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 1Hussein DiaPART I SHORT-TERM TRAFFIC FORECASTING AND CONGESTION PREDICTION1 A comparative evaluation of established and contemporary deep learning traffic prediction methods 14Ta Jiun Ting, Scott Sanner, and Baher Abdulhai2 Fault tolerance and transferability of short-term traffic forecasting hybrid AI models 47Rusul Abduljabbar, Hussein Dia, and Pei-Wei Tsai3 A review of deep learning-based approaches and use cases for traffic prediction 80Rezaur Rahman, Jiechao Zhang, and Samiul Hasan4 The ensemble learning process for short-term prediction of traffic state on rural roads 102Arash Rasaizadi, Fateme Hafizi, and Seyedehsan Seyedabrishami5 Using machine learning and deep learning for traffic congestion prediction: a review 124Adriana-Simona Mihaita, Zhulin Li, Harshpreet Singh, Nabin Sharma, Mao Tuo, and Yuming OuPART II PUBLIC TRANSPORT PLANNING AND OPERATIONS6 The potential of explainable deep learning for public transport planning 155Wenzhe Sun, Jan-Dirk Schmöcker, Youxi Lai, and Koji Fukuda7 Neural network approaches for forecasting short-term on-road public transport passenger demands 176Sohani Liyanage, Hussein Dia, Rusul Abduljabbar, and Pei-Wei TsaiPART III RAILWAYS8 Artificial intelligence in railway traffic planning and management Taxonomy, a systematic review of the state-of-the-art of AI, and transferability analysis 222Ruifan Tang, Zhiyuan Lin, Ronghui Liu, Rob M.P. Goverde, and Nikola Bešinović9 Artificial intelligence in railways: current applications, challenges, and ongoing research 249Lorenzo 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 VittoriniPART IV FREIGHT AND AVIATION10 Artificial intelligence and machine learning applications in freight transport 285Yijie Su, Hadi Ghaderi, and Hussein Dia11 A paradigm shift in the aviation industry with digital twin, blockchain, and AI technologies 323Tommy Cheung, Bo Li, and Zheng LeiPART V VIDEO ANALYTICS AND MACHINE VISION APPLICATIONS12 A deep learning approach to real-time video analytics for people and passenger counting 348Chris McCarthy, Hadi Ghaderi, Prem Prakash Jayaraman, and Hussein Dia13 AI machine vision for safety and mobility: an autonomous vehicle perspective 380Sagar Dasgupta, Xishi Zhu, Muhammad Sami Irfan, Mizanur Rahman, Jiaqi Gong, and Steven JonesPART VI DATA ANALYTICS AND PATTERN ANALYSIS14 A review of AI-enabled and model-based methodologies for travel demand estimation in urban transport networks 411Sajjad Shafiei and Hussein Dia15 Recombination-based two-stage out-of-distribution detection method for traffic flow pattern analysis 434Yuchen Lu, Ying Jin, and Xi Chen16 An intelligent machine learning alerting system for distracted pedestrians 465M.L. Cummings, Lixiao Huang, and Michael ClamannPART VII PREDICTIVE TRAFFIC SIGNAL CONTROL17 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 482Xiaoyu Wang, Baher Abdulhai, and Scott SannerPART VIII AI ETHICS AND CYBERSECURITY CHALLENGES18 A review of AI ethical and moral considerations in road transport and vehicle automation 534Dorsa Alipour and Hussein Dia19 Cybersecurity challenges in AI-enabled smart transportation systems 567Lyuyi Zhu, Ao Qu, and Wei Ma20 Autonomous driving: present and emerging trends of technology, ethics, and law 596Gustav Lindberg, Ikeya Carrero, Fermín Mallor, Julián Estévez, Manuela Battaglini, and Ricardo VinuesaIndex 617

‘Under the astute editorship of Hussein Dia, the Handbook on Artificial Intelligence and Transport deftly elucidates a panoply of AI advancements across a myriad of transportation spheres. An indispensable tome for both academia and industry, it propels the transportation field towards a future replete with innovation and sagacity.’