New Book: Harnessing Multi-Source Heterogeneous Data for Public Transit
📖 A New Monograph on Data-Driven Public Transit
I am pleased to announce the publication of our new research monograph:
Harnessing Multi-Source Heterogeneous Data for Public Transit: Problem Diagnosis and Stochastic Optimization by Shaopeng Zhong and Yu Jiang Springer Singapore · International Series in Operations Research & Management Science
This book represents the culmination of years of research into how multi-source heterogeneous data can be systematically harnessed to address key challenges in public transit systems — from diagnosing operational problems to developing robust stochastic optimisation solutions.
What This Book Covers
Public transit systems generate vast quantities of data from diverse sources: smart card transactions, GPS traces, passenger surveys, operational logs, and more. Yet integrating these heterogeneous data streams into actionable planning tools remains a formidable challenge. This monograph provides a rigorous framework for doing exactly that.
Key Topics
- 🔍 Problem Diagnosis: Bayesian network methods for identifying root causes of service disruptions and performance degradation in transit networks.
- 📊 Multi-Objective Optimisation: Stochastic optimisation models that balance competing objectives — service quality, operational cost, passenger satisfaction — under real-world uncertainty.
- 🗂️ Cluster & Pattern Analysis: Advanced techniques for mining spatial-temporal patterns from multi-source transit data, revealing hidden structures in passenger demand and network usage.
- 🚌 Transit Assignment: State-of-the-art assignment models that account for heterogeneous passenger behaviour and multi-modal network interactions.
- 🧠 Data Mining for Planning: Practical methodologies for transforming raw, noisy, multi-source data into structured inputs for transit planning and optimisation.
Bibliographic Details
| Detail | Value |
|---|---|
| Title | Harnessing Multi-Source Heterogeneous Data for Public Transit |
| Subtitle | Problem Diagnosis and Stochastic Optimization |
| Authors | Shaopeng Zhong, Yu Jiang |
| Series | International Series in Operations Research & Management Science |
| Publisher | Springer Singapore |
| Pages | XI, 272 |
| Hardcover ISBN | 978-981-92-3097-6 |
| eBook ISBN | 978-981-92-3098-3 |
| Copyright | 2026 Springer Nature Singapore |
Where to Get It
👉 View on Springer Nature Link
This monograph is written for researchers, graduate students, and practitioners working at the intersection of operations research, transportation engineering, and data science. I hope it serves as a useful resource for advancing the next generation of data-driven public transit systems.
Frequently Asked Questions
What is the book Harnessing Multi-Source Heterogeneous Data for Public Transit about?
This Springer monograph presents innovative methods for diagnosing public transit problems and developing stochastic optimisation solutions using multi-source heterogeneous data. It covers Bayesian network diagnostics, multi-objective optimisation, cluster and pattern analysis, transit assignment models, and data mining techniques for transit planning. Published in the International Series in Operations Research & Management Science.
Who are the authors?
The book is authored by Professor Shaopeng Zhong (School of Economics and Management, Dalian University of Technology) and Dr. Yu Jiang (Associate Professor at Lancaster University and the Technical University of Denmark). Dr. Jiang is recognised as a Stanford/Elsevier Top 2% Scientist, with research supported by the European Union, the Independent Research Fund Denmark, the Otto Mønsted Foundation, the Royal Society, and the EuroTech Alliance.
Who should read this book?
The monograph is designed for researchers, graduate students, and practitioners working at the intersection of operations research, transportation engineering, and data science — particularly those interested in applying multi-source data analytics and stochastic optimisation to public transit systems.
What methodologies does the book cover?
Key methodologies include Bayesian networks for problem diagnosis, multi-objective stochastic optimisation models, cluster analysis and spatial-temporal pattern mining, transit assignment under heterogeneous passenger behaviour, and practical data mining pipelines for transforming raw multi-source data into actionable transit planning inputs.
Where can I buy it?
The book is available on Springer Nature Link (ISBN: 978-981-92-3097-6) and on Amazon UK.