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

arXiv

Formal Analysis Of A Neural Network Predictor inShared-Control Autonomous Driving

John M. Grese • Corina Pasareanu • Erfan Pakdamanian

Jan 4, 2021

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

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

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

Transportation Research Part C: Emerging Technologies

Efficient AI and Prediction Techniques for Smart 5G-enabled Vehicular Networks

Noura Aljeri

Nov 24, 2020

uOttawa Theses

Short-Term Demand Forecasting for on-Demand Mobility Service

Xinwu Qian • Satish V. Ukkusuri • Chao Yang • Fenfan Yan

Sep 3, 2020

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

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

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

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

 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

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

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

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

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

Transportation Research Part C: Emerging Technologies