Large-scale and Long-term Forecasting of Performance Measurement of Public Transportation Systems
Accurate forecasting of public transportation metrics is critical towards the high reliability and efficiency of the public transportation system. However, deploying a forecasting system to serve city-level public transportation with long-term forecasting is challenging. In this project, the authors develop the capability for processing the entire Los Angeles Metropolitan Area (LAMA) for long-term forecasting of a variety of public transportation system performance metrics. First, the authors explore both spatial statistical methods and machine learning methods to estimate traffic flows for the road segments that do not have traffic sensors. Second, the authors develop methods to enable traffic forecasting with a deep learning model designed for small networks for the entire LAMA road network. The authors also study various training strategies (e.g., teacher forcing) to enable accurate long-term forecasting of traffic flows and bus arrival times. Lastly, the authors develop an end-to-end deep learning approach that combines the estimation and forecasting of traffic flow with data imputation methods for estimating bus arrival time for each stop in individual bus routes in LAMA. Using the real-world data in the University of Southern California Archived Transportation Data Management System (ADMS), the authors show that the proposed approach and system are capable of predicting bus arrival times with a city-level spatial coverage and a route-level temporal forecasting horizon. The authors also demonstrate the overall result of the bus arrival time estimation in a web dashboard. This dashboard enables users at all levels of technical skills to benefit from the developed machine learning approach and access to valuable information for trip planning, vehicle management, and policymaking.
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- Summary URL:
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Supplemental Notes:
- This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
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Corporate Authors:
METRANS Transportation Center
University of Southern California
Los Angeles, CA United States 90089-0626Pacific Southwest Region University Transportation Center
University of Southern California
Los Angeles, CA United States 90089California Department of Transportation
Sacramento, CA United States 95819Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Authors:
- Tran, Luan
- Chiang, Yao-Yi
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0000-0002-8923-0130
- Shahabi, Cyrus
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0000-0001-9118-0681
- Publication Date: 2021-7-26
Language
- English
Media Info
- Media Type: Digital/other
- Edition: Final Report
- Features: Figures; References; Tables;
- Pagination: 35p
Subject/Index Terms
- TRT Terms: Arrivals and departures; Bus routes; Machine learning; Performance measurement; Public transit; Traffic forecasting
- Geographic Terms: Los Angeles Metropolitan Area
- Subject Areas: Operations and Traffic Management; Planning and Forecasting; Public Transportation;
Filing Info
- Accession Number: 01785223
- Record Type: Publication
- Report/Paper Numbers: PSR-20-21 TO-038
- Contract Numbers: USDOT Grant 69A3551747109; Caltrans 65A0674 TO 038
- Files: UTC, NTL, TRIS, ATRI, USDOT, STATEDOT
- Created Date: Oct 22 2021 9:18AM