Machine Learning
Machine learning is an application of artificial intelligence (Al), focused on building applications that learn from data and improve their accuracy over time without being programmed to do so. Today, examples of machine learning are all around us. Machine learning can be used where designing and programming explicit algorithms cannot be done traditionally. These algorithms are often categorized as supervised or unsupervised.
DeepTake: Prediction Of Driver Takeover Behavior Using Multimodal Data
Erfan Pakdamanian • Shili Sheng • Sonia Baee • Seongkook Heo • Sarit Kraus • Lu Feng
Jan 15, 2021
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arXiv
Formal Analysis Of A Neural Network Predictor inShared-Control Autonomous Driving
John M. Grese • Corina Pasareanu • Erfan Pakdamanian
Jan 4, 2021
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Aerospace Research Central
Review Of Learning-Based Longitudinal Motion Planning For Autonomous Vehicles: Implications On Traffic Congestion
Hao Zhou • Jorge A. Laval • Anye Zhou • Yu Wang • Wenchao Wu • Zhu Qing • Srinivas Peeta
Jan 1, 2021
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Transportation Research Board (TRB)
Safety Critical Event Prediction Through Unified Analysis Of Driver And Vehicle Volatilities: Application Of Deep Learning Methods
Ramin Arvin • Asad J. Khattak • Hairong Qi
Dec 29, 2020
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Accident Analysis & Prevention
Classifying Travelers' Driving Style Using Basic Safety Messages Generated By connected Vehicles: Application Of Unsupervised Machine Learning
Amin Mohammadnazar • Ramin Arvin • Asad J. Khattak
Dec 16, 2020
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Transportation Research Part C: Emerging Technologies
Efficient AI and Prediction Techniques for Smart 5G-enabled Vehicular Networks
Noura Aljeri
Nov 24, 2020
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uOttawa Theses
Short-Term Demand Forecasting for on-Demand Mobility Service
Xinwu Qian • Satish V. Ukkusuri • Chao Yang • Fenfan Yan
Sep 3, 2020
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IEEE Transactions on Intelligent Transportation Systems
Toward Sustainable And Economic Smart Mobility: Shaping The Future Of Smart Cities
Mahmoud Hashem Eiza • Yue Cao • Lexi Xu
Jun 18, 2020
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World Scientific
Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era
Alexandros Nikitas • Kalliopi Michalakopoulou • Eric Tchouamou Njoya • Dimitris Karampatzakis
Apr 1, 2020
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Sustainability
Smart mobility and public transport: Opportunities and challenges in rural and urban areas
Simone Porru • Francesco Edoardo Misso • Filippo Eros Pani • Cino Repetto
Feb 1, 2020
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journal of traffic and transportation engineering
Deep Truck: A Deep Neural Network Model For Longitudinal Dynamics Of Heavy Duty Trucks
Saleh Albeaik • Fang-Chieh Chou • Xiao-Yun Lu • Alexandre M. Bayen
Nov 28, 2019
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2019 IEEE Intelligent Transportation Systems Conference
Autonomous Vehicles and Embedded Artificial Intelligence: The Challenges of Framing Machine Driving Decisions
Martin Cunneen • Martin Mullins • Finbarr Murphy
May 13, 2019
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Applied Artificial Intelligence
Applying Machine Learning Approaches to Analyze The Vulnerable Road-Users' Crashes At Statewide Traffic Analysis Zones
Md Sharikur Rahman • Mohamed Abdel-Aty • Samiul Hasan • Qing Cai
May 10, 2019
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Journal Of Safety Research
Classifying Travelers' Driving Style Using Basic Safety messages Generated By Connected Vehicles: Application Of Unsupervised Machine Learning
Amin Mohammadnazar • Ramin Arvin • Asad J. Khattak
Dec 16, 2018
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Transportation Research Part C: Emerging Technologies
Predicting Station-Level Hourly Demand in a Large-scale Bike-Sharing Network: A Graph Conolutional Neural Network Approach
Lei Lin • Zhengbing He • Srinivas Peeta
Nov 5, 2018
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Transportation Research Part C: Emerging Technologies
The Station-Free Sharing Bike Demand Forecasting With a Deep Learning Approach And Large-Scale Datasets
Chengcheng Xu • Junyi Ji • Pan Liu
Jul 19, 2018
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Transportation Research Part C: Emerging Technologies