Freeway Travel Time Estimation and Forecasting


Dr. Angshuman Guin, Senior Research Engineer, Georgia Tech
Dr. Jorge Laval, Assistant Professor, Georgia Tech
Rama Chilukuri, Ph.D. Student, Georgia Tech

Project Overview:

Real-time traffic information provided by GDOT has proven invaluable for commuters in the Georgia freeway network.  The increasing number of Variable Message Signs, addition of services such as My-NaviGAtor, NaviGAtor-to-go etc. and the advancement of the 511 traffic information system will require the Traffic Management Center to provide more detailed and accurate traffic information to an increasing number of users.  In this context, the ability to forecast traffic conditions (both in space and time) would augment the services provided by NaviGAtor by allowing users to plan ahead for their trip.  Forecasts built into the estimation model will make the travel-time estimates more accurate by reducing the use of stale data.  Additionally, spatial forecast can help GDOT provide reliable information in areas with temporary outages in coverage; e.g. outages due to detector or cameras malfunction. 

The vast majority of real-time travel time estimation algorithms proposed in the literature are based on data mining techniques [1-5]. Unfortunately, this approach is unable to produce reliable forecasts because it does not take into account traffic dynamics (e.g., via a simulation model).  In Germany, a
simulation-based forecast system is already in place at most metropolitan areas, with very favorable user impacts [6].  Although successful, the German example is based on a type of simulation model (a Cellular Automata model) that has important drawbacks: difficult to calibrate, unable to incorporate different user classes (e.g., cars and trucks), and not proven to replicate detailed traffic dynamics on freeways.  The model proposed in this project [7-9] overcomes these drawbacks by incorporating the latest advances in traffic flow theory and simulation. 

This project will incorporate recent advances in traffic flow theory and simulation to build a framework able to provide short-term (up to ~30 min) travel time forecasts across the metropolitan Atlanta freeway network. This would allow us to predict the onset and propagation of congestion trough the network, and to improve current "real-time" travel time estimates in NaviGAtor (which are usually displayed with a ~10 min delay). Because off-the-shelf commercial simulation packages do not perform well in saturated freeways, we will use a traffic simulation model being developed at Georgia Tech, which is able to predict realistic traffic dynamics on congested freeways.


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  2. Rose, G. and Paterson, D. (1999) Dynamic Travel Time Estimation on Instrumented Freeways. Proceedings of 6th World Congress on Intelligent Transport Systems, Toronto, Canada.
  3. Kisgyorgy, L. and Rilett, L.R. (2002) Travel Time Prediction by Advanced Neural Network., Periodica Polytechnica Civil Engineering, Vol. 46, No. 1, 15-32.
  4. Ishak, S. and Al-Deek, H. (2002) Performance evaluation of short-term time-series traffic prediction model, Journal of Transportation Engineering, Vol. 128, No. 6, 490-498.
  5. Chien, S.I-J. and Kuchipudi, C. M. (2003) Dynamic travel time prediction with real-time and historic data, Journal of Transportation Engineering, Vol. 129, No. 6, 608-616.
  6. Traffic State in NRW.
  7. Laval, J. A. and Leclercq, L. Mechanism to describe stop-and-go waves: A mechanism to describe the formation and propagation of stop-and-go waves in congested freeway traffic. Forthcoming in Proceedings of  The Royal Society A, 2010.
  8. J A Laval and L Leclercq. Microscopic modeling of the relaxation phenomenon using a macroscopic lane-changing model. Transportation Research Part B, 42 (6):511-522, 2008.
  9. L Leclercq, J Laval, and E Chevallier. The Lagrangian coordinate system and what it means for first order traffic flow models. In B Heydecker, M Bell, and R Allsop, editors, 17th International Symposium on Transportation and Traffic Theory, Elsevier, New York, 2007.