Professor Majid Sarvi

  • Room: Level: 02 Room: B205
  • Building: Engineering Block B
  • Campus: Parkville

Research interests

  • Transportation Engineering


Majid Sarvi is the chair in Transport Engineering and the professor in Transport for Smart Cities at the University of Melbourne. He is the founder and the director of the AIMES (Australian Integrated Multimodal EcoSystem). AIMES is the world’s first urban testing ecosystem for implementing and testing of emerging connected transport technologies at large scale and in complex urban environment which involves 37 partners from government and leading Australian and global industry partners.

He has over 22 years of professional, academic and research experience in the areas of traffic and transport engineering. His researches are multidisciplinary with international outlook and both theoretically oriented and applied in nature. His fields of research cover a range of topics, including: connected multimodal transport network modelling and analysis, crowd dynamic modelling and simulation and network vulnerability assessment and optimization. He has been the author/co-author of over 250 refereed publications in top transportation journals and various conference and symposia proceedings. This includes over 140 ISI publications listed in Scopus and 7 papers in the International symposium of Traffic and Transportation Theory (ISTTT). He currently serves on the editorial board of several journals including Transportation Research Part C, Transportmetrica, and Journal of Transportation Letters.

He has served on several international research committees, the Network Modelling Committee (ADB30), Traffic Flow Theory and Characteristics Committee (AHB45), and the Emergency Evacuation Task Force of the Transportation Research Board (TRB) of the U.S. National Research Council. He is also the co-founder and co-chair of the Crowd Dynamic Modelling Subcommittee AHB45(2) of TRB.

Recent publications

  1. Li Y, Khoshelham K, Sarvi M, Haghani M. Direct generation of level of service maps from images using convolutional and long short-term memory networks. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations. 2019. DOI: 10.1080/15472450.2018.1563865
  2. Haghani M, Sarvi M. Heterogeneity of decision strategy in collective escape of human crowds: On identifying the optimum composition. International Journal of Disaster Risk Reduction. Elsevier BV. 2019. DOI: 10.1016/j.ijdrr.2019.101064
  3. Haghani M, Sarvi M. Imitative (herd) behaviour in direction decision-making hinders efficiency of crowd evacuation processes. Safety Science. Elsevier BV. 2019, Vol. 114. DOI: 10.1016/j.ssci.2018.12.026
  4. Asadi Bagloee S, Avi Ceder A, Sarvi M, Asadi M. Is it time to go for no-car zone policies? Braess Paradox Detection. Transportation Research Part A: Policy and Practice. Pergamon. 2019, Vol. 121. DOI: 10.1016/j.tra.2019.01.021
  5. Haghani M, Sarvi M. Simulating pedestrian flow through narrow exits. PHYSICS LETTERS A. Elsevier Science. 2019, Vol. 383, Issue 2-3. DOI: 10.1016/j.physleta.2018.10.029
  6. Gu Z, Saberi M, Sarvi M, Liu Z. A big data approach for clustering and calibration of link fundamental diagrams for large-scale network simulation applications. 22nd International Symposium on Transportation and Traffic Theory (ISTTT). Pergamon-Elsevier Science. 2018, Vol. 94. DOI: 10.1016/j.trc.2017.08.012
  7. Asadi Bagloee S, Asadi M, Sarvi M, Patriksson M. A hybrid machine-learning and optimization method to solve bi-level problems. EXPERT SYSTEMS WITH APPLICATIONS. Pergamon. 2018, Vol. 95. DOI: 10.1016/j.eswa.2017.11.039
  8. Kaviani Arani A, Thompson R, Rajabifard A, Sarvi M. A model for multi-class road network recovery scheduling of regional road networks. Transportation. Springer. 2018. DOI: 10.1007/s11116-017-9852-5
  9. Asadi Bagloee S, Sarvi M. An outer approximation method for the road network design problem. PLOS ONE. Public Library of Science. 2018, Vol. 13, Issue 3. DOI: 10.1371/journal.pone.0192454
  10. Haghani M, Sarvi M. Crowd behaviour and motion: Empirical methods. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL. Pergamon-Elsevier Science. 2018, Vol. 107. DOI: 10.1016/j.trb.2017.06.017
  11. Kaviani Arani A, Thompson R, Rajabifard A, Sarvi M. Hybrid machine learning and optimisation method to solve a tri-level road network protection problem. 25th World Congress on Intelligent Transport Systems (ITS). Institution of Engineering and Technology. 2018, Vol. 12, Issue 9. DOI: 10.1049/iet-its.2018.5168
  12. Haghani M, Sarvi M. Hypothetical bias and decision-rule effect in modelling discrete directional choices. TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE. Pergamon. 2018, Vol. 116. DOI: 10.1016/j.tra.2018.06.012
  13. Hoogendoorn SP, Daamen W, Knoop VL, Steenbakkers J, Sarvi M. Macroscopic Fundamental Diagram for pedestrian networks: Theory and applications. Transportation Research Part C: Emerging Technologies. Pergamon-Elsevier Science. 2018, Vol. 94. DOI: 10.1016/j.trc.2017.09.003
  14. Asadi Bagloee S, Sarvi M, Patriksson M, Asadi M. Optimization for Roads' Construction: Selection, Prioritization, and Scheduling. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING. Blackwell. 2018, Vol. 33, Issue 10. DOI: 10.1111/mice.12370
  15. Shahhoseini Z, Sarvi M, Saberi M. Pedestrian crowd dynamics in merging sections: Revisiting the "faster-is-slower" phenomenon. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS. Elsevier BV. 2018, Vol. 491. DOI: 10.1016/j.physa.2017.09.003

View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile