Mobility Data-Driven Urban Traffic Monitoring

This book introduces the concepts of mobility data and data-driven urban traffic monitoring, presenting three novel approaches: compressive sensing-based, dynamical non-linear traffic correlation modelling-based, and crowdsensing-based. It aims at researchers, computer scientists, and engineers interested in intelligent transportation systems, urban computing, big data analytics, and IoT.

Format: Paperback / softback
Length: 69 pages
Publication date: 19 May 2021
Publisher: Springer Verlag, Singapore

This comprehensive book delves into the realm of mobility data and data-driven urban traffic monitoring, offering a comprehensive framework for understanding and managing traffic in urban areas. It begins by introducing the fundamental concepts of mobility data and its significance in understanding urban traffic patterns. The book then presents a typical framework for mobility data-based urban traffic monitoring, outlining the processes involved in collecting, processing, modeling, and applying traffic models for monitoring urban traffic.

In order to address the challenge of mobility data sparsity, the authors propose a novel approach using compressive sensing. This approach leverages the traffic correlation at the road network scale and employs the compressive sensing theory to recover traffic conditions from sparse traffic samplings. This innovative solution enables efficient monitoring of urban traffic without the need for extensive data collection.

Furthermore, the book compares the performance of linear and nonlinear traffic correlation models and introduces a dynamical non-linear traffic correlation modeling-based urban traffic monitoring approach. By adapting the traffic modeling and estimation tasks to Apache Spark, a popular parallel computing framework, the authors overcome the computational overheads associated with traditional methods. This approach ensures scalability and efficiency in monitoring large-scale urban traffic.

In addition to traditional mobility data collected from public transit systems, the book proposes a crowdsensing-based urban traffic monitoring approach. By leveraging lightweight mobility data collected from participatory bus riders, the authors develop a method for recovering traffic statuses through careful data processing and analysis. This approach complements traditional data sources and provides a more comprehensive view of urban traffic dynamics.

The book concludes by highlighting some future research directions that can further enhance the accuracy and efficiency of mobility data-driven urban traffic monitoring. It emphasizes the importance of developing advanced algorithms, integrating multiple data sources, and addressing the challenges of real-time monitoring and decision-making in urban traffic management.

Overall, this book serves as a valuable resource for researchers, computer scientists, and engineers interested in advancing the field of mobility data-driven urban traffic monitoring. It provides a comprehensive understanding of the current state-of-the-art techniques and offers practical insights into developing effective and efficient solutions for managing urban traffic.

Weight: 454g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811622403
Edition number: 1st ed. 2021