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Encyclopedia of Complexity and Systems Science pp 3943–3964Cite as

Freeway Traffic Management and Control

  • Reference work entry

Acronyms and Abbreviations

MPC:

Model Predictive Control

OD:

Origin-Destination

ADAS:

Advanced Driver Assistance Systems

AHS:

Automated Highway System

IVHS:

Intelligent Vehicle/Highway System

Definition of the Subject

The goal of this chapter is to provide an overview of dynamic traffic control techniques described in the literature and applied inpractice. Dynamic traffic control is the term to indicate a collection of tools, procedures, and methods that areused to intervene in traffic in order to improve the traffic flow on the short term, i. e., ranging from minutes to hours. The nature of theimprovement may include increased safety, higher traffic flows, shorter travel times, more stable traffic flows, more reliable travel times, or reducedemissions and noise production.

The tools used for this purpose are in general changeable signs (including traffic signals, dynamic speed limit signs, and changeable messagesigns), radio broadcast messages, or human traffic controllers at the...

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Abbreviations

Feedforward control:

The block diagram of a feedforward control structure is shown in Fig. 1 [4]. The behavior of process P can be influenced by the control inputs. As a result the outputs (measurements or observations) show a given behavior. The controller C determines the control inputs in order to reach a given desired behavior of the outputs, taking into account the disturbances that act on the process. In the feedforward structure the controller C translates the desired behavior and the measured disturbances into control actions for the process.

The term feedforward refers to the fact that the direction of the information flow in the system contains no loops, i. e., it propagates only “forward”.

The main advantages of a feedforward controller are that the complete system is stable if the controller and the process are stable, and that its design is in general simple.

Figure 1
figure 1_232

The feedforward control structure

Full size image
Feedback control:

In Fig. 2 the feedback control structure is shown [4]. In contrast to the feedforward control structure, here the behavior of the outputs is coupled back to the controller (hence the name feedback). This structure is also often referred to as “closed‐loop” control.

Figure 2
figure 2_232

The feedback control structure

Full size image

The main advantages of a feedback controller over a feedforward controller are that (1) it may have a quicker response (resulting in better performance), (2) it may correct undesired offsets in the output, (3) it may suppress unmeasurable disturbances that are observable through the output only, and (4) it may stabilize an unstable system.

Optimal control:

Optimal control is a control methodology that formulates a control problem in terms of a performance function, also called an objective function [73]. This function expresses the performance of the system over a given period of time, and the goal of the controller is to find the control signals that result in optimal performance. Depending on the mathematical description of the control problem there exist several methods for the optimization of the control input including analytic and numerical approaches. Optimal control can be considered as a feedforward control approach.

Model predictive control:

Model predictive control (MPC) is an extension of the optimal control framework [15,79]. In Fig. 3 the block diagram of MPC is shown.

Figure 3
figure 3_232

The model predictive control (MPC) structure

Full size image

In MPC, at each time step k the optimal control signal is computed (by numerical optimization) over a prediction horizon of \( { N_{\mathrm{p}} } \) steps. A control horizon \( { N_{\mathrm{c}} (< N_{\mathrm{p}} } \)) can be selected to reduce the number of variables and to improve the stability of the system. Beyond the control horizon the control signal is usually taken to be constant. From the resulting optimal control signal only the first sample of the computed control signal is applied to the process. In the next time step \( { k+1 } \), a new optimization is performed with a prediction horizon that is shifted one time step ahead, and of the resulting control signal again only the first sample is applied, and so on. This scheme, called rolling horizon, allows for updating the state from measurements, or even for updating the model in every iteration step.

In other words, MPC is equivalent to optimal control extended with feedback. The advantage of updating the state through feedback is that this results in a controller that has a low sensitivity to prediction errors. Regularly updating the prediction model results in an adaptive control system, which could be useful in situations where the model significantly changes, such as in case of incidents or changing weather conditions.

Bibliography

Primary Literature

  1. Alessandri A, Di Febbraro A, Ferrara A, Punta E (1999) Nonlinear optimization for freeway control using variable‐speed signaling. IEEE Trans Veh Technol 48(6):2042–2052

    Google Scholar 

  2. Algers S, Bernauer E, Boero M, Breheret L, Di Taranto C, Dougherty M, Fox K, Gabard J-F (2000) SMARTEST – Final report for publication. Technical report, ITS, University of Leeds. http://www.its.leeds.ac.uk/projects/smartest. Accessed on 22 July 2008

  3. André M, Hammarström U (2000) Driving speeds in europe for pollutant emissions estimation. Transp Res Part D 5(5):321–335

    Google Scholar 

  4. Åström KJ, Wittenmark B (1997) Computer Controlled Systems, 3rd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  5. Banks JH (2005) Metering ramps to divert traffic around bottlenecks: Some elementary theory. Transp Res Rec 1925:12–19

    Google Scholar 

  6. Bell MGH (2000) A game theory approach to measuring the performance reliability of transport networks. Transp Res Part B 34(6):533–545

    Google Scholar 

  7. Bellemans T (2003) Traffic Control on Motorways. Ph.D. Thesis, Katholieke Universiteit Leuven, Leuven. ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/bellemans/PhD/03-82.pdf. Accessed on 22 July 2008

  8. Bellemans T, De Schutter B, De Moor B (2006) Model predictive control for ramp metering of motorway traffic: A case study. Control Eng Practice 14:757–767

    Google Scholar 

  9. Bellemans T, De Schutter B, Wets G, De Moor B (2006) Model predictive control for ramp metering combined with extended kalman filter‐based traffic state estimation. In: Proceedings of the 2006 IEEE Intelligent Transportation Systems Conference (ITSC 2006), Toronto, Canada, pp 406–411

    Google Scholar 

  10. Bishop R (2005) Intelligent vehicle technology and trends. In: Walker J (ed) Artech House ITS Library. Artech House, Boston

    Google Scholar 

  11. Braess D (1968) Über ein Paradoxon aus der Verkehrsplanung. Unternehmensforsch 12:258–268

    MathSciNet  MATH  Google Scholar 

  12. Branston D (1976) Models of single lane time headway distributions. Transp Sci 10:125–148

    Google Scholar 

  13. Brownfield J, Graham A, Eveleigh H, Maunsell F, Ward H, Robertson S, Allsop R (2003) Congestion and accident risk. In: Technical report, Road Safety Research Report no. 44. Department for Transport, London

    Google Scholar 

  14. Byon Y-J, Shalaby A, Abdulhai B (2006) Travel time collection and traffic monitoring via GPS technologies. In: Proceedings of the 2006 IEEE Intelligent Transportation Systems Conference (ITSC 2006), Toronto, Canada, pp 677–682

    Google Scholar 

  15. Camacho EF, Bordons C (1995) Model Predictive Control in the Process Industry. Springer, Berlin

    Google Scholar 

  16. Cambridge Systematics, Inc. (2001) Twin Cities Ramp Meter Evaluation – Final Report. Cambridge Systematics, Inc., Oakland. Prepared for the Minnesota Department of Transportation

    Google Scholar 

  17. Carsten O, Tate F (2001) Intelligent speed adaptation: The best collision avoidance system? In: Proceedings of the 17th International Technological Conference on the Enhanced Safety of Vehicles, Amsterdam, Netherlands, pp 1–10

    Google Scholar 

  18. Carvell JD, Balke K Jr, Ullman J, Fitzpatrick K, Nowlin L, Brehmer C (1997) Freeway management handbook. Technical report. Federal Highway Administration, Department of Transport (FHWA, DOT), Washington DC, Report No. FHWA-SA-97-064

    Google Scholar 

  19. Castello P, Coelho C, Ninno ED (1999) Traffic monitoring in motorways by real-time number plate recognition. In: Proceedings of the 10th International Conference on Image Analysis and Processing, Venice, Italy, pp 1129–1131

    Google Scholar 

  20. Chaudhary NA, Messer CJ (2000) Ramp metering technology and practice. Technical Report 2121-1. Texas Transportation Institute, The Texas A&M University System, College Station

    Google Scholar 

  21. Chen A, Yang H, Lo HK, Tang WH (2002) Capacity reliability of a road network: An assessment methodology and numerical results. Transp Res Part B 36(3):225–252

    Google Scholar 

  22. Chen OJ, Hotz AF, Ben-Akiva ME (1997) Development and evaluation of a dynamic ramp metering control model. In: Proceedings of the 8th IFAC/IFIP/IFORS Symposium on Transportation Systems, Chania, Greece, June 1997, pp 1162–1168

    Google Scholar 

  23. Chien C-C, Zhang Y, Ioannou PA (1997) Traffic density control for automated highway systems. Automatica 33(7):1273–1285

    Google Scholar 

  24. Cools M, Moons E, Wets G (2007) Investigating the effect of holidays on daily traffic counts: A time series approach. Transp Res Record 2019:22–31

    Google Scholar 

  25. Cuena J, Hernández J, Molina M (1995) Knowledge‐based models for adaptive traffic management systems. Transp Res Part C 3(5):311–337

    Google Scholar 

  26. Daganzo CF (1994) The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory. Transp Res Part B 28B(4):269–287

    Google Scholar 

  27. Daganzo CF (1996) The nature of freeway gridlock and how to prevent it. In: Lesort JB (ed) Proceedings of the 13th International Symposium of Transportation and Traffic Theory, Lyon, France

    Google Scholar 

  28. Daganzo CF (1997) Fundamentals of Transportation and Traffic Operations. Pergamon, Kidlington

    Google Scholar 

  29. Di Febbraro A, Parisini T, Sacone S, Zoppoli R (2001) Neural approximations for feedback optimal control of freeway systems. IEEE Trans Veh Technol 50(1):302–312

    Google Scholar 

  30. Diakaki C, Papageorgiou M, McLean T (2000) Integrated traffic‐responsive urban corridor control strategy in Glasgow, Scotland. Transp Res Rec 1727:101–111

    Google Scholar 

  31. Elefteriadou L (1997) Freeway merging operations: A probabilistic approach. In: Proceedings of the 8th International Federation of Automatic Control (IFAC) Symposium on Transportation Systems, Chania, Greece, pp 1351–1356

    Google Scholar 

  32. Federal Highway Administration, US Department of Transportation (1995) Detection Technology for IVHS – Final Report Addendum. Technical report. Federal Highway Administration, US Department of Transportation, Washington, Contract DTFH61-91-C-00076, Report No. FHWA-RD-96-109

    Google Scholar 

  33. Federal Highway Administration, US Department of Transportation (1995) Detection Technology for IVHS – Task L Final Report. Technical report. Federal Highway Administration, US Department of Transportation, Washington, Contract DTFH61-91-C-00076, Report No. FHWA_RD-95-100

    Google Scholar 

  34. Fenton RE (1994) IVHS/AHS: Driving into the future. IEEE Control Syst Mag 14(6):13–20

    Google Scholar 

  35. Gartner NH, Improta G (eds) (1995) Urban Traffic Networks – Dynamic Flow Modeling and Control. Springer, Berlin

    Google Scholar 

  36. Gupta A, Maslanka VJ, Spring GS (1992) Development of prototype knowledge‐based expert system for managing congestion on massachusets turnpike. Transp Res Rec (1358):60–66

    Google Scholar 

  37. Hall FL, Agyemang-Duah K (1991) Freeway capacity drop and the definition of capacity. Transp Res Rec (1320):91–98

    Google Scholar 

  38. Hardman EJ (1996) Motorway speed control strategies using SISTM. In: Proceedings of the 8th International Conference on Road Traffic Monitoring and Control. IEE Conference Publication, no 422. London, 23–25 April 1996, pp 169–172

    Google Scholar 

  39. Hasan M, Jha M, Ben-Akiva M (2002) Evaluation of ramp control algorithms using microscopic traffic simulation. Transp Res Part C 10:229–256

    Google Scholar 

  40. Hedrick JK, Tomizuka M, Varaiya P (1994) Control issues in automated highway systems. IEEE Control Syst Mag 14(6):21–32

    Google Scholar 

  41. Hegyi A, De Schutter B, Hellendoorn J (2005) Model predictive control for optimal coordination of ramp metering and variable speed limits. Transp Res Part C 13(3):185–209

    Google Scholar 

  42. Hegyi A, De Schutter B, Hellendoorn J (2005) Optimal coordination of variable speed limits to suppress shock waves. IEEE Trans Intell Transp Syst 6(1):102–112

    Google Scholar 

  43. Hegyi A, Girimonte D, Babuška R, De Schutter B (2006) A comparison of filter configurations for freeway traffic state estimation. In: Proceedings of the International IEEE Conference on Intelligent Transportation Systems 2006, Toronto, Canada, 17–20 September 2006, pp 1029–1034

    Google Scholar 

  44. Helbing D (1997) Verkehrsdynamik – Neue physikalische Modellierungskonzepte. Springer, Berlin

    MATH  Google Scholar 

  45. Hellinga B, Van Aerde M (1995) Examining the potential of using ramp metering as a component of an ATMS. Transp Res Record 1494:75–83

    Google Scholar 

  46. Hernández J, Cuena J, Molina M (1999) Real-time traffic management through knowledge‐based models: The TRYS approach. In: ERUDIT tutorial on Intelligent Traffic Management Models. Helsinki, Finland, 3 August 1999. http://www.erudit.de/erudit/events/tc-c/tut990803.htm. Accessed on 23 July 2008

  47. Hoogendoorn SP (1997) Optimal control of dynamic route information panels. In: Proceedings of the 4th World Congress on Intelligent Transportation Systems, IFAC Transportation Systems, Chania, Greece, pp 399–404

    Google Scholar 

  48. Hoogendoorn SP, Bovy PHL (2000) Continuum modelling of multiclass traffic flow. Transp Res Part B 34:123–146

    Google Scholar 

  49. Hoogendoorn SP, Bovy PHL (2001) State-of-the-art of vehicular traffic flow modelling. J Syst Control Eng – Proc Inst Mech Eng, Part I 215(14):283–303

    Google Scholar 

  50. Hotz A, Much C, Goblick T, Corbet E, Waxman A, Ashok K, Ben-Akiva M, Koutsopoulos H (1992) A distributed, hierarchical system architecture for advanced traffic management systems and advanced traffic information systems. In: Proceedings of the Second Annual Meeting of IVHS AMERICA, Newport Beach

    Google Scholar 

  51. http://www.auto21.ca/index.php. Accessed on 23 July 2008

  52. http://www.cvisproject.org/. Accessed on 23 July 2008

  53. http://www.damas.ift.ulaval.ca/projets/auto21/en/index.html. Accessed on 23 July 2008

  54. http://www.path.berkeley.edu/. Accessed on 23 July 2008

  55. Jacobson L, Henry K, Mehyar O (1989) Real-time metering algorithm for centralized control. Transp Res Record 1232:17–26

    Google Scholar 

  56. Jiang G, Gang L, Cai Z (2006) Impact of probe vehicles sample size on link travel time estimation. In: Proceedings of the 2006 IEEE Intelligent Transportation Systems Conference (ITSC 2006), Toronto, Canada, 2006, pp 891–896

    Google Scholar 

  57. Karimi A, Hegyi A, De Schutter B, Hellendoorn H, Middelham F (2004) Integration of dynamic route guidance and freeway ramp metering using model predictive control. In: Proceedings of the 2004 American Control Conference (ACC 2004), Boston, MA, USA, 30 June–2 July 2004, pp 5533–5538

    Google Scholar 

  58. Kell JH, Fullerton IJ, Mills MK (1990) Traffic Detector Handbook, Number FHWA-IP-90-002, 2nd edn. Federal Highway Administration, US Department of Transportation, Washington DC

    Google Scholar 

  59. Kerner BS (2002) Empirical features of congested patterns at highway bottlenecks. Transp Res Record (1802):145–154

    Google Scholar 

  60. Kerner BS (2004) The Physics of Traffic. Understanding Complex Systems. Springer, Berlin

    Google Scholar 

  61. Kerner BS (2007) Control of spatiotemporal congested traffic patterns at highway bottlenecks. IEEE Trans Intell Transp Syst 8(2):308–320

    MathSciNet  ADS  Google Scholar 

  62. Kerner BS (2007) On-ramp metering based on three-phase traffic theory. Traffic Eng Control 48(1):28–35

    Google Scholar 

  63. Klein LA, Kelley MR, Mills MK (1997) Evaluation of overhead and in‐ground vehicle detector technologies for traffic flow measurement. J Test Evaluation (JTEVA) 25(2):215–224

    Google Scholar 

  64. Klein LA, Mills MK, Gibson DRP (2006) Traffic Detector Handbook, Number FHWA-HRT-06-108, 3rd edn. Federal Highway Administration, US Department of Transportation, Washington DC

    Google Scholar 

  65. Kotsialos A, Papageorgiou M (2005) A hierarchical ramp metering control scheme for freeway networks. In: Proceedings of the American Control Conference, Portland, OR, USA, 8–10 June 2005, pp 2257–2262

    Google Scholar 

  66. Kotsialos A, Papageorgiou M, Middelham F (2001) Optimal coordinated ramp metering with advanced motorway optimal control. In: Proceedings of the 80th Annual Meeting of the Transportation Research Board, vol 3125. Washington DC

    Google Scholar 

  67. Kotsialos A, Papageorgiou M, Mangeas M, Haj-Salem H (2002) Coordinated and integrated control of motorway networks via non‐linear optimal control. Transp Res C 10(1):65–84

    Google Scholar 

  68. Kraan M, van der Zijpp N, Tutert B, Vonk T, van Megen D (1999) Evaluating networkwide effects of variable message signs in the netherlands. Transp Res Record 1689:60–67

    Google Scholar 

  69. Kühne RD (1991) Freeway control using a dynamic traffic flow model and vehicle reidentification techniques. Transp Res Record 1320:251–259

    Google Scholar 

  70. Lee HY, Lee H-W, Kim D (1999) Empirical phase diagram of traffic flow on highways with on-ramps. In: Helbing D, Herrmann HJ, Schreckenberg M, Wolf DE (eds) Traffic and Granular Flow '99. Springer, Berlin

    Google Scholar 

  71. Lenz H, Sollacher R, Lang M (2001) Standing waves and the influence of speed limits. In: Proceedings of the European Control Conference 2001, Porto, Portugal, pp 1228–1232

    Google Scholar 

  72. Levinson D, Zhang L (2006) Ramp meters on trial: Evidence from the twin cities metering holiday. Transp Res Part A 40A:810–828

    Google Scholar 

  73. Lewis FL (1992) Applied Optimal Control and Estimation. Prentice Hall, Upper Saddle River

    Google Scholar 

  74. Li PY, Horowitz R, Alvarez L, Frankel J, Robertson AM (1995) Traffic flow stabilization. In: Proceedings of the American Control Conference, Seattle, Washington, June 1995, pp 144–149

    Google Scholar 

  75. Lighthill MJ, Whitham GB (1955) On kinematic waves, II. A theory of traffic flow on long crowded roads. Proc Royal Soc 229A(1178):317–345

    MathSciNet  ADS  Google Scholar 

  76. Lin P-W, Chang G-L (2007) A generalized model and solution algorithm for estimation of the dynamic freeway origin‐destination matrix. Transp Res Part B, 41B:554–572

    Google Scholar 

  77. Little R, Rubin D (1987) Handbook of Transportation Science. Wiley, New York

    Google Scholar 

  78. Lo HK, Luo XW, Siu BWY (2006) Degradable transport network: Travel time budget of travelers with heterogeneous risk aversion. Transp Res Part B 40(9):792–806

    Google Scholar 

  79. Maciejowski JM (2002) Predictive Control with Constraints. Prentice Hall, Harlow

    Google Scholar 

  80. Maerivoet S, De Moor B (2005) Cellular automata models of road traffic. Phys Rep 419(1):1–64

    MathSciNet  ADS  Google Scholar 

  81. May AD (1990) Traffic Flow Fundamentals. Prentice Hall, Englewood Cliffs

    Google Scholar 

  82. McDonald M, Hounsell NB, Njoze SR (1995) Strategies for route guidance systems taking account of driver response. In: Proceedings of 6th Vehicle Navigation and Information Systems Conference, Seattle, WA, July 1995, pp 328–333

    Google Scholar 

  83. Middelham F (1999) A synthetic study of the network effects of ramp metering. Technical report. Transport Research Centre (AVV), Dutch Ministry of Transport, Public Works and Water Management, Rotterdam

    Google Scholar 

  84. Middelham F (2003) State of practice in dynamic traffic management in The Netherlands. In: Proceedings of the 10th IFAC Symposium on Control in Transportation Systems (CTS 2003), Tokyo, Japan, August 2003

    Google Scholar 

  85. Middleton D, Gopalakrishna D, Raman M (2002) Advances in traffic data collection and management. Technical Report BAT-02-006. Texas Transportation Institute, Cambridge Systematics Inc., Washington. http://www.itsdocs.fhwa.dot.gov/JPODOCS/REPTS_TE/13766.html. Accessed on 23 July 2008

  86. Molina M, Hernández J, Cuena J (1998) A structure of problem‐solving methods for real-time decision support in traffic control. Int J Human-Computer Stud 49:577–600

    Google Scholar 

  87. Papageorgiou M (1980) A new approach to time-of-day control based on a dynamic freeway traffic model. Transp Res Part B, 14B:349–360

    Google Scholar 

  88. Papageorgiou M (1983) A hierarchical control system for freeway traffic. Transp Res Part B, 17B(3):251–261

    Google Scholar 

  89. Papageorgiou M (1998) Some remarks on macroscopic traffic flow modelling. Transp Res Part A 32(5):323–329

    Google Scholar 

  90. Papageorgiou M (ed) (1991) Concise Encyclopedia of Traffic & Transportation Systems. Pergamon

    Google Scholar 

  91. Papageorgiou M, Kotsialos A (2000) Freeway ramp metering: An overview. In: Proceedings of the 3rd Annual IEEE Conference on Intelligent Transportation Systems (ITSC 2000), Dearborn, Michigan, USA, October 2000, pp 228–239

    Google Scholar 

  92. Papageorgiou M, Kotsialos A (2002) Freeway ramp metering: An overview. IEEE Trans Intell Transp Syst 3(4):271–280

    Google Scholar 

  93. Papageorgiou M, Messmer A (1991) Dynamic network traffic assignment and route guidance via feedback regulation. Transp Res Rec 1306:49–58

    Google Scholar 

  94. Papageorgiou M, Blosseville J-M, Hadj-Salem H (1990) Modelling and real-time control of traffic flow on the southern part of Boulevard Périphérique in Paris: Part I: Modelling. Transp Res Part A, 24A(5):345–359

    Google Scholar 

  95. Papageorgiou M, Blosseville J-M, Hadj-Salem H (1990) Modelling and real-time control of traffic flow on the southern part of Boulevard Périphérique in Paris: Part II: Coordinated on-ramp metering. Transp Res Part A, 24A(5):361–370

    Google Scholar 

  96. Papageorgiou M, Hadj-Salem H, Blosseville J-M (1991) ALINEA: A local feedback control law for on-ramp metering. Transp Res Rec (1320):58–64

    Google Scholar 

  97. Papageorgiou M, Hadj-Salem H, Middelham F (1997) ALINEA local ramp metering – summary of field results. Transp Res Rec 1603:90–98

    Google Scholar 

  98. Papageorgiou M, Wang Y, Kosmatopoulos E, Papamichail I (2007) ALINEA maximizes motorway throughput – An answer to flawed criticism. Traffic Eng Control 48(6):271–276

    Google Scholar 

  99. Payne HJ (1971) Models of freeway traffic and control. Simul Counc Proc 1:51–61

    Google Scholar 

  100. Rees I (1995) Orbital decongestant. Highways 63(5):17–18

    MathSciNet  Google Scholar 

  101. Rees T, Harbord B, Dixon C, Abou-Rhame N (2004) Speed‐control and incident detection on the M25 controlled motorway (summary of results 1995–2002). Technical Report PPR033, TRL (UK's Transport Research Laboratory), Wokingham

    Google Scholar 

  102. Richards PI (1956) Shock waves on the highway. Oper Res 4:42–51

    MathSciNet  Google Scholar 

  103. Ritchie SG (1990) A knowledge‐based decision support architecture for advanced traffic management. Transp Res Part A, 24A(1):27–37

    Google Scholar 

  104. Schik P (2003) Einfluss von Streckenbeeinflussungsanlagen auf die Kapazität von Autobahnabschnitten sowie die Stabilität des Verkehrsflusses. Ph.D. Thesis, Universität Stuttgart

    Google Scholar 

  105. Shladover SE, Desoer CA, Hedrick JK, Tomizuka M, Walrand J, Zhang WB, McMahon DH, Peng H, Sheikholesham S, McKeown N (1991) Automatic vehicle control developments in the PATH program. IEEE Trans Veh Technol 40(1):114–130

    Google Scholar 

  106. Sisiopiku VP (2001) Variable speed control: Technologies and practice. In: Proceedings of the 11th Annual Meeting of ITS America, Miami, pp 1–11

    Google Scholar 

  107. Slotine JE, Li W (1991) Applied Nonlinear Control. Prentice Hall, New Jersey

    MATH  Google Scholar 

  108. Smith BL, Zhang H, Fontaine MD, Green MW (2004) Wireless location technology based traffic monitoring: Critical assessment and evaluation of an early‐generation system. J Transp Eng 130(5):576–584

    Google Scholar 

  109. Smulders S (1990) Control of freeway traffic flow by variable speed signs. Transp Res Part B, 24B(2):111–132

    Google Scholar 

  110. Smulders SA, Helleman DE (1998) Variable speed control: State-of-the-art and synthesis. In: Road Transport Information and Control, number 454 in Conference Publication, IEE, 21–23 April 1998, pp 155–159

    Google Scholar 

  111. Soriguera F, Thorson L, Robuste F (2007) Travel time measurement using toll infrastructure. In: Proceedings of the 86th Annual Meeting of the Transportation Research Board, CDROM paper 07-1389.pdf

    Google Scholar 

  112. Taylor CJ, Young PC, Chotai A, Whittaker J (1998) Nonminimal state space approach to multivariable ramp metering control of motorway bottlenecks. IEE Proc – Control Theory Appl 146(6):568–574

    Google Scholar 

  113. Taylor MAP (1999) Dense network traffic models, travel time reliability and traffic management. I: General introduction. J Adv Transp 33(2):218–233

    Google Scholar 

  114. Taylor MAP (1999) Dense network traffic models, travel time reliability and traffic management. II: Application to network reliability. J Adv Transp 33(2):235–251

    Google Scholar 

  115. Taylor MAP, Woolley JE, Zito R (2000) Integration of the global positioning system and geographical information systems for traffic congestion studies. Transp Res Part C, 8C:257–285

    Google Scholar 

  116. Transportation Research Board (1998) Managing speed: Review of current practice for setting and enforcing speed limits. In: TRB Special Report 254, Transportation Research Board, Committee for Guidance on Setting and Enforcing Speed Limits. National Academy Press, Washington

    Google Scholar 

  117. Treiber M, Hennecke A, Helbing D (2000) Congested traffic states in empirical observations and microscopic simulation. Phys Rev E 62:1805–1824

    ADS  Google Scholar 

  118. Tsugawa S, Kato S, Tokuda K, Matsui T, Fujii H (2000) An architecture for cooperative driving of automated vehicles. In: Proceedings of the IEEE Intelligent Transportation Symposium, Dearborn, MI, USA 2000, pp 422–427

    Google Scholar 

  119. Vahidi A, Eskandarian A (2003) Research advances in intelligent collision avoidance and adaptive cruise control. IEEE Trans Intell Transp Syst 4(3):143–153

    Google Scholar 

  120. van Arem B, van Driel CJG, Visser R (2006) The impacts of cooperative adaptive cruise control on traffic‐flow characteristics. IEEE Trans Intell Transp Syst (4):429–436

    Google Scholar 

  121. van den Berg M, Bellemans T, De Schutter B, De Moor B, Hellendoorn J (2005) Control of traffic with anticipative ramp metering. In: Proceedings of the 84th Annual Meeting of the Transportation Research Board, Washington DC, 2005 CDROM paper 05-0252, pp 166–174

    Google Scholar 

  122. van den Hoogen E, Smulders S (1994) Control by variable speed signs: Results of the Dutch experiment. In: Proceedings of the 7th International Conference on Road Traffic Monitoring and Control, IEE Conference Publication No. 391, London, England, 26–28 April 1994, pp 145–149

    Google Scholar 

  123. Varaiya P (1993) Smart cars on smart roads: Problems of control. IEEE Trans Autom Control 38(2):195–207

    MathSciNet  Google Scholar 

  124. Wang Y, Papageorgiou M (2005) Real-time freeway traffic state estimation based on extended Kalman filter: A general approach. Transp Res Part B 39(2):141–167

    Google Scholar 

  125. Wang Y, Papageorgiou M, Messmer A (2003) A predictive feedback routing control strategy for freeway network traffic. In: Proceedings of the 82nd Annual Meeting of the Transportation Research Board, Washington DC, January 2003

    Google Scholar 

  126. Wang Y, Papageorgiou M, Messmer A (2006) RENAISSANCE A unified macroscopic model-based approach to real-time freeway network traffic surveillance. Transp Res Part C, 14C:190–212

    Google Scholar 

  127. Wattleworth JA (1965) Peak‐period analysis and control of a freeway system. Highw Res Rec 157:1–21

    Google Scholar 

  128. Wilkie JK (1997) Using variable speed limit signs to mitigate speed differentials upstream of reduced flow locations. Technical report. Department of Civil Engineering, Texas A& M University, College Station, Prepared for CVEN 677 Advanced Surface Transportation Systems, Report No. SWUTC/97/72840-00003-2

    Google Scholar 

  129. Williams B (1996) Highway control. IEE Rev 42(5):191–194

    Google Scholar 

  130. Wolf DE (1999) Cellular automata for traffic simulations. Physica A 263:438–451

    MathSciNet  ADS  Google Scholar 

  131. Zhang H, Ritchie SG (1994) Real-time decision‐support system for freeway management and control. J Comput Civ Eng 8(1):35–51

    Google Scholar 

  132. Zhang L (2007) Traffic diversion effect of ramp metering at the individual and system levels. In: Proceedings of the 86th Annual Meeting of the Transportation Research Board, Washington DC, January 2007, CDROM paper 07-2087

    Google Scholar 

Books and Reviews

  1. We refer the interested reader to the following references

    Google Scholar 

  2. in the various fields that have been discussed in thischapter:

    Google Scholar 

  3. Control: General introduction [4], optimal control [73], model predictive control [15,79], nonlinear control [107],

    Google Scholar 

  4. Traffic flow modeling: General overviews [48,49], cell transmission model [26], Kerner's three-phase theory [60], microscopic simulation models [2], cellular automata [80],

    Google Scholar 

  5. Ramp metering: Overviews of ramp metering strategies [18,91], field test and simulation studies [39],

    Google Scholar 

  6. Speed limit systems: Overviews of practical speed limit systems [106,128],

    Google Scholar 

  7. Intelligent vehicles: Overview [10],

    Google Scholar 

  8. Sensor technologies: Overviews [32,33,63,64]

    Google Scholar 

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Author information

Authors and Affiliations

  1. TU Delft, Delft, The Netherlands

    A. Hegyi & B. De Schutter

  2. Hasselt University, Diepenbeek, Belgium

    T. Bellemans

Authors
  1. A. Hegyi
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  2. T. Bellemans
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  3. B. De Schutter
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Editor information

Editors and Affiliations

  1. RAMTECH LIMITED, 122 Escalle Lane, Larkspur, CA, 94939, USA

    Robert A. Meyers Ph. D. (Editor-in-Chief) (Editor-in-Chief)

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© 2009 Springer-Verlag

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Hegyi, A., Bellemans, T., De Schutter, B. (2009). Freeway Traffic Management and Control. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387-30440-3_232

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