West Seattle Bridge Case Study (Phase I)

Background
West Seattle is an area of the city of Seattle 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 West Seattle to the rest of the city, was closed indefinitely to traffic due to its increasing rate of structural deterioration. Moreover, access to the Spokane Street Lower Bridge, a smaller bridge connecting West Seattle with Harbor Island and the rest of the city, was also restricted; prioritizing heavy freight, public transit, and emergency vehicles. After the bridge closure and restrictions, the total number of vehicle travel lanes crossing the Duwamish River was reduced from 21 to 12.

The unexpected closure of WSHB disrupted passenger and freight mobility to and from West Seattle, increasing travel times and generating bottlenecks on the remaining bridges, which can potentially negatively impact the livability of the peninsula as well as its economy and the environment. The situation might further deteriorate as traffic demand to and from West Seattle increases during recovery from the COVID-19 pandemic.

The Seattle Department of Transportation (SDOT) is taking actions to monitor changes in travel behavior to/from West Seattle and identify and implement strategies that could mitigate the negative impacts caused by the WSHB closure.

Goals
SDOT has engaged the Urban Freight Lab to conduct research to explore strategies to alleviate congestion impacts and minimize the disruption of goods and service delivery to West Seattle.

The purpose of this study is to support SDOT to:

understand current freight movements and freight demand in West Seattle;
identify a data-driven mitigation strategy for freight and service flow to and from West Seattle;
assess ex-ante the effectiveness of an implemented strategy.

The freight operations considered and analyzed within the scope of the project are consumer goods and services destined for West Seattle residents and businesses. Intermediate goods and raw materials destined for construction of production and other goods transiting through West Seattle but not destined for local residents or businesses will not be studied.

Continuation
This project continues with the West Seattle Bridge Case Study Phase II.

Article

Physics-Informed Machine Learning of Parameterized Fundamental Diagrams

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URL: https://doi.org/10.48550/arXiv.2208.00880

Publication: arXiv

Volume: 2208.0088

Publication Date: 2022

Summary:

Fundamental diagrams describe the relationship between speed, flow, and density for some roadway (or set of roadway) configuration(s). These diagrams typically do not reflect, however, information on how speed-flow relationships change as a function of exogenous variables such as curb configuration, weather or other exogenous, contextual information. In this paper we present a machine learning methodology that respects known engineering constraints and physical laws of roadway flux–those that are captured in fundamental diagrams– and show how this can be used to introduce contextual information into the generation of these diagrams. The modeling task is formulated as a probe vehicle trajectory reconstruction problem with Neural Ordinary Differential Equations (Neural ODEs). With the presented methodology, we extend the fundamental diagram to non-idealized roadway segments with potentially obstructed traffic data. For simulated data, we generalize this relationship by introducing contextual information at the learning stage, i.e. vehicle composition, driver behavior, curb zoning configuration, etc, and show how the speed-flow relationship changes as a function of these exogenous factors independent of roadway design.

Authors: Thomas MaxnerDr. Andisheh Ranjbari, James Koch, Vinay Amatya, Chase Dowling

Recommended Citation:
Koch, J., Maxner, T., Amatya, V.C., Ranjbari, A., & Dowling, C.P. (2022). Physics-informed Machine Learning of Parameterized Fundamental Diagrams.

Paper

Do Commercial Vehicles Cruise for Parking? Empirical Evidence from Seattle

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URL: https://doi.org/10.1016/j.tranpol.2020.06.013

Publication: Transport Policy

Volume: 97

Pages: 26-36

Publication Date: 2020

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 affect 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 Seattle downtown. 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.

Authors: Dr. Anne GoodchildDr. Giacomo Dalla Chiara

Recommended Citation:
Dalla Chiara, Giacomo, & Goodchild, Anne. (2020) Do Commercial Vehicles Cruise for Parking? Empirical Evidence from Seattle. Transport Policy, 97, 26-36. https://doi.org/10.1016/j.tranpol.2020.06.013