the methodology, which consists of a survey form, survey collection app, data quality control process, data structure and a proposed typology for off public right of way freight loading / unloading infrastructure based on basic physical infrastructure characteristics. The data collection process methodology is applied to three Seattle urban centers. The method was then revised and improved for a second data collection effort in two additional urban centers.

Authors: Dr. Anne GoodchildGabriela Girón-ValderramaDr. Ed McCormackJosé Luis Machado León
Recommended Citation:
Machado-León, Jose Luis, Gabriela del Carmen Giron-Valderrama, Anne Goodchild, and Edward McCormack. Mapping Urban Freight Infrastructure for Planning: A Demonstration of a Methodology. No. 18-06171. 2018.
Paper

Forecasting Tools for Analyzing Urban Land Use Patterns and Truck Movement: A Case Study and Discussion

 
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URL: https://doi.org/10.3141/2547-11
Publication: Transportation Research Record
Volume: Volume 2547
Pages: 74-82
Publication Date: 2016
Summary:

Many urban planning efforts have supported development in dense, mixed-use areas, but tools are not widely available to help understand the relationship between urban form and goods movement. A review is presented on the status of urban goods movement forecasting models to account for the impacts of density and mixed land use. A description is given of a series of forecasting model runs conducted with state-of-the-practice tools available at the Puget Sound Regional Council. By comparing dense, mixed-use scenarios with different baseline and transportation network alternatives, the ability of the model to capture the relationship between goods movement and density is evaluated. The paper concludes with a discussion of the implications of the results for truck forecasting and freight planning.

Authors: Dr. Anne GoodchildDr. Ed McCormack, Erica Wygonik, Alon Bassok, Daniel Carlson
Recommended Citation:
Wygonik, Erica, Alon Bassok, Edward McCormack, Anne Goodchild, and Daniel Carlson. "Forecasting Tools for Analyzing Urban Land Use Patterns and Truck Movement: Case Study and Discussion of Results." Transportation Research Record 2547, no. 1 (2016): 74-82.