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.