Handbook on Artificial Intelligence and Transport

✍ Scribed by Hussein Dia (editor)

• Publisher: Edward Elgar
• Year: 2023
• Tongue: English
• Leaves: 649
• Series: Research Handbooks in Transport Studies
• Category: Library

✦ Synopsis

With AI advancements eliciting imminent changes to our transport systems, this enlightening Handbook presents essential research on this evolution of the transportation sector. It focuses on not only urban planning, but relevant themes in law and ethics to form a unified resource on the practicality of AI use.

The Handbook on Artificial Intelligence and Transport provides a full investigation of the most recent AI transport developments, authored by an international collective of renowned contributors. Chapters examine several often challenging topics such as autonomous driving and cyber security ethics. They conclude that AI technology is likely to offer resolutions to persistent transport issues that have been almost impossible to solve using conventional approaches.

This timely Handbook will be an important resource for students of transport planning and engineering, innovation and regional law. It will also benefit practitioners within the sectors of urban planning and engineering seeking updated evidence on the role of AI in transport improvement.

✦ Table of Contents

Front Matter
Copyright
Contents
Contributors
Introduction to the Handbook on Artificial Intelligence and Transport
Part I Short-term traffic forecasting and congestion prediction
1. A comparative evaluation of established and contemporary deep learning traffic prediction methods
2. Fault tolerance and transferability of short-term traffic forecasting hybrid AI models
3. A review of deep learning-based approaches and use cases for traffic prediction
4. The ensemble learning process for short-term prediction of traffic state on rural roads
5. Using machine learning and deep learning for traffic congestion prediction: a review
Part II Public transport planning and operations
6. The potential of explainable deep learning for public transport planning
7. Neural network approaches for forecasting short-term on-road public transport passenger demands
Part III Railways
8. Artificial intelligence in railway traffic planning and management Taxonomy, a systematic review of the state-of-the-art of AI, and transferability analysis
9. Artificial intelligence in railways: current applications, challenges, and ongoing research
Part IV Freight and aviation
10. Artificial intelligence and machine learning applications in freight transport
11. A paradigm shift in the aviation industry with digital twin, blockchain, and AI technologies
Part V Video analytics and machine vision applications
12. A deep learning approach to real-time video analytics for people and passenger counting
13. AI machine vision for safety and mobility: an autonomous vehicle perspective
Part VI Data analytics and pattern analysis
14. A review of AI-enabled and model-based methodologies for travel demand estimation in urban transport networks
15. Recombination-based two-stage out-of-distribution detection method for traffic flow pattern analysis
16. An intelligent machine learning alerting system for distracted pedestrians
Part VII Predictive traffic signal control
17. A critical review of traffic signal control and a novel unified view of reinforcement learning and model predictive control approaches for adaptive traffic signal control
Part VIII AI ethics and cybersecurity challenges
18. A review of AI ethical and moral considerations in road transport and vehicle automation
19. Cybersecurity challenges in AI-enabled smart transportation systems
20. Autonomous driving: present and emerging trends of technology, ethics, and law
Index