Saif Eddin  Jabari, PhD

Saif Eddin Jabari, PhD

Assistant Professor, NYUAD
Research Assistant Professor

Civil and Urban Engineering

Biography

Saif Jabari is an Assistant Professor of Civil and Urban Engineering at New York University in Abu Dhabi and also holds an appointment with New York University Tandon School of Engineering as Research Assistant Professor.  His research interests lie at the interface between data analysis and theoretical traffic flow modeling.  One of the main themes of his research is the development of methods for understanding and quantifying uncertainty in transportation systems.  His recent work has focused on the development of real-time traffic analytics, including traffic state estimation, network-wide real-time dynamic control, and incident detection, localization, and sensor placement problems for urban traffic networks.

Prior to joining NYUAD, Jabari was a Post-Doctoral Researcher in the Mathematical Sciences and Analytics Department at the IBM T.J. Watson Research Center in Yorktown Heights, NY.  Jabari received his Ph.D. in Civil Engineering from the University of Minnesota, Twin Cities in 2012 and his B.Sc. degree in Civil Engineering from in the University of Jordan in 2001. His doctoral dissertation received the 2012 Milton Pikarsky Memorial Award for best dissertation in Science and Technology.

Journal Articles

  • Jabari, S.E., Zheng, F., Liu, H., and Filipovska, M. (2017).  Stochastic Lagrangian modeling of traffic dynamics. [preprint]
  • Jabari, S.E., Freris, N., and Dilip, D. (2017). Sparse travel time estimation from streaming data. Under review. [preprint]
  • Jabari, S.E. (2016).  Node modeling for congested urban road networks. Transportation Research Part B, 91. 229–249. [manuscript]
  • Jabari, S.E. & Wynter, L. (2016).  Sensor placement with time-to-detection guarantees.  EURO Journal on Transportation and Logistics, 5(4). 415-433. [manuscript]
  • Jabari, S.E., Zheng, J., & Liu, H. (2014). A probabilistic stationary speed-density relation based on Newell's simplified car-following model. Transportation Research Part B, 68. 205-223.  [manuscript]
  • Jabari, S.E. & Liu, H. (2013). A stochastic model of traffic flow: Gaussian approximation and estimation. Transportation Research Part B, 47(1). 15-41. [manuscript]
  • Jabari, S.E. & Liu, H. (2012). A stochastic model of traffic flow: Theoretical foundations. Transportation Research Part B, 46(1). 156-174. [manuscript]
  • Liu, H. & Jabari, S.E. (2008). Evaluation of corridor traffic management and planning strategies that use microsimulation: A case study. Transportation Research Record 2088. 26-35. [link to article]

Other Publications

  • Jabari, S.E. (2012). A stochastic model of macroscopic traffic flow: Theoretical foundations (Doctoral dissertation).  University of Minnesota. [disseration]
  • Jabari, S.E., He, X., & Liu, H. (2011). Heuristic solution techniques for no-notice emergency evacuation traffic management. In: Network Reliability in Practice. Springer, New York, pp. 241-259. [link to chapter]
  • Liu, H. & Jabari, S.E. (2009). Responding to the unexpected: Development of a dynamic data-driven model for e ffective evacuation. (Report no. Mn/DOT 2009-36). Minneapolis, MN: Center for Transportation Studies, University of Minnesota, Twin Cities. [report]

Education

University of Minnesota, Twin Cities, 2012

Doctor of Philosophy, Civil Engineering

University of Minnesota, Twin Cities, 2009

Master of Science, Civil Engineering

University of Jordan, 2001

Bachelor of Science, Civil Engineering

Experience

New York University, Abu Dhabi

Assistant Professor

From: September 2014 to present

IBM T.J. Watson Research Center

Post-doctoral Researcher

From: November 2012 to August 2014

University of Minnesota, Twin Cities

Research Assistant

From: June 2006 to September 2012

Arabtech Jardaneh, Engineers and Architects

Highway Engineer

From: May 2002 to June 2005

Patents

Traffic Network Sensor Placement (no. US9466209 B2), (Utility)

Locations for traffic sensors can be determined by a computer system that identifies a particular segment of a travel path. Traffic flow data from other segments of travel path are accessed based on traffic flow characteristics of the particular path. Using the traffic flow data, parameters for a traffic incident symptom propagation model are generated, and a location of a traffic incident along the segment of the path is determined. Using time-to-detection limits and the incident model, upstream and downstream distances are determined, and the locations of two sensors are identified based on the distances.

Traffic Incident Location Identification (no. US9286797 B1), (Utility)

A location for a traffic incident can be determined by a computer system, using data from a first and second sensor along a travel path. A receiving and sending symptom of the traffic incident are detected from a first and second sensor, using traffic flow data from the sensors. The locations of the first and second sensors are determined. The location and traffic flow data from each sensor are used to create a sending and receiving profiles. From the profiles, a convergence formula is build. Using the convergence formula and by determining a convergence point for the sending and receiving symptoms, a time and location of the traffic incident is identified.

Awards + Distinctions

  • Milton Pikarsky Award. 2012 Best Doctoral Dissertation in Science and Technology. Awarded by the Council of University Transportation Centers (CUTC).
  • Student of the Year. 2010 Student of the Year in ITS.  Awarded by the ITS Institute of the University of Minnesota.
  • Best MS Thesis in Civil Engineering, 2009.  Awarded by the Department of Civil Engineering at the University of Minnesota.

Research Interests

  • Traffic flow theory and dynamical modeling
  • Real-time traffic analytics
  • Network flows and sensor placement problems
  • Applied probability and uncertainty quantification