Overview

Artificial intelligence is starting to transform the field of traffic safety. At the core of traffic safety engineering, we are trying to spot patterns, make predictions, and develop optimized strategies. These activities have been traditionally done with conventional mathematics: inflexible formulas with only a few input variables applied on an ad hoc basis.

At its core, AI offers a new approach to spot patterns, make predictions, and develop optimized strategies: neural networks. Neural networks are flexible, consume vast amounts of input data, and they can operate on a continuous basis, constantly learning and getting better.

This course will introduce fundamentals of AI to non-AI specialists, examine eight case studies involving the application of AI to road safety, and examine eight case studies involving the application of AI to broader traffic engineering problems. Participants will emerge with a better understanding of what is possible, and be better equipped to initiate and work on AI applications in traffic safety.

Why Online?

• Earn 20 Professional Development Hours
• Expert training by professionals for Professionals: access IRF’s unique curriculum and lectures
• developed by world-class specialists
• Accelerated learning processes: get up to speed and gain new insights in less time and with no travel constraints
• Full access to learning materials and session recordings
• Small classrooms & scheduled One-on-One sessions with instructors
• Self-paced options available
• Interactive group projects and case studies
• Receive IRF Certification

Format

The lectures will be taught over a one-week period with live 2,5-hour on-line sessions held Monday through Thursday. Upon completion of the training program, the IRF will administer an on-line knowledge test. Participants with a score of 80% of the exam will be awarded with a certificate verifying their successful completion of the course.

Learning Objectives

• Understand the key principles & benefits of Machine Learning for safe traffic operations
• Learn how to diagnose road safety problems proactively within a data-rich environment
• Calibrate your safety engineering interventions to precisely match your diagnoses
• Transform your Traffic Engineering processes through Machine Learning
• Anticipate the traffic impacts of Connected & Autonomous Vehicles

Target Audience

• National Road & Transport Agency Executives
• Highway Engineers and Managers
• Federal and State Road Safety Agencies
• Road Safety Professionals
• Private Consultants & Contractors

Agenda

The lectures will be taught over a one-week period with live 2,5-hour on-line sessions held Monday through Thursday. Upon completion of the training program, the IRF will administer an on-line knowledge test. Participants with a score of 80% of the exam will be awarded with a certificate verifying their successful completion of the course.

usingartificialintelligence.sched.com/ View the Using Artificial Intelligence to Accelerate Road Safety schedule & directory.

Instructors

LEAD INSTRUCTOR

Lead Road Safety Engineer, MicroTraffic
Craig Milligan, Ph.D., P.Eng., RSP2I

Craig has completed design stage road safety audits for more than $8B of capital works and in-service road safety audits for more than 300 intersections and 4000 km of highway. He applies a scientific and safe systems approach to the identification of road user risk and opportunities for safety improvement.
Craig leads a 30-person team at MicroTraffic, which is active in 64 cities and focused on helping governments to use artificial intelligence and video analytics within their road safety audit and improvement planning activities.
Craig has a Ph.D. in road safety engineering from the University of Manitoba and 15 years of professional and leadership work experience. He worked at the World Bank before starting MicroTraffic and has held leadership roles within the IRF, CARSP, TAC, and TRB.

LinkedIn | MicroTraffic

Computer Requirements

Operating System
Windows 7 – Windows 10, Mac OS X 10.9 (Mavericks), macOS Catalina (10.15), Linux, Google Chrome OS, Android OS 5 (Lollipop) – Android 9 (Pie), iOS 10 – iOS 12, Windows Phone 8+, Windows 8RT+

Web browser
Google Chrome (most recent 2 versions)
Mozilla Firefox (most recent 2 versions)
Internet Explorer v11 (with Adobe Flash if running Windows 7)
Apple Safari (most recent 2 versions)
Microsoft Edge (most recent 2 versions)

Internet connection
1 Mbps or better (broadband recommended)

Hardware
2GB of RAM (minimum), 4GB or more of RAM (recommended)
Microphone and speakers (USB headset recommended)