报告题目：Planning, Evaluating and Optimising On-Demand Transport: Spatial Analytics, Performance Evaluation and Agent-Based Modelling
报告人：Chinh Ho, Assoc. Prof. 悉尼大学商学院
邀请人：刘锴 教授
报告时间及地点：2026年7月16日 9：00-11：00 经济管理学院 B312
报告人简介：
Chinh Ho is an Associate Professor in Spatial Logistics and GIS with expertise in spatial analytics and demand modelling using Big Data. He has a research track record with 60+ journal articles (95% in Q1), one book, 15 book chapters, and many fully refereed conference papers (four winning the best paper award) in the areas of logistics and transport, statistical modelling, and big data. Chinh has an immense interest in intelligent mobility and has led a few studies, including the Sydney MaaS Trial, investigating the use digital technologies for improving customer experience and greening the transport and logistic sector. At USYD, Chinh leads the development of two strategic travel demand modelling systems, known as MetroScan, a fully integrated transport and land use modelling suite for Greater Sydney, and NSW Regional Transport Model. He also takes part in the review and on-going development of Transport for New South Wales authoritative models including Strategic Travel Model, Activity-Based Model, and Freight Movement Model, and other projects exploring the potential of emerging technologies for improving survey practice, sustainability and the mobility landscape.
报告摘要：
This course examines contemporary methodologies for planning, evaluating, and optimizing on-demand transport (ODT) as an integral component of sustainable public transport systems. Recognising that many ODT services have struggled to achieve long-term success, the course presents evidence-based approaches that support more effective deployment, evaluation, and operational design across diverse geographical and service contexts.
The course begins by introducing the SPOT (Suitable Placement for On-demand Transport) methodology, a GIS-based planning framework developed to identify locations where ODT can most effectively complement conventional public transport. Students will examine how spatial analytics, transport accessibility, travel demand, network integration, and social equity considerations can be combined to determine optimal service areas, operational hubs, and opportunities for restructuring underperforming local bus services.
Students will then explore recent advances in evaluating ODT performance beyond traditional measures such as patronage and operating cost. The course introduces a context-dependent performance evaluation framework that recognises the diverse service intentions of ODT systems, incorporating Data Envelopment Analysis (DEA), meta-frontier efficiency analysis, and policy-oriented performance indicators to provide a more comprehensive assessment of service effectiveness. Using a large-scale study of U.S. demand-responsive transport services, students will examine how operational design, local demographic characteristics, and service intentions influence performance outcomes.
Finally, the course considers how agent-based modelling can support the design of future ODT systems by simulating traveller behaviour, operational strategies, and service configurations under varying demand and geographical conditions. Students will examine how simulation-based approaches can identify suitable operating models, evaluate policy scenarios, and support evidence-based decision making for the implementation of flexible public transport services. Together, the methodologies presented in this course illustrate how spatial analytics, operational research, behavioural modelling, and transport planning can be integrated to improve the effectiveness, efficiency, and long-term sustainability of on-demand transport.
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