Mobility Data Driven Urban Traffic Monitoring
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Mobility Data-Driven Urban Traffic Monitoring
This book introduces the concepts of mobility data and data-driven urban traffic monitoring. A typical framework of mobility data-based urban traffic monitoring is also presented, and it describes the processes of mobility data collection, data processing, traffic modelling, and some practical issues of applying the models for urban traffic monitoring. This book presents three novel mobility data-driven urban traffic monitoring approaches. First, to attack the challenge of mobility data sparsity, the authors propose a compressive sensing-based urban traffic monitoring approach. This solution mines the traffic correlation at the road network scale and exploits the compressive sensing theory to recover traffic conditions of the whole road network from sparse traffic samplings. Second, the authors have compared the traffic estimation performances between linear and nonlinear traffic correlation models and proposed a dynamical non-linear traffic correlation modelling-based urban traffic monitoring approach. To address the challenge of involved huge computation overheads, the approach adapts the traffic modelling and estimations tasks to Apache Spark, a popular parallel computing framework. Third, in addition to mobility data collected by the public transit systems, the authors present a crowdsensing-based urban traffic monitoring approach. The proposal exploits the lightweight mobility data collected from participatory bus riders to recover traffic statuses through careful data processing and analysis. Last but not the least, the book points out some future research directions, which can further improve the accuracy and efficiency of mobility data-driven urban traffic monitoring at large scale. This book targets researchers, computer scientists, and engineers, who are interested in the research areas of intelligent transportation systems (ITS), urban computing, big data analytic, and Internet of Things (IoT). Advanced level students studying these topics benefit from this book as well.
Mobility Patterns, Big Data and Transport Analytics
Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling, Second Edition provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizing, and controlling mobility patterns-a key aspect of transportation modeling. The book features prominent international experts who provide overviews on new analytical frameworks, applications, and concepts in mobility analysis and transportation systems. Fields covered are evolving rapidly, and this new edition updates existing material and provides new chapters that reflect recent developments in the field (such as the emergence of active, transfer and reinforcement learning). Users will find a detailed, mobility ‘structural’ analysis and a look at the extensive behavioral characteristics of transport, observability requirements, limitations for realistic transportation applications, and transportation systems analysis that are related to complex processes and phenomena. This book bridges the gap between big data, data science, and transportation systems analysis with a study of big data’s impact on mobility and an introduction to the tools necessary to apply new techniques.
Cognitive Computing for Smart Automotive Transportation
Author: G. R. Kanagachidambaresan
language: en
Publisher: CRC Press
Release Date: 2025-06-19
This reference book explores the integration of cognitive computing technologies in the automotive industry to enhance smart transportation systems. It focuses on how AI, machine learning, and data analytics can improve vehicle automation, safety, and efficiency. Automation can support driverless vehicle transportation and bridge the gap between manual control and fully automated navigation systems. The text introduces a discussion on numerous applications of cognitive computing in smart transportation, motion planning, situation awareness, dynamic driving, adaptive behavior, human intent measurement, and predictive analysis. Key Features: • Discusses basic concepts and architecture of cognitive computing for vehicular systems. • Presents technologies to measure human intent for vehicle safety, including emergency management systems (EMS). • Covers the perception and localization processes in autonomous driving through LiDAR, GPS, and Stereo vision data with critical decision‐making and simulation results. • Elucidates the application of motion planning for smart transportation. • Covers visual perception technologies for advanced driver assistance systems (ADAS) through deep learning. The text is primarily written for graduate students, academic researchers, and professionals in the fields of computer science, electrical engineering, automotive engineering, and civil engineering.