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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.

Paper

A Meta-Heuristic Solution Approach to Isolated Evacuation Problems

 
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Publication: IEEE (Institute of Electrical and Electronics Engineers)
Volume: 2022 Winter Simulation Conference (WSC) INFORMS
Publication Date: 2022
Summary:

This paper provides an approximation method for the optimization of isolated evacuation operations, modeled through the recently introduced Isolated Community Evacuation Problem (ICEP). This routing model optimizes the planning for evacuations of isolated areas, such as islands, mountain valleys, or locations cut off through hostile military action or other hazards that are not accessible by road and require evacuation by a coordinated set of special equipment. Due to its routing structure, the ICEP is NP-complete and does not scale well. The urgent need for decisions during emergencies requires evacuation models to be solved quickly. Therefore, this paper investigates solving this problem using a Biased Random-Key Genetic Algorithm. The paper presents a new decoder specific to the ICEP, that allows to translate in between an instance of the S-ICEP and the BRKGA. This method approximates the global optimum and is suitable for parallel processing. The method is validated through computational experiments.

Authors: Dr. Anne GoodchildFiete Krutein, Linda Ng Boyle (University of Washington Dept. of Industrial & Systems Engineering)
Recommended Citation:
K. F. Krutein, L. N. Boyle and A. Goodchild, "A Meta-Heuristic Solution Approach to Isolated Evacuation Problems," 2022 Winter Simulation Conference (WSC), Singapore, 2022, pp. 2002-2012, doi: 10.1109/WSC57314.2022.10015470.