Harnessing Multi-Source Heterogeneous Data for Public Transit: Problem Diagnosis and Stochastic Optimization (en Inglés) - Shaopeng Zhong

This book focuses on the innovative use of multi-source heterogeneous data to address key issues in public transit systems. Readers will explore advanced methodologies for data mining, problem diagnosis, mechanism analysis, and stochastic optimization tailored to the public transit context.

This book stands out by integrating the latest in data processing techniques, pattern recognition, and mechanism analysis, providing a comprehensive framework for systematic diagnosis and optimization of transit systems. By examining both microscopic and macroscopic perspectives, it offers insights into passenger behavior, system reliability, and personalized transit solutions.

The unique approach of this book lies in its holistic perspective, emphasizing the study of patterns, problem diagnosis, mechanism analysis, and model construction through the use of multi-source heterogeneous public transit data. It bridges the gap between multi-source data analysis, Bayesian network technology, and synergistic optimization models, serving as a crucial resource for researchers, practitioners, and policymakers who aim to enhance the efficiency, reliability, and passenger satisfaction of public transit systems.