Handbook on Artificial Intelligence and Transport

Name: Handbook on Artificial Intelligence and Transport
Brand: Edward Elgar Publishing
SKU: 9781803929538
Price: 3177.72 HKD
Availability: InStock
Author: Hussein Dia
ISBN: 9781803929538

By Hussein Dia

Edward Elgar Publishing October 2023

Specifications

ISBN-13: 9781803929538
Publisher: Edward Elgar Publishing
Publication: October 2023
Format: Hardback
Jurisdiction: U.K. ? Countri(es) for reference only

Details

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.

Introduction to the Handbook on Artificial Intelligence and Transport 1; Hussein Dia; PART I. SHORT-TERM TRAFFIC FORECASTING AND CONGESTION PREDICTION; 1.
A comparative evaluation of established and contemporary deep learning traffic prediction methods 14: Ta Jiun Ting, Scott Sanner, and Baher Abdulhai; 2.
Fault tolerance and transferability of short-term traffic forecasting hybrid AI models 47: Rusul Abduljabbar, Hussein Dia, and Pei-Wei Tsai; 3.
A review of deep learning-based approaches and use cases for traffic prediction 80: Rezaur Rahman, Jiechao Zhang, and Samiul Hasan; 4.
The ensemble learning process for short-term prediction of traffic state on rural roads 102: Arash Rasaizadi, Fateme Hafizi, and Seyedehsan Seyedabrishami; 5.
Using machine learning and deep learning for traffic congestion prediction: a review 124: 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 155: Wenzhe Sun, Jan-Dirk Schmöcker, Youxi Lai, and Koji Fukuda; 7.
Neural network approaches for forecasting short-term on-road public transport passenger demands 176: Sohani Liyanage, Hussein Dia, Rusul Abduljhabbar, 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 222: Ruifan Tang, Zhiyuan Lin, Ronghui Liu, Rob M.P. Goverde, and Nikola Bešinović; 9.
Artificial intelligence in railways: current applications, challenges, and ongoing research 249: Lorenzo De Donato, Stefano Marrone, Elena Napoletano, Stefania Santini, Valeria Vittorini, Francesco Flammini, Rob M.P. Goverde, Nikola Bes̆inović, Ruifan Tang, Zhiyuan Lin, Ronghui Liu, and Roberto Nardone; PART IV. FREIGHT AND AVIATION; 10.
Artificial intelligence and machine learning applications in freight transport 285: Yijie Su, Hadi Ghaderi, and Hussein Dia; 11.
A paradigm shift in the aviation industry with digital twin, blockchain, and AI technologies 323: 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 348: Chris McCarthy, Hadi Ghaderi, Prem Jayaraman, and Hussein Dia; 13.
AI machine vision for safety and mobility: an autonomous vehicle perspective 380: 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 411: Sajjad Shafiei and Hussein Dia; 15.
Recombination-based two-stage out-of-distribution detection method for traffic flow pattern analysis 434: Yuchen Lu, Xi Chen, and Ying Jin; 16.
An intelligent machine learning alerting system for distracted pedestrians 465: 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 482: 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 534: Dorsa Alipour and Hussein Dia; 19.
Cybersecurity challenges in AI-enabled smart transportation systems 567: Lyuyi Zhu, Ao Qu, and Wei Ma; 20.
Autonomous driving: present and trends of technology, ethics, and law 596: Gustav Lindberg, Ikeya Carrero, Fermín Mallor, Julián Estévez, Manuela Battaglini, and Ricardo Vinuesa; Index 617