Optimizing Traffic Controllers along the MLK Smart Corridor Using Reinforcement Learning and Digital Twin

Abhilasha Saroj; Toan V. Trant; Angshuman Guin; Michael Hunter; Mina Sartipi

doi:10.1109/DTPI55838.2022.9998928

Optimizing Traffic Controllers along the MLK Smart Corridor Using Reinforcement Learning and Digital Twin

Abhilasha Saroj, Toan V. Trant, Angshuman Guin, Michael Hunter, Mina Sartipi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

With advancements in Intelligent Transportation Systems (ITS), sensors, and computing resources, several cities across the world are investing in the development of smart/connected corridors. These corridors are being equipped with advanced sensors that provide real-time, high-resolution data from the corridor and enable vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. The objective of this study is to optimize signal timings for one such smart corridor - MLK Smart Corridor - in Chattanooga, Tennessee, USA with respect to fuel and energy consumption (represented by Fuel Consumption Intersection Control Performance Index, EcoPI, that determines the excess fuel consumption due to stops and delays caused by traffic controllers).

Original language	English
Title of host publication	2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence, DTPI 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665492270
DOIs	https://doi.org/10.1109/DTPI55838.2022.9998928
State	Published - 2022
Externally published	Yes
Event	2nd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2022 - Boston, United States Duration: Oct 24 2022 → Oct 28 2022

Publication series

Name	2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence, DTPI 2022

Conference

Conference	2nd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2022
Country/Territory	United States
City	Boston
Period	10/24/22 → 10/28/22

Funding

This paper is supported partially by the DOE DE-EE0009208 and NSF CCRI-2120358. The views and opinions of authors expressed herein do not necessarily state or reflect those of the U.S. Government or any agency thereof.

Keywords

digital twin
reinforcement learning
traffic signal control

Access to Document

10.1109/DTPI55838.2022.9998928

Cite this

Saroj, A., Trant, T. V., Guin, A., Hunter, M., & Sartipi, M. (2022). Optimizing Traffic Controllers along the MLK Smart Corridor Using Reinforcement Learning and Digital Twin. In 2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence, DTPI 2022 (2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence, DTPI 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DTPI55838.2022.9998928

Saroj, Abhilasha ; Trant, Toan V. ; Guin, Angshuman et al. / Optimizing Traffic Controllers along the MLK Smart Corridor Using Reinforcement Learning and Digital Twin. 2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence, DTPI 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence, DTPI 2022).

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title = "Optimizing Traffic Controllers along the MLK Smart Corridor Using Reinforcement Learning and Digital Twin",

abstract = "With advancements in Intelligent Transportation Systems (ITS), sensors, and computing resources, several cities across the world are investing in the development of smart/connected corridors. These corridors are being equipped with advanced sensors that provide real-time, high-resolution data from the corridor and enable vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. The objective of this study is to optimize signal timings for one such smart corridor - MLK Smart Corridor - in Chattanooga, Tennessee, USA with respect to fuel and energy consumption (represented by Fuel Consumption Intersection Control Performance Index, EcoPI, that determines the excess fuel consumption due to stops and delays caused by traffic controllers).",

keywords = "digital twin, reinforcement learning, traffic signal control",

author = "Abhilasha Saroj and Trant, {Toan V.} and Angshuman Guin and Michael Hunter and Mina Sartipi",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2nd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2022 ; Conference date: 24-10-2022 Through 28-10-2022",

year = "2022",

doi = "10.1109/DTPI55838.2022.9998928",

language = "English",

series = "2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence, DTPI 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Saroj, A, Trant, TV, Guin, A, Hunter, M & Sartipi, M 2022, Optimizing Traffic Controllers along the MLK Smart Corridor Using Reinforcement Learning and Digital Twin. in 2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence, DTPI 2022. 2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence, DTPI 2022, Institute of Electrical and Electronics Engineers Inc., 2nd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2022, Boston, United States, 10/24/22. https://doi.org/10.1109/DTPI55838.2022.9998928

Optimizing Traffic Controllers along the MLK Smart Corridor Using Reinforcement Learning and Digital Twin. / Saroj, Abhilasha; Trant, Toan V.; Guin, Angshuman et al.
2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence, DTPI 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence, DTPI 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Optimizing Traffic Controllers along the MLK Smart Corridor Using Reinforcement Learning and Digital Twin

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AU - Trant, Toan V.

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AU - Hunter, Michael

AU - Sartipi, Mina

PY - 2022

Y1 - 2022

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AB - With advancements in Intelligent Transportation Systems (ITS), sensors, and computing resources, several cities across the world are investing in the development of smart/connected corridors. These corridors are being equipped with advanced sensors that provide real-time, high-resolution data from the corridor and enable vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. The objective of this study is to optimize signal timings for one such smart corridor - MLK Smart Corridor - in Chattanooga, Tennessee, USA with respect to fuel and energy consumption (represented by Fuel Consumption Intersection Control Performance Index, EcoPI, that determines the excess fuel consumption due to stops and delays caused by traffic controllers).

KW - digital twin

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M3 - Conference contribution

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BT - 2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence, DTPI 2022

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Saroj A, Trant TV, Guin A, Hunter M, Sartipi M. Optimizing Traffic Controllers along the MLK Smart Corridor Using Reinforcement Learning and Digital Twin. In 2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence, DTPI 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence, DTPI 2022). doi: 10.1109/DTPI55838.2022.9998928

Optimizing Traffic Controllers along the MLK Smart Corridor Using Reinforcement Learning and Digital Twin

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