Professor Majid Sarvi
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- Khoshelham, K.; Yan, L.; Sarvi, M.; Haghani, M.; Tian, Y. Multi-view crowd congestion map generation based on ensemble learning. . Transportation Research Board. 2019.
- 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
- 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
- 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
- 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 SCIENCE BV. 2019, Vol. 117, pp. 339-351. DOI: 10.1016/j.ssci.2019.04.035
- Emami, A.; Sarvi, M.; Asadi Bagloee, S. Using Kalman filter algorithm for short-term traffic flow prediction in a connected vehicle environment. Journal of Modern Transportation. Springer Science and Business Media LLC. 2019. DOI: 10.1007/s40534-019-0193-2
- 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
- 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. Transportation Research Part C: Emerging Technologies. PERGAMON-ELSEVIER SCIENCE LTD. 2018, Vol. 94, pp. 151-171. DOI: 10.1016/j.trc.2017.08.012
- Bagloee, SA.; Asadi, M.; Sarvi, M.; Patriksson, M. A hybrid machine-learning and optimization method to solve bi-level problems. Expert Systems with Applications. PERGAMON-ELSEVIER SCIENCE LTD. 2018, Vol. 95, pp. 142-152. DOI: 10.1016/j.eswa.2017.11.039
- Kaviani, A.; Thompson, RG.; Rajabifard, A.; Sarvi, M. A model for multi-class road network recovery scheduling of regional road networks. Transportation. Springer Nature. 2018, pp. 1-35. DOI: 10.1007/s11116-017-9852-5
- Bagloee, SA.; Sarvi, M. An outer approximation method for the road network design problem. PLOS ONE. PUBLIC LIBRARY SCIENCE. 2018, Vol. 13, Issue 3. DOI: 10.1371/journal.pone.0192454
- Haghani, M.; Sarvi, M. Crowd behaviour and motion: Empirical methods. Transportation Research Part B: Methodological. PERGAMON-ELSEVIER SCIENCE LTD. 2018, Vol. 107, pp. 253-293. DOI: 10.1016/j.trb.2017.06.017
- Kaviani, A.; Thompson, RG.; Rajabifard, A.; Sarvi, M. Hybrid machine learning and optimisation method to solve a tri-level road network protection problem. IET Intelligent Transport Systems. INST ENGINEERING TECHNOLOGY-IET. 2018, Vol. 12, Issue 9, pp. 1011-1019. DOI: 10.1049/iet-its.2018.5168
- Haghani, M.; Sarvi, M. Hypothetical bias and decision-rule effect in modelling discrete directional choices. Transportation Research Part A: Policy and Practice. PERGAMON-ELSEVIER SCIENCE LTD. 2018, Vol. 116, pp. 361-388. DOI: 10.1016/j.tra.2018.06.012
View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile