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AICMTS 2020 : Advances in Artificial Intelligence and Computational Methods for Transportation Safety

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Link: https://easychair.org/conferences/request_view.cgi?new=1;request=97117#
 
When Mar 27, 2020 - Aug 15, 2020
Where Greater Noida
Abstract Registration Due Apr 10, 2020
Submission Deadline Apr 25, 2020
Notification Due May 15, 2020
Final Version Due Jul 25, 2020
 

Call For Papers

Transportation issues become a challenge when the framework and users' conduct is too hard to even think about modeling and foresee the movement designs. Transportation safety execution is connected to a variety of components, including roadway configuration, transit regulation requirement, road user conduct, and emergency reaction time. In this way, compelling transportation security warrants a multidisciplinary approach. Transportation safety is a necessary factor in the planning procedure and transportation organizers are key accomplices guaranteeing that security is a basic segment of all planning forms. With information and comprehension of security and security planning, transportation organizers can enhance joint effort, correspondence, and coordination with their security professional accomplices to accomplish the objective of decreasing genuine injuries and fatalities possible in any mode of transportation.
Transportation frameworks are naturally perplexing frameworks including an exceptionally enormous number of segments and various user parties, each having different and regularly clashing targets. The fast pace of improvements in Artificial Intelligence (AI) is giving exceptional chances to upgrade the presentation of various enterprises and organizations, including the transport segment. The developments presented by AI incorporate profoundly progressed computational strategies that imitate the manner in which the human mind works. The utilization of AI in the transport field is planned for conquering the difficulties of an expanding travel request, CO2 emanations, safety concerns, and ecological corruption. Transportation safety and mobility upgrade efficiency using propelled communication advances. It envelops an expansive scope of remote and wireline communication-based data and electrical innovations, when coordinated into the transportation framework's foundation, and in vehicles themselves.
Considering the accessibility of an immense measure of quantitative and subjective information and AI right now, is tending to these worries in a progressively proficient and powerful manner has gotten increasingly conceivable. This book brings in the best of artificial intelligence techniques that are being and can be implemented in the field of transportation engineering to enhance safety, aiming to acquaint the readers, students, teachers with the subject.

Chapter 1 An Overview of Transportation Safety

1.1 Introduction
1.2 What makes Transportation Safety Necessary in India
1.3 Overview of Road Safety Norms & Performance
1.4 Overview of Marine Transportation Safety Norms & Performance
1.5 Safety Management in Air Transport
1.6 Safety Management in Railway Transport
1.7 Maintaining the Transportation Infrastructures and Systems
1.8 Summary

Chapter 2 Description of Artificial Intelligence and Transportation

2.1 Introducing Artificial Intelligence
2.2 Need of Safe Transportation Engineering
2.3 Advances in Transportation Engineering
2.4 Role of Artificial Intelligence in Transportation Engineering
2.5 Embracing AI models in Transportation Systems
2.6 Applications of AI in Transportation Safety
2.7 Overview of Computational Intelligence – How is it Different
2.8 Summary

Chapter 3 Road Safety: Analyzing Road Transportation

3.1 Current Scenario of Road Safety
3.2 Evaluating Associated Risks of Road users and Pedestrian Fatalities
3.3 Addressing Road Traffic Injuries
3.4 Application of AI for Vulnerable users
3.5 AI-based Architecture for Safe System Approach
3.7 Application of AI in Interactive Cross Walks
3.8 Application of AI in Smart Vehicle Design
3.9 Conclusion

Chapter 4 Analytics of Railway Transportation Safety

4.1 Introduction to Railway Safety
4.2 Evaluating Associated Risks in Rail Transportation
4.3 Application of AI in Rail Transportation
4.4 AI Techniques for Enhancing Rail Safety
4.5 Smart Railway Maintenance with Big data
4.6 Scope of AI in Indian Rail Transportation Safety
4.7 Case Studies
4.8 Conclusion


Chapter 5 Analyzing AI in Marine Transportation Safety

5.1 An Overview of Marine Transportation Safety
5.2 Analyzing Marine Transportation Risks
5.3 Scope of AI in Marine Transportation
5.4 AI techniques for Safety Management
5.5 Maritime Safety Management using Big Data
5.6 The Rolls-Royce –Intel, Case Study
5.7 The OOCL, Hong Kong Case Study
5.8 Conclusion

Chapter 6 Analytics of AI in Air Safety

6.1 Introduction
6.2 Problems and Challenges in Aviation Safety
6.3 Role of AI in the Aviation Industry
6.4 AI for Safe Landing
6.5 AI for Weather Forecasting
6.6 Air Traffic Management Through AI
6.7 AI & Computational Intelligence as the Co-Pilot
6.8 Big Data for Aviation Industry
6.9 Case Study
6.10 Conclusion

Chapter 7 IoT for Transportation Safety

7.1 Introduction to IoT
7.2 Application of IoT in Transportation Safety
7.3 IoT in Road Safety & Traffic
7.4 IoT for Railway Safety
7.5 IoT for Marine Safety
7.6 IoT for Air Safety
7.7 Challenges and Limitations
7.8 Summary

Chapter 8 Machine Learning for Transportation Safety

8.1 Overview of Machine Learning for transportation safety
8.2 Learning framework
8.2.1 Supervised learning
8.2.2 Unsupervised learning
8.2.3 Reinforcement learning
8.3 Machine learning in transportation
8.3.1 Autonomous vehicles
8.3.2 Traffic Surveillance
8.3.3 Multi-object tracking
8.3.4 Activity recognition
8.3.5 Collision Prediction
8.3.6 Traffic Optimization
8.3.7 Traffic Sensors
8.3.8 Traffic flow prediction
8.4 Limitations of ML approaches
8.5 Security and Privacy issues

Chapter 9 Deep Learning for Transportation

9.1 Introduction to deep learning
9.2 Technical description of deep learning for the transportation system
9.3 Neural network architectures
9.4 Deep learning in transportation
9.4.1. Destination prediction
9.4.2 Demand prediction
9.4.3 Traffic flow prediction
9.4.4 Travel time estimation
9.4.5 Predicting traffic accident severity
9.4.6 Predicting the mode of transportation
9.4.7Trajectory clustering
9.4.8 Traffic signal control
9.5 Safety transportation using deep learning approaches

Chapter 10 Evaluating the Scope

10.1 An overview of Progress of AI in Transportation Safety
10.2 Opportunities and Benefits of AI & CI
10.3 Problems and Challenges of AI
10.4 Cost & Benefit Analysis of AI in Transportation Safety
10.5 Conclusion

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