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BISTRO: Berkeley Integrated System for Transportation Optimization

Authors:

Sidney A. Feygin,

Jessica R. Lazarus,

Edward H. Forscher,

Valentine Golfier-Vetterli,

Jonathan W. Lee,

Abhishek Gupta,

Rashid A. Waraich,

Colin J. R. Sheppard,

Alexandre M. BayenAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 11, Issue 4

Article No.: 38, Pages 1 - 27

https://doi.org/10.1145/3384344

Published: 24 June 2020 Publication History

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Abstract

The current trend toward urbanization and adoption of flexible and innovative mobility technologies will have complex and difficult-to-predict effects on urban transportation systems. Comprehensive methodological frameworks that account for the increasingly uncertain future state of the urban mobility landscape do not yet exist. Furthermore, few approaches have enabled the massive ingestion of urban data in planning tools capable of offering the flexibility of scenario-based design.

This article introduces Berkeley Integrated System for Transportation Optimization (BISTRO), a new open source transportation planning decision support system that uses an agent-based simulation and optimization approach to anticipate and develop adaptive plans for possible technological disruptions and growth scenarios. The new framework was evaluated in the context of a machine learning competition hosted within Uber Technologies, Inc., in which over 400 engineers and data scientists participated. For the purposes of this competition, a benchmark model, based on the city of Sioux Falls, South Dakota, was adapted to the BISTRO framework. An important finding of this study was that in spite of rigorous analysis and testing done prior to the competition, the two top-scoring teams discovered an unbounded region of the search space, rendering the solutions largely uninterpretable for the purposes of decision-support. On the other hand, a follow-on study aimed to fix the objective function. It served to demonstrate BISTRO’s utility as a human-in-the-loop cyberphysical system: one that uses scenario-based optimization algorithms as a feedback mechanism to assist urban planners with iteratively refining objective function and constraints specification on intervention strategies. The portfolio of transportation intervention strategy alternatives eventually chosen achieves high-level regional planning goals developed through participatory stakeholder engagement practices.

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Cited By

Lazarus JSchwinn MToulet LYu ZChen ATang XKasriel TBayen A(2024)Multi-Objective Transportation System Optimization Using Agent-Based Simulation—A Study of Cordon- and Mileage-Based Congestion PricingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.335736025:3(2270-2280)Online publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3357360
Harris DDa Silva FSu WGlatt R(2024)A Review on Simulation Platforms for Agent-Based Modeling in Electrified TransportationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.331892825:2(1131-1147)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TITS.2023.3318928
do Amaral JMontevechi Jde Carvalho Miranda Rdos Santos C(2024)Adaptive metamodeling simulation optimization: Insights, challenges, and perspectivesApplied Soft Computing10.1016/j.asoc.2024.112067165(112067)Online publication date: Nov-2024
https://doi.org/10.1016/j.asoc.2024.112067
Show More Cited By

Index Terms

BISTRO: Berkeley Integrated System for Transportation Optimization

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 11, Issue 4

Survey Paper and Regular Paper

August 2020

358 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3401889

Editor:
Yu Zheng
JD Finance, China

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Copyright © 2020 ACM.

© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Publication History

Published: 24 June 2020

Online AM: 07 May 2020

Accepted: 01 February 2020

Revised: 01 January 2020

Received: 01 September 2019

Published in TIST Volume 11, Issue 4

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Cited By

Lazarus JSchwinn MToulet LYu ZChen ATang XKasriel TBayen A(2024)Multi-Objective Transportation System Optimization Using Agent-Based Simulation—A Study of Cordon- and Mileage-Based Congestion PricingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.335736025:3(2270-2280)Online publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3357360
Harris DDa Silva FSu WGlatt R(2024)A Review on Simulation Platforms for Agent-Based Modeling in Electrified TransportationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.331892825:2(1131-1147)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TITS.2023.3318928
do Amaral JMontevechi Jde Carvalho Miranda Rdos Santos C(2024)Adaptive metamodeling simulation optimization: Insights, challenges, and perspectivesApplied Soft Computing10.1016/j.asoc.2024.112067165(112067)Online publication date: Nov-2024
https://doi.org/10.1016/j.asoc.2024.112067
Wang L(2023)3D urban landscape rendering and optimization algorithm for smart cityIntelligent Decision Technologies10.3233/IDT-23041817:4(943-958)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/IDT-230418
Ribeiro de Oliveira TBiancardi Rodrigues BMoura da Silva MAntonio N. Spinassé RGiesen Ludke GRuy Soares Gaudio MIglesias Rocha Gomes GGuio Cotini Lda Silva Vargens DQueiroz Schimidt MVarejão Andreão RMestria M(2023)Virtual Reality Solutions Employing Artificial Intelligence Methods: A Systematic Literature ReviewACM Computing Surveys10.1145/356502055:10(1-29)Online publication date: 2-Feb-2023
https://dl.acm.org/doi/10.1145/3565020

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