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Personal profile


Zhenliang (Mike) Ma is a Lecturer at the Institute of Transport Studies, Monash University, and jointly appointed with the SEU-Monash Graduate Program (Transportation) in China. Mike received Bachelor’s in Electrical Engineering and Master’s in Communication Engineering from Shandong University, China and Ph.D. in Transportation Engineering from the University of Queensland, Australia. Before joining Monash as a faculty staff, Mike was a Postdoc Research Associate at the US-DOT Beyond Traffic Innovation Center and Lecturer (part-time) at Northeastern University, Team-leader of the MIT-Mass Transit Railway (MTR), Hong Kong program at MIT Transit Lab (https://transitlab.mit.edu/), and Research Affiliate with the MIT Urban Mobility Lab (https://mobility.mit.edu/). 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 analytic and applications in transportation 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. He serves as the reviewer for Journals and Science Foundations, such as Nature-Scientific Data, Transportation Research Series, and Chile NSF. He is invited to contribute in writing research proposals to various agencies (US, Europe, and China), such as US NSF Engineering Research Center (NEU), US National Transportation Board (MIT), and Google (KTH, Sweden). 

Research interests

Mike’s general area of research is at the intersection of optimization, machine learning, and computer simulation. Under the umbrella of buzz words ‘big data’, ‘IoT’, and ‘sharing economy’, his research focuses on the inference (understand), prediction (inform) and optimization (design), through the integration of novel data sources, mostly from connected devices or infrastructures, into mathematical learning models. The applications include public transport (e.g. bus and urban railways) and shared mobility-on-demand services (e.g. Uber, Lyft, DiDi, micro-transit). The application topics cover service planning (e.g. network extension), operation control (e.g. probabilistic routing), behavioral modeling (e.g. habitual, information, disruption, and intervention), demand management (e.g. nudge behavior), and personalized travel (e.g. Chatbot).

Technology drives innovation, but smart and sustainability go beyond technology. Currently, Mike focuses on:

  • Revitalizing public transport through big data analytics – planning, monitoring, information, control and demand management;
  • Designing platform strategy and operation business models to engineering the sharing economy (in mobility) to attain predictable and socially desirable outcomes, and value creation for public and private sectors (NSF-ERC-SHARE, https://web.northeastern.edu/rcshare/).  

Monash teaching commitment

  • CIV 5406: Modelling Transportation Systems (Travel Demand Modeling and Forecast)
  • CIV 5319: Quantitative Methods (Engineering Probability and Statistics)

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

Information Science and Technology, M.Sc., Shandong University

1 Sep 20091 Jun 2012

Electrical Engineering, B.Sc., Shandong University

1 Sep 20051 Jun 2009

External positions

Research Affiliate, Massachusetts Institute of Technology (MIT)

28 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 2011 2019

4 Citations (Scopus)

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

1 Citation (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

5 Citations (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

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

Transit data analytics for planning, monitoring, control, and information

Koutsopoulos, H. N., Ma, Z., Noursalehi, P. & Zhu, Y., 2019, Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling. Antoniou, C., Dimitriou, L. & Pereira, F. (eds.). Amsterdam Netherlands: Elsevier, p. 229-261 33 p.

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review