• TRB 100th Annual Meeting

      January 24–28, 2021

    • Online Program

2021

Annual Meeting Event Detail




Poster Session 1331

1331 - Putting Transit Data to Work: Advancements in Making, Mixing & Matching, and Employing Machine Learning (ML) Techniques

Wednesday, January 27 4:00 PM- 5:30 PM ET
Poster
Catherine Lawson, SUNY Albany
Sponsored by:
Standing Committee on Transit Data (AP090)

This session will focus on methodological advancements with transit data, including making, mixing and matching, and employing machine learning (ML) techniques to address a number of issues.  Topics include transit and COVID, customer satisfaction, E-scooters, direct demand modeling, unplanned disruptions, WIFI, and so much more!



No agenda available

Title Presentation Number
Using Data Science and GIS-Based Analysis of Transit Passenger Complaints to Uncover Patterns of Passenger Frustration
Moran Yona, Israel Public Transport Authority, Israel Ministry of Transport
Genadi Birfir, Adalya Consulting and Management
Sigal Kaplan, Hebrew University of Jerusalem
Show Abstract
TRBAM-21-00252
Synthetic Mobility Traces from Mobile Phone Data, Infection Modelling and Public Transport
Sebastian Müller (mueller@vsp.tu-berlin.de), Technische Universität Berlin
Christian Rakow, Technische Universitat Berlin
Kai Nagel, Technische Universitat Berlin
Show Abstract
TRBAM-21-00778
Analysing the Role of Waiting Time Reliability in Public Transport Route Choice Using Smart Card Data
Sanmay Shelat (s.shelat@tudelft.nl), Delft University of Technology
Oded Cats, Delft University of Technology
Niels van Oort, Technische Universiteit Delft
Hans van Lint, Technische Universiteit Delft
Show Abstract
TRBAM-21-01189
Public Opinions on Public Transportation amid COVID-19 Pandemic: Preliminary Analysis of Social Media Data
SONG HE (she7@gmu.edu), George Mason University
Ossama Salem, George Mason University
Show Abstract
TRBAM-21-01303
Short-term Forecast on Individual Accessibility in Bus System Based on Neural Network
Yufan Zuo, Southeast University
Di Huang, Southeast University
Xiao Fu, Southeast University
Zhiyuan Liu, Southeast University
Show Abstract
TRBAM-21-02488
Automated Capturing of Transit Passenger Transfers and Network O-D Using An Optimized Wi-Fi-Based Recognition Algorithm
Majeed Algomaiah, University of Louisville
Richard Li (richard.li@louisville.edu), University of Louisville
Show Abstract
TRBAM-21-02761
Why Do People Take E-scooter Trips? Big Data and Unsupervised Machine Learning Insights on Temporal and Spatial Usage Patterns
Nitesh Shah, University of Tennessee
Jing Guo, University of Tennessee, Knoxville
Lee Han, University of Tennessee
Christopher Cherry (cherry@utk.edu), University of Tennessee, Knoxville
Show Abstract
TRBAM-21-02878
Unplanned Disruption Analysis in Urban Railway Systems Using Smart Card Data
Tianyou Liu, Northeastern University
Zhenliang Ma, Monash University
Haris Koutsopoulos, Northeastern University
Show Abstract
TRBAM-21-04106
Analyzing Bus Ridership with a Spatial Direct Demand Model
Raven McKnight, Metro Transit (MN)
Eric Lind, Metro Transit, Minneapolis-St. Paul
Show Abstract
TRBAM-21-04281
Using Origin-Destination Flows Determined from APC and AFC Data to Correct Biases in Socioeconomic and Travel Characteristics Obtained from Transit Onboard Surveys
Rabi Mishalani (mishalani@osu.edu), Ohio State University
Mark McCord, Ohio State University
Show Abstract
TRBAM-21-01961
Short-term Metro Passenger Flow Prediction Capturing the Impact of Unplanned Events
yangyang zhao, Southwest Jiaotong University
Zhenliang Ma, Monash University
Show Abstract
TRBAM-21-03642
An Examination of New York City Transit’s Bus and Subway Ridership Trends during the COVID-19 Pandemic
Anne Halvorsen, MTA New York City Transit
Daniel Wood, MTA New York City Transit
Darian Jefferson, MTA New York City Transit
Timon Stasko, No Organization
Jack Hui, MTA New York City Transit
Alla Reddy, MTA New York City Transit
Show Abstract
TRBAM-21-01633
Understanding Ridesplitting Behavior with Interpretable Machine Learning Models: Comparing Trip-level and Community-level Characteristics using Chicago’s Ridesourcing Trips
Hoseb Abkarian, Northwestern University
Ying Chen, Northwestern University
Hani Mahmassani, Northwestern University
Show Abstract
TRBAM-21-02248
Examining the Discrepancy between Self-Reported and Actual Commuting Behavior at the Individual Level
Tianyu Su (sutianyu@mit.edu), Massachusetts Institute of Technology (MIT)
M. Elena Renda, Istituto di Informatica e Telematica del CNR
Jinhua Zhao, Massachusetts Institute of Technology (MIT)
Show Abstract
TRBAM-21-02766

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