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Emerging Freeway Traffic Control Strategies

  • Antonella Ferrara
  • Simona Sacone
  • Silvia Siri
Chapter
Part of the Advances in Industrial Control book series (AIC)

Abstract

Classical freeway traffic control approaches can be conveniently revisited in the light of the new technologies which have revolutionised data collection, data processing, communications and computing. In this chapter, the emerging freeway traffic control paradigms are illustrated, without claiming to be exhaustive, as the emerging control concepts are constantly evolving together with the new technologies on which they are based. The scenarios that unfold on the horizon are incredibly dense with potentialities and opportunities. Traffic data acquisition can be performed supplementing fixed sensors with probe vehicles. The overall traffic flow, even in case of mixed traffic consisting of conventional vehicles and intelligent vehicles, can be influenced by acting in a coordinated way at the level of the single intelligent vehicle. Large amounts of data can be collected, also exploiting unconventional data sources like social networks, making of paramount importance the development of traffic-oriented big data technologies, as well as efficient data mining techniques, in order to distinguish between useful and non-useful data and suitably process them. Privacy-preserving data sharing, cybersecurity, fault-tolerance and resilience concepts also play an important role in this new and challenging scenario.

References

  1. 1.
    Papageorgiou M, Diakaki C, Nikolos I, Ntousakis I, Papamichail I, Roncoli C (2015) Freeway traffic management in presence of vehicle automation and communication systems (VACS). In: Meyer G, Beiker S (eds) Road vehicle automation, vol 2. Springer, pp 205–214Google Scholar
  2. 2.
    Schubert MN (2015) Autonomous cars - initial thoughts about reforming the liability regime. Insurance IssuesGoogle Scholar
  3. 3.
    Daponte P, De Vito L, Picariello F, Rapuano S, Tudosa I (2012) Wireless sensor network for traffic safety. In: Proceedings of the IEEE workshop on environmental energy and structural monitoring systems, pp 42–49Google Scholar
  4. 4.
    Pascale A, Nicoli M, Deflorio F, Dalla Chiara B, Spagnolini U (2012) Wireless sensor networks for traffic management and road safety. IET Intell Transp Syst 6:67–77CrossRefGoogle Scholar
  5. 5.
    Guevara J, Barrero F, Vargas E, Becerra J, Toral S (2012) Environmental wireless sensor network for road traffic applications. IET Intell Transp Syst 6:177–186CrossRefGoogle Scholar
  6. 6.
    Knorr F, Baselt D, Schreckenberg M, Mauve M (2012) Reducing traffic jams via VANETs. IEEE Trans Veh Technol 61:3490–3498CrossRefGoogle Scholar
  7. 7.
    Varaiya P (1993) Smart cars on smart roads: problems of control. IEEE Trans Autom Control 38:195–207MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hedrick JK, Tomizuka M, Varaiya P (1994) Control issues in automated highway systems. IEEE Control Syst 14:21–32CrossRefGoogle Scholar
  9. 9.
    Broucke M, Varaiya P (1996) A theory of traffic flow in automated highway system. Transp Res Part C 4:181–210CrossRefGoogle Scholar
  10. 10.
    Ioannou P (ed) (1997) Automated highway systems. Springer, USGoogle Scholar
  11. 11.
    Alvarez L, Horowitz R, Li P (1999) Traffic flow control in automated highways systems. Control Eng Pract 7:1071–1078CrossRefGoogle Scholar
  12. 12.
    Hartenstein H, Laberteaux KP (2008) A Tutorial survey on vehicular ad hoc networks. IEEE Commun Mag 46:164–171CrossRefGoogle Scholar
  13. 13.
    Watzening D, Horn M (eds) (2017) Automated driving: safer and more efficient future driving. Springer International, BerlinGoogle Scholar
  14. 14.
    Avizienis A, Laprie J-C, Randell B, Landwehr C (2004) Basic concepts and taxonomy of dependable and secure computing. Trans Dependable Sec Comput 1:11–33CrossRefGoogle Scholar
  15. 15.
    SAE On-Road Automated Driving (ORAD) Committee (2014) Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems, Standard: \(J3016_201401\), SAE InternationalGoogle Scholar
  16. 16.
    Hawkins AJ (2017) Waymo is first to put fully self-driving cars on US roads without a safety driver. The Verge, 7 Nov 2017Google Scholar
  17. 17.
    Durbin D-A (2017) FCA, Google begin offering rides in self-driving Pacifica hybrid minivan. Chicago Tribune, 25 April 2017Google Scholar
  18. 18.
    Baskar LD, De Schutter B, Hellendoorn J, Papp Z (2011) Traffic control and intelligent vehicle highway systems: a survey. IET Intell Transp Syst 5:38–52CrossRefGoogle Scholar
  19. 19.
    Rios-Torres J, Malikopoulos AA (2017) A survey on the coordination of connected and automated vehicles at intersections and merging at highway on-ramps. IEEE Trans Intell Transp Syst 18:1066–1067CrossRefGoogle Scholar
  20. 20.
    Sutton R, Barto A (1993) Reinforcement learning: an introduction, MIT Press, Cambridge, MAGoogle Scholar
  21. 21.
    Abdulhai B, Kattan L (2003) Reinforcement learning: introduction to theory and potential for transport applications. Can J Civil Eng 30:981–991CrossRefGoogle Scholar
  22. 22.
    Yang Z, Wen K (2010) Multi-objective optimization of freeway traffic flow via a fuzzy reinforcement learning method. In: Proceedings of the 3rd international conference on advanced computer theory and engineering, vol V5, pp 530–534Google Scholar
  23. 23.
    Swaroop D, Hedrick JK (1996) String stability of interconnected systems. IEEE Trans Autom Control 41:349–357MathSciNetCrossRefGoogle Scholar
  24. 24.
    Liu Y, Özgüner Ü, Acarman T (2006) Performance evaluation of inter-vehicle communication in highway systems and in urban areas. IEE Proc IntellTranspSyst 153:63–75Google Scholar
  25. 25.
    Shladover SE, Nowakowski C, Lu X-Y, Ferlis R (2015) Cooperative adaptive cruise control. Transp Res Rec 2489:145–152CrossRefGoogle Scholar
  26. 26.
    Kim T, Jerath K (2016) Mitigation of self-organized traffic jams using cooperative adaptive cruise control. In: Proceedings of the international conference on connected vehicles and expo, pp 7–12Google Scholar
  27. 27.
    Ferrara A (ed.) (2017) Sliding mode control of vehicle dynamics, IETGoogle Scholar
  28. 28.
    Ferrara A, Vecchio C (2006) Cruise control with collision avoidance for cars via sliding modes. In: Proceedings of the IEEE international conference on control applications, pp 2808–2813Google Scholar
  29. 29.
    Ferrara A, Vecchio C (2008) Second-order sliding mode control of a platoon of vehicles. Int J Model Identif Control 3:277–285CrossRefGoogle Scholar
  30. 30.
    Roncoli C, Papageorgiou M, Papamichail I (2015) Traffic flow optimisation in presence of vehicle automation and communication systems. Part II: optimal control for multi-lane motorways. Transp Res Part C 57:260–275CrossRefGoogle Scholar
  31. 31.
    Ntousakis IA, Nikolos IK, Papageorgiou M (2015) On microscopic modelling of adaptive cruise control systems. Transp Res Procedia 6:111–127CrossRefGoogle Scholar
  32. 32.
    Delis AI, Nikolos IK, Papageorgiou M (2015) Macroscopic traffic flow modeling with adaptive cruise control: development and numerical solution. Comput Math Appl 70:1921–1947MathSciNetCrossRefGoogle Scholar
  33. 33.
    Delis AI, Nikolos IK, Papageorgiou M (2016) Simulation of the penetration rate effects of ACC and CACC on macroscopic traffic dynamics. In: Proceedings of the IEEE 19th international conference on intelligent transportation systems, pp 336–341Google Scholar
  34. 34.
    Van Arem B, Van Driel CJ, Visser R (2006) The impact of cooperative adaptive cruise control on traffic-flow characteristics. IEEE Trans Intell Transp Syst 7:429–436CrossRefGoogle Scholar
  35. 35.
    Ioannou P, Wang Y, Chang H (2007) Integrated roadway/adaptive cruise control system: safety, performance, environmental and near term deployment considerations. California PATH Research Report UCB-ITS-PRR-2007-8Google Scholar
  36. 36.
    Li Y, Xu C, Xing L, Wang W (2017) Integrated cooperative adaptive cruise and variable speed limit controls for reducing rear-end collision risks near freeway bottlenecks based on micro-simulations. IEEE Trans Intell Transp Syst 18:3157–3167CrossRefGoogle Scholar
  37. 37.
    Fagnant DJ (2015) Shared autonomous vehicles: model formulation, sub-problem definitions, implementation details, and anticipated impacts. In: Proceeding of the 2015 American control conference, pp 2593–2593Google Scholar
  38. 38.
    Hartman IB, Keren D, Dbai AA, Cohen E, Knapen L, Yasar AU, Janssens D (2014) Theory and practice in large carpooling problems. Procedia Comput Sci 32:339–347CrossRefGoogle Scholar
  39. 39.
    Celsi LR, Di Giorgio A, Gambuti R, Tortorelli A, Delli Priscoli F (2017) On the many-to-many carpooling problem in the context of multi-modal trip planning. In: Proceedings of the 25th Mediterranean conference on control and automation, pp 303–309Google Scholar
  40. 40.
    European Commission (2013) Trends to 2050: Reference Scenario 2013, ISBN: 978-92-79-33728-4Google Scholar
  41. 41.
    European Automobile Manufacturers Association (ACEA) Position Paper (2016) Reducing CO2 emissions from heavy-duty vehiclesGoogle Scholar
  42. 42.
    Alam A, Besselink B, Turri V, Martensson J, Johansson KH (2015) Heavy-duty vehicle platooning for sustainable freight transportation: a cooperative method to enhance safety and efficiency. IEEE Control Syst 35:34–56MathSciNetCrossRefGoogle Scholar
  43. 43.
    Besselink B, Turri V, van de Hoef S, Liang KY, Alam A, Maartensson J, Johansson KH (2016) Cyber-physical control of road freight transport. Proc IEEE 104:1128–1141CrossRefGoogle Scholar
  44. 44.
    Liang KY, van de Hoef S, Terelius H, Turri V, Besselink B, Mrtensson J, Johansson KH (2016) Networked control challenges in collaborative road freight transport. Eur J Control 30:2–14MathSciNetCrossRefGoogle Scholar
  45. 45.
    Liang KY (2016) Fuel-efficient heavy-duty vehicle platoon formation, Ph.D. Thesis, Kungliga Tekniska Hgskolan KTH, Stockholm, SwedenGoogle Scholar
  46. 46.
    Liang KY, Mrtensson J, Johansson KH (2016) Heavy-duty vehicle platoon formation for fuel efficiency. IEEE Trans Intell Transp Syst 17:1051–1061CrossRefGoogle Scholar
  47. 47.
    Turri V, Besselink B, Johansson KH (2017) Cooperative look-ahead control for fuel-efficient and safe heavy-duty vehicle platooning. IEEE Trans Control Syst Technol 25:12–28CrossRefGoogle Scholar
  48. 48.
    Larson J, Liang KY, Johansson KH (2015) A distributed framework for coordinated heavy-duty vehicle platooning. IEEE Trans Intell Transp Syst 16:419–429CrossRefGoogle Scholar
  49. 49.
    Dijikstra EW (1959) A note on two problems in connexion with graphs. Numerische Mathematik 1:269–271MathSciNetCrossRefGoogle Scholar
  50. 50.
    Dhaou IB (2011) Fuel estimation model for eco-driving and eco-routing. In: Proceedings of the IEEE Intelligent Vehicles Symposium, pp 37–42Google Scholar
  51. 51.
    Muñoz JC, Daganzo CF (2002) Moving bottlenecks: a theory grounded on experimental observation. In: Taylor MAP (ed) Transportation and traffic theory in the 21st century, pp 441–461Google Scholar
  52. 52.
    Delle Monache ML, Goatin P (2016) A numerical scheme for moving bottlenecks in traffic flow. Bull Brazilian Math Soc New Ser 47:605–617MathSciNetCrossRefGoogle Scholar
  53. 53.
    Piacentini G, Goatin P, Ferrara A (2018) Traffic control via moving bottleneck of coordinated vehicles. In: Accepted to 15th IFAC symposium on control in transportation systemsGoogle Scholar
  54. 54.
    Johansson I, Jin J, Ma X, Pettersson H (2017) Look-ahead speed planning for heavy-duty vehicle platoons using traffic information. Transp Res Procedia 22:561–569CrossRefGoogle Scholar
  55. 55.
    Atzori L, Iera A, Morabito G (2010) The Internet of things: a survey. Comput Netw 54:2787–2805CrossRefGoogle Scholar
  56. 56.
    M. Jamshidi (ed.) (2008) Systems of systems engineering: principles and applications. CRC Press, Boca RatonGoogle Scholar
  57. 57.
    Engell S, Paulen R, Reniers MA, Sonntag C, Thompson H (2015) Core research and innovation areas in cyber-physical systems of systems, cyber physical systems. Design, Modeling, and Evaluation. In: Mousavi M, Berger C (eds) Lecture Notes in Computer Science, vol 9361. Springer, ChamCrossRefGoogle Scholar
  58. 58.
    Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (IoT): a vision, architectural elements, and future directions. Future Gener Comput Syst 29:1645–1660CrossRefGoogle Scholar
  59. 59.
    Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M (2015) Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun Surv Tutor 17:2347–2376CrossRefGoogle Scholar
  60. 60.
    Guerrero-ibanez JA, Zeadally S, Contreras-Castillo J (2015) Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies. IEEE Wirel Commun 22:122–128CrossRefGoogle Scholar
  61. 61.
    Yang F, Wang S, Li J, Liu Z, Sun Q (2014) An overview of internet of vehicles. China Commun 11:1–15CrossRefGoogle Scholar
  62. 62.
    Atzori L, Iera A, Morabito G, Nitti M (2012) The social internet of things (SIoT) - when social networks meet the internet of things: concept, architecture and network characterization. Comput Netw 56:3594–3608CrossRefGoogle Scholar
  63. 63.
    Li W, Cassandras CG (2005) Distributed cooperative coverage control of sensor networks. In: Proceedings of the 44th IEEE conference on decision and control and the European control conference, pp 2542–2547Google Scholar
  64. 64.
    Lambrou TP, Panayiotou CG (2009) Collaborative area monitoring using wireless sensor networks with stationary and mobile nodes. EURASIP J Adv Signal Process 2009:750657CrossRefGoogle Scholar
  65. 65.
    Lovisari E, Canudas de Wit C, Kibangou AY (2016) Density/flow reconstruction via heterogeneous sources and optimal sensor placement in road networks. Transp Res Part C 69:451–476CrossRefGoogle Scholar
  66. 66.
    Guerreiro G, Figueiras P, Silva R, Costa R, Jardim-Goncalves R (2016) An architecture for big data processing on intelligent transportation systems. An application scenario on highway traffic flows. In: Proceedings of the IEEE 8th international conference on intelligent systems, pp 65–72Google Scholar
  67. 67.
    Grossman RL, Kamath C, Kegelmeyer P, Kumar V, Namburu R (eds) (2001) Data mining for scientific and engineering applications. Springer Science & Business Media, BerlinGoogle Scholar
  68. 68.
    Kaufman C, Perlman R, Speciner M (2002) Network security, private communication in a public world, 2nd edn. Prentice Hall, Upper Saddle RiverGoogle Scholar
  69. 69.
    Morgan YL (2010) Notes on DSRC & WAVE standards suite. IEEE Commun Surv Tutor 12:504–518CrossRefGoogle Scholar
  70. 70.
    Zhou Y, Chen S, Zhou Y, Chen M, Xiao Q (2015) Privacy-preserving multi-point traffic volume measurement through vehicle-to-infrastructure communications. IEEE Trans Veh Technol 64:5619–5630CrossRefGoogle Scholar
  71. 71.
    Xia Z, Wang X, Sun X, Wang Q (2016) A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. Trans Parallel Distrib Syst 27:340–352CrossRefGoogle Scholar
  72. 72.
    Sherif ABT, Rabieh K, Mahmoud MMEA, Liang X (2017) Privacy-preserving ride sharing scheme for autonomous vehicles in big data era. IEEE Internet of Things J 4:611–618CrossRefGoogle Scholar
  73. 73.
    Jeske T (2013) Floating car data from smartphones: what Google and Waze know about you and how hackers can control traffic. In: Proceedings of the BlackHat EuropeGoogle Scholar
  74. 74.
    Tanenbaum AS (2003) Computer networks. Prentice Hall, Upper Saddle RiverzbMATHGoogle Scholar
  75. 75.
    Laszka A, Potteiger B, Vorobeychik Y, Amin S, Koutsoukos X (2016) Vulnerability of transportation networks to traffic-signal tampering. In: Proceedings of the ACM/IEEE 7th international conference on cyber-physical systemsGoogle Scholar
  76. 76.
    Sullivan J, Novak D, Aultman-Hall L, Scott DM (2010) Identifying critical road segments and measuring system-wide robustness in transportation networks with isolating links: a link-based capacity-reduction approach. Transp Res Part A 44:323–336Google Scholar
  77. 77.
    Jenelius E, Mattsson L-G (2012) Road network vulnerability analysis of area-covering disruptions: a grid-based approach with case study. Transp Res Part A 46:746–760Google Scholar
  78. 78.
    Reilly J, Martin S, Payer M, Bayen AM (2016) Creating complex congestion patterns via multi-objective optimal freeway traffic control with application to cyber-security. Transp Res Part B 91:366–382CrossRefGoogle Scholar
  79. 79.
    Kwon J, Chen C, Varaiya P (1870) Statistical methods for detecting spatial configuration errors in traffic surveillance. Transp Res Rec 2005:124–132Google Scholar
  80. 80.
    Phegley B, Horowitz R, Gomes G (2016) Model-based fault detection among freeway loop sensors. In: Proceedings of the ASME dynamic systems and control conference, vol 2Google Scholar
  81. 81.
    Calver SC, Snelder M (2018) A methodology for road traffic resilience analysis and review of related concepts. Transp A: Transp Sci 14:130–154Google Scholar
  82. 82.
    Snelder M, Van Zuylen HJ, Immers LH (2012) A framework for robustness analysis of road networks for short term variations in supply. Transp Res Part A 46:828–842Google Scholar
  83. 83.
    Albert R, Jeong H, Barabsi A-L (2000) Error and attack tolerance of complex networks. Nature 406:378–382CrossRefGoogle Scholar
  84. 84.
    Tran T, Ha QP (2015) Dependable control systems with internet of things. ISA Trans 59:303–313CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical, Computer and Biomedical EngineeringUniversity of PaviaPaviaItaly
  2. 2.Department of Informatics, Bioengineering, Robotics and Systems EngineeringUniversity of GenoaGenoaItaly

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