Observability and Sensor Placement Problem on Highway Segments: A Traffic Dynamics-Based Approach

Traffic congestion is a major problem on highways. To analyze this problem and then design different ways of reducing congestion, researchers need to observe traffic conditions on highways. Sensors are used to record and collect traffic data on highways. However, sensors must be placed efficiently to maximize the information collected and minimize monetary cost. This paper presents a novel approach for studying the observability problem on highway segments by utilizing linearized traffic dynamics about steady-state flows. First, we analyze the observability problem in terms of sensor placement and then present a method for comparing scenarios having different sensor placements along a highway. Different sensor placement scenarios are compared using the condition number of the observability matrix for the modeled system. Simulations are performed for various different numbers of highway cells, and then, generalized results are provided. We also discuss steps needed to extend this methodology to a general traffic network system.

[1]  Andreas Hegyi,et al.  Freeway traffic estimation within particle filtering framework , 2007, Autom..

[2]  George F. List,et al.  An Information-Theoretic Sensor Location Model for Traffic Origin-Destination Demand Estimation Applications , 2010, Transp. Sci..

[3]  R. Horowitz,et al.  Traffic density estimation with the cell transmission model , 2003, Proceedings of the 2003 American Control Conference, 2003..

[4]  Lawrence A. Klein,et al.  Traffic Detector Handbook: Third Edition - Volume I , 2006 .

[5]  Giuseppe Confessore,et al.  A Network Based Model for Traffic Sensor Location with Implications on O/D Matrix Estimates , 2001, Transp. Sci..

[6]  R B Goldblatt,et al.  FORMULATION OF GUIDELINES FOR LOCATING FREEWAY SENSORS , 1979 .

[7]  Carlos F. Daganzo,et al.  THE CELL TRANSMISSION MODEL, PART II: NETWORK TRAFFIC , 1995 .

[8]  Pamela Murray-Tuite,et al.  Vehicular network sensor placement optimization under uncertainty , 2013 .

[9]  Sabiha Amin Wadoo,et al.  Feedback ramp metering using Godunov method based hybrid model , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[10]  P. I. Richards Shock Waves on the Highway , 1956 .

[11]  Steve C. Southward,et al.  A Sensor Placement Strategy for the Hybrid Adaptive Feedforward Observer , 2006 .

[12]  Ying Liu,et al.  OPTIMAL DETECTOR LOCATIONS FOR OD MATRIX ESTIMATION , 2004 .

[13]  Ross Baldick,et al.  State Estimator Condition Number Analysis , 2001 .

[14]  C. Daganzo THE CELL TRANSMISSION MODEL.. , 1994 .

[15]  Andreas Hegyi,et al.  Parallelized Particle and Gaussian Sum Particle Filters for Large-Scale Freeway Traffic Systems , 2012, IEEE Transactions on Intelligent Transportation Systems.

[16]  Mauro Garavello,et al.  Traffic Flow on a Road Network , 2005, SIAM J. Math. Anal..

[17]  Yimin Wei,et al.  Condition Numbers of the Generalized Sylvester Equation , 2007, IEEE Transactions on Automatic Control.

[18]  Daiheng Ni,et al.  A Sampling Theorem Approach to Traffic Sensor Optimization , 2008, IEEE Transactions on Intelligent Transportation Systems.

[19]  Ying Liu,et al.  Detector Placement Strategies for Freeway Travel Time Estimation , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[20]  P S Parsonson,et al.  TRAFFIC DETECTOR HANDBOOK , 1985 .

[21]  Hai Yang,et al.  Optimal traffic counting locations for origin–destination matrix estimation , 1998 .

[22]  Mauro Garavello,et al.  Traffic Flow on Networks , 2006 .

[23]  Robert L. Bertini Toward Optimal Sensor Density for Improved Freeway Travel Time Estimation and Traveler Information , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[24]  R. Courant,et al.  Über die partiellen Differenzengleichungen der mathematischen Physik , 1928 .

[25]  Hani S. Mahmassani,et al.  Number and Location of Sensors for Real-Time Network Traffic Estimation and Prediction , 2006 .

[26]  Praveen Edara,et al.  Optimizing Freeway Traffic Sensor Locations by Clustering Global-Positioning-System-Derived Speed Patterns , 2010, IEEE Transactions on Intelligent Transportation Systems.

[27]  Markos Papageorgiou,et al.  Real-time freeway traffic state estimation based on extended Kalman filter: a general approach , 2005 .

[28]  Mauro Garavello,et al.  Source-Destination Flow on a Road Network , 2005 .

[29]  Monica Gentili,et al.  Locating Active Sensors on Traffic Networks , 2005, Ann. Oper. Res..