Accepting PhD Students

PhD projects

Sharing Economy: Platform Design and Optimization Multimodal Transportation or Mobility as a Service (On-demand Mobility + Public Transport) Big Data Analytics for Planning, Monitoring, Control and Information in Public Transport.

20112020

Research output per year

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Personal profile

Biography

Zhenliang (Mike) Ma is a Lecturer at the Institute of Transport Studies, Civil Engineering, Monash University, and affiliate with Southeast University (Top 1 in Transportation), China and Urban Mobility Lab at Massachusetts Institute of Technology (MIT), United States. Dr Ma has an interdisciplinary background in information technology, computer science, and transportation engineering.

Dr Ma completed his PhD at the University of Queensland, and was a Research Associate at the MIT Transit Lab. In this role, he co-managed and led the research partnership program with Mass Transit Railway Corporation Limited (MTRCL), Hong Kong. MTRCL operates the urban railway system in Hong Kong and also across different parts in the world, including London, Stockholm, Beijing, Hangzhou, Macau, Shenzhen, Melbourne, and Sydney.  He also collaborated on various projects funded by world-leading agencies, Transport for London, and MBTA, the Transit Authority in Boston.

Mike’s contributions on urban data analytics, public transport and shared mobility are published in prestigious transportation journals/conferences and patented by the National Intellectual Property Administration. The established models/methodologies/tools are industrialized in the fields of practitioners in urban railway systems, such as network state monitoring and prediction with opportunistic sensor data in MTR, Hong Kong.

Research interests

I have an interdisciplinary background in Transportation, Computer Science and Information Technology. My main expertise are: Big Data Analytics, Large-Scale Optimization, Public Transport, Demand Management, and Shared Mobility. My general area of research is at the intersection of optimisation, machine learning, and computer simulation. Under the umbrella of buzz words ‘big data’, ‘IoT’, and ‘sharing economy’, my research focuses on the inference (understand), prediction (inform) and optimisation (design), through the integration of novel data sources, mostly from connected devices or infrastructures, into mathematical learning models.

Accelerating the adoption of technological advances as the foundation for innovation is an important means to increase public transport appeal and hence, its use as the preferred mode will be directly related to reducing congestion in urban areas. Technological advances include mobile sensors (e.g., smart card, WiFi) that allow the collection of diverse data and direct customer communications. This data supports the development of customer-centric performance metrics, measures of equity and inclusion to inform policy, and information for better planning of operations and services. Technological advances also include the new on-demand services (e.g., Uber, DiDi) that currently impact ridership of public transport. However, they also offer opportunities for improving public transport accessibility via partnerships. In this context, leveraging on my interdisciplinary background (IT, Computer Science and Transportation), my research activities in this area center around transforming public transport using technology as a new foundation in operations, planning and control, and designing mobility-as-a-service platform for innovation in multimodal service delivery.

New vehicle technologies (e.g., connected, autonomous vehicles), emerging service models (e.g., on-demand shared mobility,) and innovative mobility concepts (e.g., mobility as a service) constitute the primary sources of ongoing and future changes in mobility landscape9. They offer the promise to improve flexibility and accessibility, however not the certainty of delivering better services, liveable and sustainable cities. The uncertainty rests not only on technologies but also on users (needs, ownership) and policies, as well as where the balance between new services and private car use may settle. In light of these requirements, my research vision aims at exploring opportunities and designing services to shape how the future mobility system together with travel demand management (TDM) and information provision could potentially deliver better and sustainable urban mobility solutions.

Monash teaching commitment

  • CIV 5406: Modelling Transportation Systems (Travel Demand Modeling and Forecast)
  • CIV 5319: Quantitative Methods (Engineering Probability and Statistics)
  • CIV 5322: Public Transport (Planning, Operations, Policy)
  • CIV 5320: Case Studies in Transportation Systems (Research Methods, Projects, Software)
  • ENG 5005: Research Methods (Projects supervison)

Supervision interests

  • Big Data Analytics and Large-Scale Optimization
  • Public Transport (Bus and Urban Rail)
  • Sharing Economy: Platform Strategy Design and Optimization
  • Multimodal Transportation (Mobility on Demand + Public Transport)

Education/Academic qualification

Transportation Engineering, Ph.D., University of Queensland

1 Aug 201225 Dec 2015

Award Date: 25 Dec 2015

Information Science, M.Sc., Shandong University

1 Sep 20091 Jun 2012

Award Date: 1 Jun 2012

Electrical Engineering, B.Sc., Shandong University

1 Sep 20051 Jun 2009

Award Date: 1 Jun 2009

External positions

Research Affiliate, Massachusetts Institute of Technology (MIT)

28 Feb 2019 → …

Teaching Affiliate, Southeast University

24 Feb 2019 → …

Research area keywords

  • Big Data Analytics
  • Machine Learning
  • Large-Scale Network Optimization
  • Shared Mobility-on-Demand
  • Urban Rail and Bus Systems
  • Travel Demand Management

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output

Revealing mobility regularities in urban rail systems

Jiang, W., Ma, Z., Kim, I. & Lee, S., 2020, Proceedings of the 11th International Conference on Ambient Systems, Networks and Technologies. Elsevier, Vol. 170. p. 219-226 8 p. (Procedia Computer Science).

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

Open Access
File

A strategy-based recursive path choice model for public transit smart card data

Nassir, N., Hickman, M. & Ma, Z. L., Aug 2019, In : Transportation Research Part B: Methodological. 126, p. 528-548 21 p.

Research output: Contribution to journalArticleResearchpeer-review

9 Citations (Scopus)

Estimation of denied boarding in urban rail systems: alternative formulations and comparative analysis

Ma, Z., Koutsopoulos, H. N., Chen, Y. & Wilson, N. H. M., Nov 2019, In : Transportation Research Record. 2673, 11, p. 771–778 8 p.

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Monitoring transit-served areas with smartcard data: a Brisbane case study

Zhou, J., Sipe, N., Ma, Z., Mateo-Babiano, D. & Darchen, S., 1 Apr 2019, In : Journal of Transport Geography. 76, p. 265-275 11 p.

Research output: Contribution to journalArticleResearchpeer-review

5 Citations (Scopus)

Optimal design of promotion based demand management strategies in urban rail systems

Ma, Z. & Koutsopoulos, H. N., Dec 2019, In : Transportation Research Part C: Emerging Technologies. 109, p. 155-173 19 p.

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)