• TRB 100th Annual Meeting - A Virtual Event

      January 24–28, 2021

    • Online Program


Annual Meeting Event Detail

Poster Session 1433

1433 - Artificial Intelligence and Machine Learning Methods for Transportation Applications (Part III)

Friday, January 29 11:30 AM- 1:00 PM ET
Kakan Dey, West Virginia University
Sponsored by:
Standing Committee on Artificial Intelligence and Advanced Computing Applications (AED50)

This session presents a variety of artificial intelligence and machine learning methods and tools that have been recently applied to solve problems and improve the operation and safety in a wide spectrum of transportation applications.  

No agenda available

Title Presentation Number
Multi-stage emergency decision-making method based on cumulative prospect theory and intuitionistic fuzzy number
Junxiang XU, Southwest Jiaotong University
Jin Zhang, Southwest Jiaotong University
Jingni Guo, Southwest Jiaotong University
Show Abstract
Spatiotemporal Attention-Based Graph Convolution Network for Segment-Level Traffic Prediction
Duo Li (dl655@cam.ac.uk), University of Cambridge
Joan Lasenby, University of Cambridge
Show Abstract
State-wide Traffic Volume Estimation for Non-freeway Roads Using Probe-vehicle Data and Machine Learning Methods
Yi Hou, National Renewable Energy Laboratory (NREL)
Venu Garikapati, National Renewable Energy Laboratory (NREL)
Christopher Hoehne, National Renewable Energy Laboratory (NREL)
Kevin Kasundra, National Renewable Energy Laboratory (NREL)
Stanley Young, National Renewable Energy Laboratory (NREL)
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Travel and Built Environment: A Deep Learning Approach
Shenhao Wang (shenhao@mit.edu), Massachusetts Institute of Technology (MIT)
Rachel Luo, Massachusetts Institute of Technology (MIT)
Xiaohu Zhang, Massachusetts Institute of Technology (MIT)
Jason Lu, Massachusetts Institute of Technology (MIT)
Hongzhou Lin, Massachusetts Institute of Technology (MIT)
Joan Walker, University of California, Berkeley
Jinhua Zhao, Massachusetts Institute of Technology (MIT)
Show Abstract
Proposed Data Model and Prototype Software for Improving the Efficiency and Efficacy of the Transportation Research Board Annual Meeting Paper Peer Review Process
David Ory, WSP
Sijia Wang, WSP
Gayathri Shivaraman, WSP
Show Abstract
Race, Gender, and Income Disparity in Travel Behavior Prediction with Machine Learning
Yunhan Zheng, Massachusetts Institute of Technology (MIT)
Shenhao Wang (shenhao@mit.edu), Massachusetts Institute of Technology (MIT)
Jinhua Zhao, Massachusetts Institute of Technology (MIT)
Show Abstract
A Recurrent Neural Network for Estimating Speed Using Probe Vehicle Data in Urban Area
Jae Hwan Yang, The Seoul Institute
Dong-Kyu Kim, Seoul National University
Seung-Young Kho (sykho@snu.ac.kr), Seoul National University
Show Abstract
Demand-driven optimization method for shared mobility services
Cyril VEVE (cyril.veve@entpe.fr), Ecole Nationale des Travaux Publics de l'Etat
Nicolas Chiabaut, Universite de Lyon
Show Abstract
Crash prediction through unified analysis of driver and vehicle volatilities: Application of 1D-Convolutional Neural Network - Long Short-Term Memory
Ramin Arvin (rarvin@vols.utk.edu), University of Tennessee, Knoxville
Asad J. Khattak, University of Tennessee
Hairong Qi, University of Tennessee, Knoxville
Show Abstract
Linkage Problem in Mathematical Optimization of Transportation Networks
Murat Bayrak, Pennsylvania State University
S. Ilgin Guler (sig123@psu.edu), Pennsylvania State University
Show Abstract
Modeling Anticipation and Relaxation of Lane Changing Behavior Using Deep Learning
Ke quan Chen, Southeast University
Zhibin Li, Southeast University
Pan Liu, Southeast University
Yuxuan Wang, Southeast University
Yunxue Lu, Southeast University
Show Abstract
Development of a Multi-Distress Detection System for Asphalt Pavements: A Transfer Learning-based Approach
Naga Siva Pavani Peraka, Indian Institute of Technology, Tirupati
Krishna Prapoorna Biligiri, Indian Institute of Technology, Tirupati
Satyanarayana Kalidindi, Indian Institute of Technology, Tirupati
Show Abstract
A CNN-Based In-Vehicle Occupant Detection and Classification Method Using SHRP 2 Cabin Images
Ioannis Papakis (ioannis1@vt.edu), Virginia Polytechnic Institute and State University (Virginia Tech)
Abhijit Sarkar, Virginia Polytechnic Institute and State University (Virginia Tech)
Andrei Svetovidov, Virginia Polytechnic Institute and State University (Virginia Tech)
Jeffrey Hickman, Virginia Polytechnic Institute and State University (Virginia Tech)
Amos Abbott, Virginia Polytechnic Institute and State University (Virginia Tech)
Show Abstract
Mining association rules between near-crash events and geometric and trip features through a naturalistic driving dataset
Xiaoqiang "Jack" Kong (jackxqkong@gmail.com), Texas A&M University, College Station
Subasish Das, Texas A&M University
Hongmin "Tracy" Zhou, Texas A&M University
Yunlong Zhang, Texas A&M University, College Station
Show Abstract
Using an Interpretable Machine Learning Framework to Understand the Relationship of Mobility and Reliability Indices on Truck Drivers’ Route Choices
Xiaoqiang "Jack" Kong (jackxqkong@gmail.com), Texas A&M University, College Station
Yunlong Zhang, Texas A&M University, College Station
William Eisele, Texas A&M Transportation Institute
Xiao Xiao, Texas A&M University
Show Abstract
Weiran Yao, Carnegie Mellon University
Sean Qian (seanqian@cmu.edu), Carnegie Mellon University
Show Abstract
Intelligent Helipad Detection And(Grad-Cam) Estimation Using Satellite Imagery
David Specht, Rowan University
Asim Waqas, Rowan University
Ghulam Rasool, Rowan University
Charles Johnson, Federal Aviation Administration (FAA)
Nidhal Bouaynaya, Rowan University
Show Abstract
Development of Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) Applications to Classify Pavement Cracks as Top-down, Bottom-up, and Cement-Treated Reflective Cracking
Nirmal Dhakal, Louisiana State University
Mostafa Elseifi (elseifi@lsu.edu), Louisiana State University
Zia Zihan, Iowa State University
Zhongjie Zhang, Louisiana Department of Transportation and Development
Christophe Fillastre, Louisiana Department of Transportation and Development
Jagannath Upadhyay, State University of New York (SUNY)
Show Abstract
A Comparative Evaluation of Established and Contemporary Deep Learning Traffic Prediction Methods
Ta Jiun Ting, University of Toronto
Xiaoyu Wang, University of Toronto
Islam Taha, University of Toronto
Scott Sanner, University of Toronto
Baher Abdulhai, University of Toronto
Show Abstract
A Novel Transfer Learning Framework for Cross-Scene Pavement Distress Detection
Yishun Li, Tongji University
Pengyu Che, Tongji University
Chenglong Liu, Tongji University
Yuchuan Du (wszwsz179@163.com), Tongji University
Jian Wang, China Mobile (Shanghai) ICT Co Ltd
Show Abstract
Spatiotemporal Traffic Data Imputation and Pattern Discovery with Bayesian Kernelized Probabilistic Matrix Factorization
Mengying Lei, McGill University
Aurelie Labbe, HEC Montreal
Lijun Sun, McGill University
Show Abstract
A Reinforcement Learning Method based on Graph Attention Network for Multi-Depot Vehicle Routing Problem with Soft Time Window
Ke Zhang, Tsinghua University
Meng Li (mengli@tsinghua.edu.cn), Tsinghua University
Xi Lin, Tsinghua University
FANG HE, Tsinghua University
HUIPING LI, Tsinghua University
Show Abstract
Estimating Pedestrian Crossing Volume at Signalized Intersections
Xiaofeng Li (lxf14120852@gmail.com), University of Arizona
Peipei Xu, University of Arizona
Yao-Jan Wu, University of Arizona
Show Abstract
Vehicle Trajectory Planning with Hierarchical Imitation Learning in Highway Merging Scenarios
Zhen Yang, University of Michigan
Yiheng Feng (feng333@purdue.edu), Purdue University
Henry Liu, University of Michigan, Ann Arbor
Show Abstract
Freeway Traffic State Estimation Using Physics-guided Machine Learning Technique
Zhao Zhang, University of Utah
Yun Yuan, University of Utah
Xianfeng Yang (x.yang@utah.edu), University of Utah
Show Abstract
Large-scale Freeway Traffic Volume Estimation using Crowdsourced Speed Data: A Case Study in Arizona
Adrian Cottam (acottam1@email.arizona.edu), University of Arizona
Xiaobo Ma, University of Arizona
Xiaofeng Li, University of Arizona
Yao-Jan Wu, University of Arizona
Show Abstract
Deep Reinforcement Learning Approach for Improving Freeway Lane Reduction Bottlenecks Throughput Via Variable Speed Limit Control Through Connected Vehicles
Reza Vatani, Old Dominion University
Mecit Cetin, Old Dominion University
Show Abstract
Unsupervised Learning to Support Early Identification of Traffic Pattern Changes: A Case Study of 2018 Heavy Rain Disaster in Hiroshima
Canh Do (canh.doxuan@gmail.com), Hiroshima University
Makoto Chikaraishi, Hiroshima University
Akimasa Fujiwara, "Hiroshima Daigaku"
Yasuhiro Kusuhashi, West Nippon Expressway Engineering Chugoku
Show Abstract
Avoiding Gridlock On Large Congested Networks: A Multi-agent Deep Reinforcement Learning Approach with Spillback Knowledge
Hao Zhou (zhouhao@gatech.edu), Georgia Institute of Technology (Georgia Tech)
Jorge Laval, Georgia Institute of Technology (Georgia Tech)
Show Abstract
Multi-Modal Traffic Speed Monitoring: A Real-Time System Based on Passive Wi-Fi and Bluetooth Sensing Technology
Ziyuan Pu, University of Washington
Zhiyong Cui, University of Washington
Shuo Wang, University of Washington
Hao Yang, University of Washington
Yinhai Wang (yinhai@uw.edu), University of Washington
Show Abstract
Freeway Traffic State Prediction using Constructed Traffic Information from Hybrid Machine Learning
Zhao Zhang, University of Utah
Yun Yuan, University of Utah
Xianfeng Yang (x.yang@utah.edu), University of Utah
Show Abstract

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