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  • Riccarton

    EH14 4AS Edinburgh

    United Kingdom

20202026

Research activity per year

Personal profile

Biography

Jingjun is an Assistant Professor in sustainable transport planning (passenger mobility) at Heriot-Watt University, Scotland. He has contributed to international transport planning projects across Belgium, the Netherlands, Ireland and China. Currently, Jingjun is a Co-Investigator in TransiT (https://transit.ac.uk/), the UK’s largest research hub dedicated to transport decarbonisation through digital twins, bringing together 8 universities and 66 industrial partners.

Jingjun’s works develop human-centred solutions for more accessible, inclusive and sustainable passenger mobility transport systems, including reducing reliance on private vehicles, promoting public transport and active travel (walking and cycling), and guiding the adoption of emerging technologies such as electric and autonomous vehicles.

Methodologically, Jingjun uses data-driven digital technologies to design and evaluate the impact of transport policies on both individuals (preferences, behaviour and equity) and transport systems (emissions, accessibility and resilience). Recognising transport as a socio-technical system linking infrastructure and human decision-making, he adopts multi-disciplinary methods/toolkits, including agent-based simulation to assess policy impacts; optimisation to design implementable interventions; transport behaviours analysis and generative AI to better understand individual choices; and multi-stakeholder workshops to ensure policies fit the interests of different groups. By combining these methods into a structured workflow with up-to-date transport data, Jingjun develops “digital twins” that enable more comprehensive and adaptive policymaking than single tools alone. Notably, most of Jingjun’s research is delivered through open-source software (https://www.matsim.org/), supporting uptake by both researchers and practitioners.

Beyond research, Jingjun has contributed to multiple projects in Europe and China. He is also a member of the programme committee for the annual ABMTrans and ANT conference. Additionally, Jingjun has been involved in 5 teaching activities related to transport engineering and has supervised 19 bachelor's and master's theses from Heriot-Watt University (UK), TU Delft (the Netherlands), Beijing Jiaotong University (China) and Vrije Universiteit Brussel (Belgium).

Jingjun will share most of his latest updates on LinkedIn: https://www.linkedin.com/in/jingjunli/. Please feel free to connect if you find any common interests! :)

Research interests

Agent-based transport modelling, passenger mobility planning, sustainable transport, transport digital twin, autonomous vehicles

Education/Academic qualification

PostDoc Researcher in Transport Digital Twin at XCARCITY project (https://xcarcity.nl/), Delft University of Technology

20242025

Doctor of Philosophy, Reducing Urban Private Vehicle Dependency With Shared Autonomous Vehicles - Agent-based Simulation As A Digital Testbed for Transport Policy Evaluations, Vrije Universiteit Brussel

20192024

Master of Engineering, MEng: Transport Engineering with Business, University College Dublin

20162019

Bachelor of Engineering, Transport Engineering, Chang’an University

20132017

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy
  3. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure
  4. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities
  5. SDG 12 - Responsible Consumption and Production
    SDG 12 Responsible Consumption and Production
  6. SDG 15 - Life on Land
    SDG 15 Life on Land

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