Data Driven Solutions To Transportation Problems
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Data-Driven Solutions to Transportation Problems
Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more. - Synthesizes the newest developments in data-driven transportation science - Includes case studies and examples in each chapter that illustrate the application of methodologies and technologies employed - Useful for both theoretical and technically-oriented researchers
Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making
This book comprehensively discusses nature‐inspired algorithms, deep learning methods, applications of mathematical programming, and artificial intelligence techniques. It further covers important topics such as the use of machine learning and the Internet of Things and multi‐objective optimization under Fermatean hesitant fuzzy and uncertain environment. This book: Addresses solving practical problems such as supply chain management, smart manufacturing, and healthcare analytics using intelligent computing and discusses solving the fuzzy inference system in ant colony optimization for traveling salesman problem Presents an overview of artificial intelligence (AI) and explainable AI decision‐making (XAIDM) and illustrates a data‐driven optimization concept for modeling environmental and economic sustainability Discusses machine learning‐based multi‐objective optimization technique for load balancing in integrated fog‐cloud environment Explains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goals Discusses sustainable transit of hazardous waste, green fractional transportation system, perishable inventory, M‐estimation of functional regression operator, and intuitionistic fuzzy sets applications The text is primarily written for graduate students and academic researchers in diverse fields, including operations research, mathematics, statistics, computer science, information and communication technology, and industrial engineering.
Sustainable Green Conversion
Author: Numan M. Durakbasa
language: en
Publisher: Springer Nature
Release Date: 2025-05-02
This book contains the second volume of selected papers from International Symposium for Production Research 2024, held on October 10–12, 2024, in Budva, Montenegro. The book reports recent advances in production engineering and operations. This year's conference had the overarching theme of "Sustainable Green Conversion." The book explores topics including: Simulation and Modelling, Supply Chain and Logistics Management, Sustainability and Capstone Projects. Presenting real-life applications, case studies, and mathematical models, this book is of interest to researchers, academics, and practitioners in the field of production and operation engineering. It provides both the results of recent research and practical solutions to real-world problems.