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, the professor in Transport for Smart Cities and the program director of the "Transport Technologies" 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 and largest connected urban testing ecosystem for implementing and testing of emerging connected transport technologies at large scale and in complex urban environments which involves over 40 partners from government and leading Australian and global industry partners.

He has over 23 years of professional, academic and research experience in the areas of traffic and transport engineering. His research is 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 , 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. He currently serves on the editorial board of several journals including Transportation Research Part B, 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 Committee (ABR30) 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. Haghani, M.; Sarvi, M. 'Herding' in direction choice-making during collective escape of crowds: How likely is it and what moderates it?. Safety Science. ELSEVIER SCIENCE BV. 2019, Vol. 115, pp. 362-375. DOI: 10.1016/j.ssci.2019.02.034
  2. Haghani, M.; Sarvi, M. 'Rationality' in Collective Escape Behaviour: Identifying Reference Points of Measurement at Micro and Macro Levels. Journal of Advanced Transportation. Hindawi Publishing Corp. 2019, Vol. 2019. DOI: 10.1155/2019/2380348
  3. Emami, A.; Sarvi, M.; Asadi Bagloee, S. A neural network algorithm for queue length estimation based on the concept of k-leader connected vehicles. Journal of Modern Transportation. Springer Science and Business Media LLC. 2019, Vol. 27, Issue 4, pp. 341-354. DOI: 10.1007/s40534-019-00200-y
  4. 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. TAYLOR & FRANCIS INC. 2019, Vol. 23, Issue 3, pp. 300-308. DOI: 10.1080/15472450.2018.1563865
  5. Haghani, M.; Sarvi, M.; Shahhoseini, Z.; Boltes, M. Dynamics of social groups’ decision-making in evacuations. Transportation Research Part C: Emerging Technologies. Elsevier. 2019, Vol. 104, pp. 135-157. DOI: 10.1016/j.trc.2019.04.029
  6. Wang, J.; Ma, J.; Lin, P.; Chen, J.; Fu, Z.; Li, T.; Sarvi, M. Experimental study of architectural adjustments on pedestrian flow features at bottlenecks. Journal of Statistical Mechanics: Theory and Experiment. IOP PUBLISHING LTD. 2019, Vol. 2019, Issue 8. DOI: 10.1088/1742-5468/ab3190
  7. 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 SCIENCE BV. 2019, Vol. 35. DOI: 10.1016/j.ijdrr.2019.101064
  8. Haghani, M.; Sarvi, M. Imitative (herd) behaviour in direction decision-making hinders efficiency of crowd evacuation processes. Safety Science. ELSEVIER SCIENCE BV. 2019, Vol. 114, pp. 49-60. DOI: 10.1016/j.ssci.2018.12.026
  9. Bagloee, SA.; Ceder, AA.; 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-ELSEVIER SCIENCE LTD. 2019, Vol. 121, pp. 251-264. DOI: 10.1016/j.tra.2019.01.021
  10. Haghani, M.; Sarvi, M. Laboratory experimentation and simulation of discrete direction choices: Investigating hypothetical bias, decision-rule effect and external validity based on aggregate prediction measures. Transportation Research Part A: Policy and Practice. Elsevier Ltd. 2019, Vol. 130, pp. 134-157. DOI: 10.1016/j.tra.2019.09.040
  11. Dias, C.; Abdullah, M.; Sarvi, M.; Lovreglio, R.; Alhajyaseen, W. Modeling and simulation of pedestrian movement planning around corners. Sustainability. MDPIAG. 2019, Vol. 11, Issue 19. DOI: 10.3390/su11195501
  12. Khoshelham, K.; Yan, L.; Sarvi, M.; Haghani, M.; Tian, Y. Multi-view crowd congestion map generation based on ensemble learning. . Transportation Research Board. 2019.
  13. Shahhoseini, Z.; Sarvi, M. Pedestrian crowd flows in shared spaces: Investigating the impact of geometry based on micro and macro scale measures. Transportation Research Part B: Methodological. PERGAMON-ELSEVIER SCIENCE LTD. 2019, Vol. 122, pp. 57-87. DOI: 10.1016/j.trb.2019.01.019
  14. Emami, A.; Sarvi, M.; Bagloee, SA. Short-term traffic flow prediction based on faded memory Kalman Filter fusing data from connected vehicles and Bluetooth sensors. Simulation Modelling Practice and Theory. Elsevier BV. 2019, pp. 102025-102025. DOI: 10.1016/j.simpat.2019.102025
  15. Haghani, M.; Sarvi, M. Simulating dynamics of adaptive exit-choice changing in crowd evacuations: Model implementation and behavioural interpretations. Transportation Research Part C: Emerging Technologies. PERGAMON-ELSEVIER SCIENCE LTD. 2019, Vol. 103, pp. 56-82. DOI: 10.1016/j.trc.2019.04.009
  16. Haghani, M.; Sarvi, M. Simulating pedestrian flow through narrow exits. Physics Letters A. ELSEVIER SCIENCE BV. 2019, Vol. 383, Issue 2-3, pp. 110-120. DOI: 10.1016/j.physleta.2018.10.029
  17. Haghani, M.; Sarvi, M.; Scanlon, L. Simulating pre-evacuation times using hazard-based duration models: Is waiting strategy more efficient than instant response?. Safety Science. Elsevier. 2019, Vol. 117, pp. 339-351. DOI: 10.1016/j.ssci.2019.04.035
  18. Bagloee, SA.; Sarvi, M.; Rajabifard, A.; Thompson, RG. System optimal relaxation and Benders decomposition algorithm for the large-sized road network design problem. International Journal of Logistics Systems and Management. Inderscience Publishers. 2019, Vol. 34, Issue 4, pp. 486-509. DOI: 10.1504/IJLSM.2019.103516
  19. Emami, A.; Sarvi, M.; Bagloee, SA. Using Kalman filter algorithm for short-term traffic flow prediction in a connected vehicle environment. Journal of Modern Transportation. SpringerOpen. 2019, Vol. 27, Issue 3, pp. 222-232. DOI: 10.1007/s40534-019-0193-2
  20. Haghani, M.; Sarvi, M.; Shahhoseini, Z. When 'push' does not come to 'shove': Revisiting 'faster is slower' in collective egress of human crowds. Transportation Research Part A: Policy and Practice. PERGAMON-ELSEVIER SCIENCE LTD. 2019, Vol. 122, pp. 51-69. DOI: 10.1016/j.tra.2019.02.007

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