Dr. Mike Ma

Dr. Mike Ma

Lecturer in Transport Engineering
Department of Civil Engineering
Suzhou Campus

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.


  • BS.c. in Electrical Engineering, Shandong University, China, 2009
  • MS.c. in Information Science and Technology, Shandong University, China, 2012
  • Ph.D. in Transportation Engineering, The University of Queensland, Australia, 2016


Big Data Analytics
Large-Scale Optimization
Public Transport
Shared Mobility

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 landscape. 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.

Research Projects

Current projects

Sensing the Pulse of Urban Dynamics and Human Mobility

Cities are increasingly host sophisticated sensor networks that automatically collect data with extensive spatial coverage on a continuous basis. While data from dedicated sensors is still the backbone for various applications, data from opportunistic sensors, have shown the potential to provide valuable information. This project will explore innovative approaches to take advantage of opportunistic data fused with traditional data to better inform decisions. Examples of such data include:

  • Geolocation data from dedicated GPS devices (e.g. fleets)
  • Geolocation data from apps (e.g. mobile phones, social media)
  • Data from various APIs (e.g. Google Maps, Uber, Lyft)
  • AFC and AVL data from transit

This data is diverse, multimodal, multiscale with extensive spatiotemporal characteristics. Building on the previous work on big data analytics, the research will propose new and advanced methods for analysis and inference of the quantities of interest from multisource data at both the aggregate and individual levels. At the aggregate level, they support OD inference, estimation of link flows and speeds, while at the disaggregate level they can provide information on individual mobility patterns and behavioural dynamics. As such, they provide the information building blocks for a wide range of applications related to the design of mobility services, new planning paradigms,  innovative control strategies, dynamic pricing, and personalised information and incentives for collaborative travel.

Revitalising Public Transport Services

Accelerating the adoption of technological advances as the foundation for innovation is an important means to increase public transportation appeal and hence, its use as the preferred mode which is directly related to reducing congestion in urban areas. Building on the previous research on public transport data analytics for understanding systems/users, real-time decision support/information provision, and long-term planning practices, the project focuses on the innovative designs of public transportation services and operations:

  • Exploring the self-evolution opportunities for public transport systems by adding flexibility (e.g. flexible routes and fleets) and automation (i.e. AV fleet platoon).
  • Examining the partnership opportunity with shared mobility services (e.g. first/last mile access) to deliver high capacity, high frequency, high quality bus rapid transit (BRT) services.
  • Developing systematic optimization and simulation approaches for the design of on-demand public transport services and the evaluation of innovative multimodal services

Mobility as a Service: Platform Design and Optimisation

Most of the developed world have long been shaped around automobiles. However, the flourish of sharing economy coupled with information and communication technology (ICT) – such as the smartphone-facilitated ride-sharing, bike-sharing, ride-hailing, on-demand public transport – has been altering city dwellers’ travel patterns: vehicle ownership is yielding to mobility accessibility. They follow different business models with no one emerging as successful. This trend is expected to accelerate in the future, generating many questions, not the least of which is how the changing supply matches and shapes future demand, and how transit systems can interact with those developments to meet their mission more effectively and efficiently.

The area focuses on platform strategy and operation business models of mobility as a service to attain predictable and socially desirable outcomes, and value creation for public and private sectors. Particularly, the research focuses on:

  • Platform-based algorithms for dynamic stochastic matching in two-sided markets
  • Behavior-based models and algorithms for simultaneous design of pricing and incentive strategies

Past projects

Congestion Management in Urban Railway Systems

Urbanization is increasing globally, and 55% of the world’s population now lives in urban areas. By 2050, this is projected to increase to 68% (notably 83% for upper-middle-income countries like North America, Europe, and Oceania) (United Nations, 2019). The densification of urban areas has led to a rise in urban congestion on roads and public transport systems. Adding capacity in urban rails, such as extending networks or updating signaling systems, to deal with the crowding is often difficult, especially in the short term. This project focuses on better utilisation of physical capacity through management of operations and demand leveraging on the smart card and train movements data. The project was done at MIT Transit Lab, funded by Mass Transit Railway Corporation Limited, Hong Kong.

The research has developed a suite of data-driven methodologies/models/tools/platforms, serving as building blocks for monitoring, prediction, control and planning practices in urban railway systems.

  • Monitoring and Prediction: Estimating crowding on the platform for a specific day and also short-term prediction to provide passenger information. The methodologies/tools have been transferred to Hong Kong for daily performance evaluation and support MTR mobile app on providing real-time/predicted waiting time at key stations.
  • Planning (Public Transport Demand Management, PTDM): Developing framework to structure the development and implementation of PTDM strategies; Evaluating and monitoring the effectiveness of PTDM strategies at different levels of aggregation; Designing promotion-based PTDM strategies to increase capacity utilization while save inestments. The methodology was used to assess the potential of various incentive structures (OD/station, pricing, times) for ‘Early-Bird’ promotion program in Hong Kong.
  • Control (Network Performance Model Platform):  Data-driven monitoring & decision support platform with visual analytics serves for applications, such as performance monitoring (what was/is…), operations control and strategic planning (what if…), and network resilience assessment under unexpected disruptions.


Please refer to my ORCID for the up-to-date list of publications


Teaching Commitments

  • 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 supervision)
Last modified: 19/02/2021