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

Contents:

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 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 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, 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 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 Prakash 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, Ying Jin, and Xi Chen
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 emerging trends of technology, ethics, and law 596
Gustav Lindberg, Ikeya Carrero, Fermín Mallor, Julián Estévez, Manuela Battaglini, and Ricardo Vinuesa

Index 617