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Predictive Analytics for Infrastructure Maintenance and Traffic Modeling
ISBN Book

Predictive Analytics for Infrastructure Maintenance and Traffic Modeling

Author:
Sarath Babu Gosipathala

ISBN: 978-93-49929-15-9

DOI: https://doi.org/10.63282/978-93-49929-15-9


Predictive Analytics for Infrastructure Maintenance and Traffic Modeling examines how predictive modeling can transform infrastructure management from a reactive discipline into an anticipatory one — moving beyond periodic inspection and historical averages toward systems capable of forecasting both structural degradation and traffic behavior before they become costly problems.I nfrastructure failure and traffic congestion have traditionally been managed after the fact: repairs follow visible damage, and traffic interventions follow observed gridlock. This book argues that the same underlying capability — the ability to model how infrastructure systems age, strain, and respond to demand over time — can be applied to both problems simultaneously, using sensor data, historical maintenance and usage records, and modern machine learning techniques. The book is written for researchers, civil and transportation engineers, and infrastructure and technology decision-makers seeking to apply predictive analytics to real-world infrastructure and traffic challenges. It is grounded throughout in practical deployability rather than purely theoretical modeling, reflecting the realities of working with imperfect, real-world data. The book's central premise is that infrastructure maintenance and traffic modeling, though often treated as separate disciplines, share a common analytical foundation: understanding how systems degrade and respond to demand over time. Organizations that build predictive capability into their infrastructure and traffic management practices — rather than continuing to respond only after failure or congestion occurs — will be better positioned to allocate resources efficiently, reduce risk, and maintain public trust in the systems they operate.


Sarath Babu Gosipathala is an Independent Researcher based in the USA with over 21 years of experience in project and product development, specializing in architecting, designing, developing, and performance engineering enterprise applications built on Microsoft technologies. Sarath Babu received his Master’s degree (2001) in Computer Applications from the University of Hyderabad, India. His career reflects a rare combination of deep technical expertise and long-term domain specialization, having spent more than 14 years working within the Electronic Toll Collection domain, where he has helped shape systems that are critical to the everyday functioning of modern transportation infrastructure. His position oversees mission critical platforms that process millions of daily financial and vehicle-based transactions, ensuring accuracy, continuity, and compliance for major toll agency operations. His role requires deep expertise in high volume transaction processing, distributed systems, real time data workflows, and the unique regulatory and technical requirements of intelligent transportation systems.

Throughout his career, Sarath has handled technically challenging assignments across the full software development lifecycle, from initial design through implementation, bringing a disciplined, end-to-end perspective to every project he undertakes. His experience extends beyond pure engineering into the strategic and analytical dimensions of technology delivery, including working on Request for Proposals (RFP) and conducting gap analyses for Requirements Traceability Matrices (RTMs) work that demands both technical precision and a clear understanding of business and regulatory requirements. He has also served as a critical liaison between agencies, operations teams, and development teams, translating between stakeholders with very different priorities and vocabularies to ensure that complex systems are delivered successfully and function reliably in the field.

Sarath's technical foundation is built on a strong understanding of object-oriented concepts and a broad, modern technology skill set, including ASP.NET MVC, C#, .NET Core, Azure, Microservices Architecture, and Cloud Architecture, alongside front-end and data technologies such as Angular, MS-SQL Server, web services, and RESTful services. This combination of architectural depth and hands-on development experience has positioned him to lead complex, mission-critical systems from concept to production.

His extensive experience in the Electronic Toll Collection domain, paired with his broader expertise in Intelligent Transportation Systems and Enterprise Architecture, has given him a distinctive vantage point on the practical challenges of building AI-driven, real-time systems for transportation and infrastructure insight that directly informs his research and writing in this book.


Title: Predictive Analytics for Infrastructure Maintenance and Traffic Modeling
Author: Sarath Babu Gosipathala
Publisher: ScienceTech Xplore
Copyright: ScienceTech Xplore
Language: English
Publication Format: Online
ISBN: 978-93-49929-15-9
DOI: https://doi.org/10.63282/978-93-49929-15-9
Subject/Genre: AI Technology & Engineering
Pages: 1-219
Chapters: 15
Year of Publication: 2026
URL: https://sciencetechxplore.org/978-93-49929-15-9.php
Cite As:

Gosipathala, S. B. (2026). Predictive Analytics for Infrastructure Maintenance and Traffic Modeling. ScienceTech Xplore. https://doi.org/10.63282/978-93-49929-15-9

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