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Applications of Artificial Intelligence in Transportation Systems
The document provides an overview of artificial intelligence applications in transportation systems, emphasizing the importance of intelligent machines in enhancing decision-making, productivity, and efficiency. It discusses various AI techniques such as big data analytics, computer vision, and predictive analytics, along with their potential applications in self-driving cars, traffic management, and predictive maintenance. Key considerations for implementing AI solutions, such as data privacy and regulations, are also outlined.
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Overview of AI's application in transportation, intelligent machines, and future implications.
The necessity for intelligent machines to empower humans, improve decision-making, and increase productivity.
Endsley's model of situation awareness showing how humans assess situations, integrating perception and comprehension.
Various techniques in AI such as Big Data Analytics, Computer Vision, and Robotics critical for transportation.
Examples of AI transforming various fields like transportation, healthcare, and finance with specific applications.
Introduction to courses on machine learning topics such as regression, decision trees, and deep learning.
Demonstrations of machine learning applications through examples like gaming, medical imaging, and classification.
Insights into changing mobility patterns and innovation in transportation technologies.
Technological advancements in self-driving cars including object detection and safety applications.
AI techniques to improve traffic management, including congestion analysis and smarter traffic solutions.
Benefits of AI in parking management, including space utilization and integration with other services.
Use of telematics for vehicle monitoring, predictive maintenance, and improving safety and efficiency.
The role of AI in freight and transit applications, enhancing delivery speeds and management systems.
The importance of data integration in smart mobility applications and managing complex relationships.
Essential concerns in implementing AI such as data privacy, cybersecurity, ownership, and regulations.
Presented contact information for inquiries, encouraging further audience engagement.
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Applications of Artificial Intelligence in Transportation Systems
- 1. Applications of ArtificialIntelligence in Transportation Systems: An Overview Dr. Mohamed Elshenawy Assistant Professor, Zewail City of Science and Technology 2019-06-23
- 2. Overview • Why weneed AI? • What are intelligent machine? • How can AI change our future? Transportation as an example. • Things to consider when implementing AI solutions. 2019-06-23 2
- 3. Why we needintelligent machines? •Empower humans with technology and qmplify our human intelligence • Ability to analyze large volumes of data to provide a basis for various human decisions. • Transform the ways we do things and drive more value. • Increased productivity and ability to automate repetitive tasks. • Higher Efficiency. 2019-06-23 3
- 4. How humans assessdifferent situations and make decisions? 2019-06-23 4 Dr. Mica Endsley's model of SA.
- 5. How humans assessdifferent situations and make decisions? 2019-06-23 5 Perception “Perceive the status, attributes, dynamics of relevant elements in the environment” Endsley's model of SA. Comprehension Integrate different sources of data and organize them to form a holistic picture of the environment Projection “Project the future actions of the elements in the environment” Decision Make a decision using an effective problem-solving strategy
- 6. Intelligent Machines 2019-06-23 6 Perception Acquire datavia sensors databases, etc. Comprehension Use data fusion and machine learning techniques to characterize situations and understand the environment Projection Use logical reasoning, planning, abstract thinking and other cognitive techniques to project future states and dynamics. Decision Make a decision using an effective problem-solving strategy
- 7.
- 8. How can AIchange our future? AI Transportation Healthcare Manufacturing Finance Agriculture Media Retail Consumer Electronics 2019-06-23 8
- 9. Example applications • Usingpredictive analytics to gain insights of customers, sales, and product performance and provide recommendations on actions that improve companies’ sales. • Predicting repairs and maintenance needs of devices, machinery, cars, trucks, etc. • Home Robots • Self-driving cars • Disease detection, diagnosis, treatment and management processes. • Financial search engines and automated financial investing platforms. • Suggest people to follow, tweets and news based on user’s individual preferences. • ……. And the list goes on 2019-06-23 9
- 10. Examples from CIE407 and CIE 555 CIE 407 • The course provides an overview of underlying concepts and practical algorithms used in machine learning. Topics include linear regression, decision trees, support vector machines, neural networks, ensembles, and reinforcement learning. CIE 555 • The course provides an introduction to deep Learning. Topics include optimization techniques for deep networks, regularization techniques, CNN, RNNs, LSTM, , and more 2019-06-23 10
- 11. Example 1: RLAgent to play Flappy Bird game • By Esraa Magdy • ML Technique: Deep Reinforcement Learning 2019-06-23 11
- 12. Example 2: PneumoniaX-Ray Classification • By: Ahmed Wael • ML Technique: CNN 2019-06-23 12
- 13. Example 3: BanknoteClassification • By: Mohamed Megahed Mohamed Gad Abdallah Shawky AbdelMoez Elsadany • Technique: CNN 2019-06-23 13
- 14. The future oftransportation system 2019-06-23 14 Forces of change: The future of mobility Part of a Deloitte series on the future of mobility
- 15. Self-driving Cars 2019-06-23 15 Photo byVjeran Pavic / The Verge • 3-D positioning and motion estimation of surrounding objects • Accurate detection and prediction of the motion trajectories of surrounding objects • Detect distracted drivers • Maneuvering and Control
- 16. Self-driving cars 2019-06-23 16 Source: Mercedes- dailymail.co.uk
- 17. 2019-06-23 17 •Rear cross-traffic alert •Forwardcollision warning •Automatically adjusts the headlights in response to darkness •Blind Spot Detection and Warning Systems Safety Applications
- 18. Traffic Management • Usea combination of cameras and sensors to analyze road congestion, provide alternative routes, and plan better road networks. • Smarter traffic lights that extends green intervals when needed. • Smarter ramp metering solutions • Smarter incident detection and management 2019-06-23 18 Egypttoday.com
- 19. Traffic Monitoring 2019-06-23 19 A systematicmechanism using time series analysis that imputes missing data in large traffic datasets collected at different locations and at different times. “Automatic Imputation of Missing Highway Traffic Volume Data”, 2018, IEEE International Conference on Pervasive Computing and Communications
- 20. Parking • Better spacemanagement • Better payment and pricing solutions (payments can be integrated with other services such as public transport passing) • Efficient demand management • Adding services (electric-vehicle charging stations, security features,etc) 2019-06-23 20 www.businessinsider.com
- 21. Telematics 2019-06-23 21 https://www.cmtelematics.com/drivewell/ • Monitor vehiclesand offer predictive maintenance capabilities. • Provide real-time information about expected arrival times. • Monitor driver behaviour and offer recommendations to reduce fuel consumption and improve safety.
- 22. Other applications • Freightapplications: increased delivery speeds, lower shipping price, easy returns, increased utilization of carriers, enhanced visibility of shipments. • Transit applications: Increased fleet utilization, better scheduling mechanisms, better incident management, autonomous buses • … 2019-06-23 22
- 23. Automate sharing dataacross different systems • Representation of complex relationships among cyber and physical components in integrated smart mobility applications using a machine understandable language 2019-06-23 23
- 24. Things to considerwhen implementing AI solutions. A major concern that should be addressed in your solution. Data Privacy Integrate security into the software development life cycle Cybersecurity Who owns the data if multiple parties are involved Data Ownership World-wide adoption of regulations and standards facilitates cross-border connectivity and collaboration Regulations and standards 2019-06-23 24
- 25.
Editor's Notes
- #4 London terror attack: Uber slammed for being slow to turn off ‘surge pricing’ after rampage https://money.cnn.com/2017/06/04/technology/uber-london-attack-surge-pricing/index.html
- #6 Perception: a pilot would perceive elements such as aircraft, mountains, or warning lights along with their relevant characteristics (color, speed, size, and location)