Harnessing Multi-Source Heterogeneous Data for Public Transit
Problem Diagnosis and Stochastic Optimization
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Beschreibung
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.
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.
Advanced Utilization of Multi-Source Heterogeneous Transportation Big Data Comprehensive Framework for Systematic Diagnosis and Optimization of Transit Systems Stochastic Optimization for Public Transit System
Autor*in
Shaopeng Zhong
Themen in »Harnessing Multi-Source Heterogeneous Data for Public Transit«
Public transit planning Multi-Source heterogeneous data Transportation big data Bayesian network Multi-objective optimization model Cluster analysis Pattern analysis Transit Assignment Data mining Problem diagnosis