Handbook on artificial intelligence and transport / edited by Hussein Dia (Professor of Future Urban Mobility, Department of Civil and Construction Engineering, Swinburne University of Technology, Australia).

Format:

Book

Contributor:

Dia, Hussein, editor.

Edward Elgar Publishing, publisher.

Series:

Research handbooks in transport studies

Research handbooks in transport studies series

Language:

English

Subjects (All):

Artificial intelligence--Handbooks, manuals, etc.

Artificial intelligence.

Intelligent transportation systems.

Transportation--Technological innovations.

Transportation.

Genre:

Electronic books.

Physical Description:

1 online resource (648 pages)

Place of Publication:

Northampton : Edward Elgar Publishing, 2023.

Summary:

"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"-- Provided by publisher.

Contents:

Contents: Introduction to the handbook on artificial intelligence and transport / Hussein Dia

Part I. Short-term traffic forecasting and congestion prediction

1. A comparative evaluation of established and contemporary deep learning traffic prediction methods / Ta Jiun Ting, Scott Sanner, and Baher Abdulhai

2. Fault tolerance and transferability of short-term traffic forecasting hybrid AI models / Rusul Abduljabbar, Hussein Dia, and Pei-Wei Tsai

3. A review of deep learning-based approaches and use cases for traffic prediction / Rezaur Rahman, Jiechao Zhang, and Samiul Hasan

4. The ensemble learning process for short-term prediction of traffic state on rural roads / Arash Rasaizadi, Fateme Hafizi, and Seyedehsan Seyedabrishami

5. Using machine learning and deep learning for traffic congestion prediction: A review / Adriana-Simona Mihaita, Zhulin Li, Harshpreet Singh, Nabin Sharma, Mao Tuo, and Yuming Ou

Part II. Public transport planning and operations

6. The potential of explainable deep learning for public transport planning / Wenzhe Sun, Jan-Dirk Schmöcker, Youxi Lai, and Koji Fukuda

7. Neural network approaches for forecasting short-term on-road public transport passenger demands / Sohani Liyanage, Hussein Dia, Rusul Abduljabbar, and Pei-Wei Tsai

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 / Ruifan Tang, Zhiyuan Lin, Ronghui Liu, Rob M.P. Goverde, and Nikola Besinović

9. Artificial intelligence in railways: Current applications, challenges, and ongoing research / Lorenzo De Donato, Ruifan Tang, Nikola Bes̆inović, Francesco Flammini, Rob M.P. Goverde, Zhiyuan Lin, Ronghui Liu, Stefano Marrone, Elena Napoletano, Roberto Nardone, Stefania Santini, Valeria Vittorini

Part IV. Freight and aviation

10. Artificial intelligence and machine learning applications in freight transport / Yijie Su, Hadi Ghaderi, and Hussein Dia

11. A paradigm shift in the aviation industry with digital twin, blockchain, and AI technologies / Tommy Cheung, Bo Li, and Zheng Lei

Part V. Video analytics and machine vision applications

12. A deep learning approach to real-time video analytics for people and passenger counting / Chris McCarthy, Hadi Ghaderi, Prem Prakash Jayaraman, and Hussein Dia

13. AI machine vision for safety and mobility: An autonomous vehicle perspective / Sagar Dasgupta, Xishi Zhu, Muhammad Sami Irfan, Mizanur Rahman, Jiaqi Gong, and Steven Jones

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 / Sajjad Shafiei and Hussein Dia

15. Recombination-based two-stage out-of-distribution detection method for traffic flow pattern analysis / Yuchen Lu, Ying Jin, and Xi Chen

16. An intelligent machine learning alerting system for distracted pedestrians / M.L. Cummings, Lixiao Huang, and Michael Clamann

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 / Xiaoyu Wang, Baher Abdulhai, and Scott Sanner

Part VIII. AI ethics and cybersecurity challenges

18. A review of AI ethical and moral considerations in road transport and vehicle automation / Dorsa Alipour and Hussein Dia

19. Cybersecurity challenges in AI-enabled smart transportation systems / Lyuyi Zhu, Ao Qu, and Wei Ma

20. Autonomous driving: Present and emerging trends of technology, ethics, and law / Gustav Lindberg, Ikeya Carrero, Fermín Mallor, Julián Estévez, Manuela Battaglini, and Ricardo Vinuesa

Index.

Notes:

Includes bibliographical references and index.

ISBN:

9781803929545 (e-book)

Access Restriction:

Restricted for use by site license.