Robust Decision-Making Framework for Sustainable Operations and Planning of MBTA Rapid Transit Vehicles

Han, Zhuo; Christofa, Eleni; Gonzales, Eric; Oke, Jimi

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Robust Decision-Making Framework for Sustainable Operations and Planning of MBTA Rapid Transit Vehicles

2024-09-01
By Han, Zhuo ; Christofa, Eleni ; Gonzales, Eric

File Language:

English

Details

Creators:

Han, Zhuo ; Christofa, Eleni ; Gonzales, Eric ; Oke, Jimi
Corporate Creators:

University of Massachusetts at Amherst
Contributors:

Donaghy, Sean
Corporate Contributors:

Massachusetts. Department of Transportation. Office of Transportation Planning ; United States. Department of Transportation. Federal Highway Administration
Subject/TRT Terms:
Publication/ Report Number:

24-063
Resource Type:

Tech Report
Geographical Coverage:

Massachusetts ; United States
Edition:

Final Report July 2024 March 2023-September 2024
Corporate Publisher:

Massachusetts. Dept. of Transportation. Office of Transportation Planning
Abstract:

Urban Rail Transit (URT) systems play a critical role in modern cities but consume significant amounts of energy. Understanding and predicting energy consumption patterns in these systems is vital for sustainable urban planning, especially during disruptive events. This study presents a Long Short-Term Memory (LSTM) recurrent neural network, the model can accurately predict daily energy consumption and average daily temperature, with root mean squared errors (RMSE) of 50.6 MWh and 6.62°F. Additionally, a decision-making tool was developed to simulate various operational strategies and their impacts on energy consumption and temperature. These findings provide URT operators with a robust framework for making data-driven decisions and improving energy efficiency in URT systems.
Format:

PDF
Download URL:

https://rosap.ntl.bts.gov/view/dot/79350/dot_79350_DS1.pdf
File Type:

[PDF - 4.88 MB]
Collection(s):

Artificial Intelligence Deployment Center of Excellence (AI COE) ; US Transportation Collection
Main Document Checksum:

urn:sha-512:3ecd5abc4e6526457e154ba134f697dd4898c0be8298d80802a151113d347ecc3d5de2819e8f48706854b95c2811e857f591cb4130e987ef134f7241b94d8f6a

File Language:

English

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