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Transportation Systems: Monitoring, Control, and Security

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Intelligent Monitoring, Control, and Security of Critical Infrastructure Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 565))

Abstract

Transportation is one of the main cornerstones of human civilization which facilitates the movement of people and goods from one location to another. People routinely use several transportation modes, such as road, air, rail and water for their everyday activities. However, the continuous global population increase and urbanization around the globe is pushing transportation systems to their limits. Unquestionably, the road transportation system is the one mostly affected because it is difficult and costly to increase the capacity of existing infrastructure by building or expanding new roads, especially in urban areas. Towards this direction, Intelligent Transportation Systems (ITS) can have a vital role in enhancing the utilization of the existing transportation infrastructure by integrating electronic, sensing, information and communication technologies into a transportation system. However, such an integration imposes major challenges in the monitoring, control and security of transportation systems. This chapter surveys the state of the art and the challenges for the implementation of ITS in road transportation systems with a special emphasis on monitoring, control and security.

This work is partially funded by the European Research Council Advanced Grant FAULT-ADAPTIVE (ERC-2011-AdG-291508).

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Notes

  1. 1.

    The fact that occupancy is measured over a short road section is a practical limitation; ideally, occupancy measurements can be performed with point detectors.

  2. 2.

    It has been found that in transportation networks, the congestion capacity is lower than the maximum capacity of the network by about 5–15 % [83].

  3. 3.

    It is called phantom congestion, because severe congestion is caused with no obvious reason, such as lane closure, merging and accident.

  4. 4.

    The search for parking comprises 30 % of total congestion in downtown areas [90].

  5. 5.

    Yaw stability prevents vehicles from skidding and spinning out.

References

  1. U.S. Energy Information Administration: Total Energy Flow, 2011 (2011)

    Google Scholar 

  2. U. D. o. T. Federal Highway Administration: Urban Congestion Trends—Operations: The Key to Reliable Travel. U.S. Department of Transport, Federal Highway Administration, Washington, DC, USA (2013)

    Google Scholar 

  3. Anas, A., Lindsey, R.: Reducing urban road transportation externalities: road pricing in theory and in practice. Rev. Environ. Econ. Policy 5(1), 66–88 (2011)

    Article  Google Scholar 

  4. Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., Chen, C.: Data-driven intelligent transportation systems: A survey. IEEE Trans. Intell. Transp. Syst. 12(4), 1624–1639 (2011)

    Article  Google Scholar 

  5. Eliasson, J.: A cost-benefit analysis of the Stockholm congestion charging system. Transp. Res. Part A Policy Pract. 43(4), 468–480 (2009)

    Article  Google Scholar 

  6. Leape, J.: The London congestion charge. J. Econ. Perspect. 20(4), 157–176 (2006)

    Article  Google Scholar 

  7. Ezell, S.: Intelligent Transportation Systems. The Information Technology & Innovation Foundation (ITIF), Washington, DC, USA (2010)

    Google Scholar 

  8. Ibanez-Guzmán, J., Laugier, C., Yoder, J.-D., Thrun, S.: Autonomous driving: context and state-of-the-art. In: Eskandarian, A. (ed.) Handbook of Intelligent Vehicles, pp. 1271–1310. Springer, London (2012)

    Chapter  Google Scholar 

  9. Pinto, C.: How autonomous vehicle policy in California and Nevada addresses technological and non-technological liabilities. Intersect Stanf. J. Sci. Technol. Soc. 5, 1−16 (2012)

    Google Scholar 

  10. Mack, C.A.: Fifty years of Moore’s law. IEEE Trans. Semicond. Manuf. 24(2), 202–207 (2011)

    Article  Google Scholar 

  11. Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)

    Article  Google Scholar 

  12. Li, Q., Zhang, T., Yu, Y.: Using cloud computing to process intensive floating car data for urban traffic surveillance. Int. J. Geogr. Inf. Sci. 25(8), 1303–1322 (2011)

    Article  Google Scholar 

  13. Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T.: A survey of mobile phone sensing. IEEE Commun. Mag. 48(9), 140–150 (2010)

    Article  Google Scholar 

  14. World Health Organization: Global status report on road safety 2013: supporting a decade of action (2013)

    Google Scholar 

  15. Malta, L., Miyajima, C., Takeda, K.: A study of driver behavior under potential threats in vehicle traffic. IEEE Trans. Intell. Transp. Syst. 10(2), 201–210 (2009)

    Article  Google Scholar 

  16. Amanna, A.: Overview of IntelliDrive/Vehicle Infrastructure Integration (VII). Technical report. Virginia Tech Transportation Institute (2009)

    Google Scholar 

  17. Frequency of Target Crashes for IntelliDrive Safety Systems. U.S. Department of Transport, National Highway Traffic Safety Administration, Washington, DC, USA (2010)

    Google Scholar 

  18. Cambridge Systematics: Twin cities ramp meter evaluation. Prepared for Minnesota Department of Transportation (2001)

    Google Scholar 

  19. Papamichail, I., Papageorgiou, M., Vong, V., Gaffney, J.: Heuristic ramp-metering coordination strategy implemented at Monash freeway, Australia. Transp. Res. Rec. J. Transp. Res. Board 2178(1), 10–20 (2010)

    Article  Google Scholar 

  20. Cambridge Systematics: Crashes versus Congestion—What’s the Cost to Society? (2011)

    Google Scholar 

  21. Bertozzi, M., Broggi, A., Fascioli, A.: Vision-based intelligent vehicles: State of the art and perspectives. Robot. Auton. Syst. 32(1), 1–16 (2000)

    Article  Google Scholar 

  22. Hall, F.L.: Traffic stream characteristics. In: Lieu, H., (ed.) Traffic Flow Theory. US Federal Highway Administration (1996)

    Google Scholar 

  23. Labry, D., Dolcemascolo, V., Jacob, B., Stanczyk, D.: Piezoelectric sensors for weigh-in-motion systems: sensor behaviour analysis and recommendations. In: 4th International Conference on Weigh-In-Motion, Taipei, Taiwan (2005)

    Google Scholar 

  24. Hsieh, J.-W., Yu, S.-H., Chen, Y.-S., Hu, W.-F.: Automatic traffic surveillance system for vehicle tracking and classification. IEEE Trans. Intell. Transp. Syst. 7(2), 175–187 (2006)

    Article  Google Scholar 

  25. Antoniou, C., Balakrishna, R., Koutsopoulos, H.N.: A synthesis of emerging data collection technologies and their impact on traffic management applications. Eur. Trans. Res. Rev. 3(3), 139–148 (2011)

    Article  Google Scholar 

  26. Calabrese, F., Colonna, M., Lovisolo, P., Parata, D., Ratti, C.: Real-time urban monitoring using cell phones: a case study in Rome. IEEE Trans. Intell. Transp. Syst. 12(1), 141–151 (2011)

    Article  Google Scholar 

  27. Coleri, S., Cheung, S.Y., Varaiya, P.: Sensor networks for monitoring traffic. In: Allerton Conference on Communication, Control and Computing, pp. 32–40 (2004)

    Google Scholar 

  28. Ahmed, H., El-Darieby, M., Abdulhai, B., Morgan, Y.: Bluetooth-and Wi-Fi-based mesh network platform for traffic monitoring. In: Transportation Research Board 87th Annual Meeting (2008)

    Google Scholar 

  29. Mimbela, L., Klein, L., Kent, P., Hamrick, J., Luces, K., Herrera, S.: Summary of Vehicle Detection and Surveillance Technologies used in Intelligent Transportation Systems. Federal Highway Administration’s (FHWA) Intelligent Transportation Systems Program Office, August 2007

    Google Scholar 

  30. Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 743–761 (2012)

    Article  Google Scholar 

  31. Mohan, P., Padmanabhan, V.N., Ramjee, R.: Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, SenSys’08, New York, NY, USA, pp. 323–336. ACM (2008)

    Google Scholar 

  32. Paje, S., Bueno, M., Terán, F., Viñuela, U.: Monitoring road surfaces by close proximity noise of the tire/road interaction. J. Acoust. Soc. Am. 122, 2636 (2007)

    Article  Google Scholar 

  33. Bera, S., Rao, K.V.K.: Estimation of origin-destination matrix from traffic counts: the state of the art. Eur. Trans. Trasporti Europei 49, 2–23 (2011)

    Google Scholar 

  34. Mogelmose, A., Trivedi, M., Moeslund, T.: Vision-based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey. IEEE Trans. Intell. Transp. Syst. 13(4), 1484–1497 (2012)

    Article  Google Scholar 

  35. Herrera, J.C., Bayen, A.M.: Incorporation of Lagrangian measurements in freeway traffic state estimation. Transp. Res. Part B Methodol. 44(4), 460–481 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  37. Work, D.B., Blandin, S., Tossavainen, O.-P., Piccoli, B., Bayen, A.M.: A traffic model for velocity data assimilation. Appl. Math. Res. Express 2010(1), 1–35 (2010)

    MATH  MathSciNet  Google Scholar 

  38. van Hinsbergen, C.P., Schreiter, T., Zuurbier, F.S., Van Lint, J., van Zuylen, H.J.: Localized extended Kalman filter for scalable real-time traffic state estimation. IEEE Trans. Intell. Transp. Syst. 13(1), 385–394 (2012)

    Article  Google Scholar 

  39. Kong, Q.-J., Li, Z., Chen, Y., Liu, Y.: An approach to urban traffic state estimation by fusing multisource information. IEEE Trans. Intell. Transp. Syst. 10(3), 499–511 (2009)

    Article  Google Scholar 

  40. Stathopoulos, A., Karlaftis, M.G.: A multivariate state space approach for urban traffic flow modeling and prediction. Transp. Res. Part C Emerg. Technol. 11(2), 121–135 (2003)

    Article  Google Scholar 

  41. Bar-Gera, H.: Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: a case study from Israel. Transp. Res. Part C Emerg. Technol. 15(6), 380–391 (2007)

    Article  Google Scholar 

  42. Antoniou, C., Koutsopoulos, H.N., Yannis, G.: Dynamic data-driven local traffic state estimation and prediction. Transp. Res. Part C Emerg. Technol. 34, 89–107 (2013)

    Article  Google Scholar 

  43. Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach. Transp. Res. Part C Emerg. Technol. 13(3), 211–234 (2005)

    Article  Google Scholar 

  44. Tyagi, V., Kalyanaraman, S., Krishnapuram, R.: Vehicular traffic density state estimation based on cumulative road acoustics. IEEE Trans. Intell. Transp. Syst. 13(3), 1156–1166 (2012)

    Article  Google Scholar 

  45. Zhang, Y.: How to provide accurate and robust traffic forecasts practically? In: Abdel-Rahim, A. (ed.) Intelligent Transportation Systems. InTech (2012)

    Google Scholar 

  46. Ki, Y.-K., Baik, D.-K.: Vehicle-classification algorithm for single-loop detectors using neural networks. IEEE Trans. Veh. Technol. 55(6), 1704–1711 (2006)

    Article  Google Scholar 

  47. Coifman, B., Kim, S.: Speed estimation and length based vehicle classification from freeway single-loop detectors. Transp. Res. Part C Emerg. Technol. 17(4), 349–364 (2009)

    Article  Google Scholar 

  48. Duarte, M.F., Hen Hu, Y.: Vehicle classification in distributed sensor networks. J. Parallel Distrib. Comput. 64(7), 826–838 (2004)

    Article  Google Scholar 

  49. Urazghildiiev, I., Ragnarsson, R., Ridderstrom, P., Rydberg, A., Ojefors, E., Wallin, K., Enochsson, P., Ericson, M., Lofqvist, G.: Vehicle classification based on the radar measurement of height profiles. IEEE Trans. Intell. Transp. Syst. 8(2), 245–253 (2007)

    Article  Google Scholar 

  50. Bajwa, R., Rajagopal, R., Varaiya, P., Kavaler, R.: In-pavement wireless sensor network for vehicle classification. In: 10th International Conference on Information Processing in Sensor Networks (IPSN), pp. 85–96. IEEE (2011)

    Google Scholar 

  51. Ma, X., Grimson, W.E.L.: Edge-based rich representation for vehicle classification. In: Tenth IEEE International Conference on Computer Vision, vol. 2, pp. 1185–1192. IEEE (2005)

    Google Scholar 

  52. Zhang, B.: Reliable classification of vehicle types based on cascade classifier ensembles. IEEE Trans. Intell. Transp. Syst. 14(1), 322–332 (2013)

    Article  Google Scholar 

  53. Mei, X., Ling, H.: Robust visual tracking and vehicle classification via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 33(11), 2259–2272 (2011)

    Article  Google Scholar 

  54. Shehata, M.S., Cai, J., Badawy, W.M., Burr, T.W., Pervez, M.S., Johannesson, R.J., Radmanesh, A.: Video-based automatic incident detection for smart roads: the outdoor environmental challenges regarding false alarms. IEEE Trans. Intell. Transp. Syst. 9(2), 349–360 (2008)

    Article  Google Scholar 

  55. Veeraraghavan, H., Masoud, O., Papanikolopoulos, N.P.: Computer vision algorithms for intersection monitoring. IEEE Trans. Intell. Transp. Syst. 4(2), 78–89 (2003)

    Article  Google Scholar 

  56. Anagnostopoulos, C.-N., Anagnostopoulos, I.E., Psoroulas, I.D., Loumos, V., Kayafas, E.: License plate recognition from still images and video sequences: A survey. IEEE Trans. Intell. Transp. Syst. 9(3), 377–391 (2008)

    Article  Google Scholar 

  57. Du, S., Ibrahim, M., Shehata, M., Badawy, W.: Automatic License Plate Recognition (ALPR): a state-of-the-art review. IEEE Trans. Circ. Syst. Video Technol. 23(2), 311–325 (2013)

    Article  Google Scholar 

  58. Coifman, B., Beymer, D., McLauchlan, P., Malik, J.: A real-time computer vision system for vehicle tracking and traffic surveillance. Transp. Res. Part C Emerg. Technol. 6(4), 271–288 (1998)

    Article  Google Scholar 

  59. Gao, T., Wang, P., Wang, C., Yao, Z.: Feature particles tracking for moving objects. J. Multimedia 7(6), 408–414 (2012)

    Article  Google Scholar 

  60. Kamijo, S., Matsushita, Y., Ikeuchi, K., Sakauchi, M.: Traffic monitoring and accident detection at intersections. IEEE Trans. Intell. Transp. Syst. 1(2), 108–118 (2000)

    Article  Google Scholar 

  61. Gupte, S., Masoud, O., Martin, R.F., Papanikolopoulos, N.P.: Detection and classification of vehicles. IEEE Trans. Intell. Transp. Syst. 3(1), 37–47 (2002)

    Article  Google Scholar 

  62. Buch, N., Velastin, S.A., Orwell, J.: A review of computer vision techniques for the analysis of urban traffic. IEEE Trans. Intell. Transp. Syst. 12(3), 920–939 (2011)

    Article  Google Scholar 

  63. Trivedi, M.M., Gandhi, T., McCall, J.: Looking-in and looking-out of a vehicle: computer-vision-based enhanced vehicle safety. IEEE Trans. Intell. Transp. Syst. 8(1), 108–120 (2007)

    Article  Google Scholar 

  64. Apostoloff, N., Zelinsky, A.: Vision in and out of vehicles: integrated driver and road scene monitoring. Int. J. Robot. Res. 23(4–5), 513–538 (2004)

    Article  Google Scholar 

  65. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886–893. IEEE (2005)

    Google Scholar 

  66. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. I-511–I-518. IEEE (2001)

    Google Scholar 

  67. Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the 17th IEEE Conference on Computer vision, vol. 2, pp. 1150–1157. IEEE (1999)

    Google Scholar 

  68. Bishop, C.M., Nasrabadi, N.M.: Pattern recognition and machine learning, vol. 1. springer, New York (2006)

    MATH  Google Scholar 

  69. Geronimo, D., Lopez, A.M., Sappa, A.D., Graf, T.: Survey of pedestrian detection for advanced driver assistance systems. IEEE Trans. Pattern Anal. Mach. Intell. 32(7), 1239–1258 (2010)

    Article  Google Scholar 

  70. Gandhi, T., Trivedi, M.M.: Pedestrian collision avoidance systems: a survey of computer vision based recent studies. In: IEEE Intelligent Transportation Systems Conference (ITSC), pp. 976–981. IEEE (2006)

    Google Scholar 

  71. Gandhi, T., Trivedi, M.M.: Pedestrian protection systems: issues, survey, and challenges. IEEE Trans. Intell. Transp. Syst. 8(3), 413–430 (2007)

    Article  Google Scholar 

  72. Sivaraman, S., Trivedi, M.: Looking at vehicles on the road: a survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Trans. Intell. Transp. Syst. 14(4), 1773–1795 (2013)

    Article  Google Scholar 

  73. Sun, Z., Bebis, G., Miller, R.: On-road vehicle detection: a review. IEEE Trans. Pattern Anal. Mach. Intell. 28(5), 694–711 (2006)

    Article  Google Scholar 

  74. McCall, J.C., Trivedi, M.M.: Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation. IEEE Trans. Intell. Transp. Syst. 7(1), 20–37 (2006)

    Article  Google Scholar 

  75. Kim, Z.: Robust lane detection and tracking in challenging scenarios. IEEE Trans. Intell. Transp. Syst. 9(1), 16–26 (2008)

    Article  Google Scholar 

  76. Dong, Y., Hu, Z., Uchimura, K., Murayama, N.: Driver inattention monitoring system for intelligent vehicles: a review. IEEE Trans. Intell. Transp. Syst. 12(2), 596–614 (2011)

    Article  Google Scholar 

  77. Faouzi, N.-E.E., Leung, H., Kurian, A.: Data fusion in intelligent transportation systems: progress and challenges—a survey. Inf. Fusion 12(1), 4–10 (2011)

    Article  Google Scholar 

  78. Wang, T., Cardone, G., Corradi, A., Torresani, L., Campbell, A.T.: WalkSafe: a pedestrian safety app for mobile phone users who walk and talk while crossing roads. In: Proceedings of the Twelfth Workshop on Mobile Computing Systems and Applications, p. 5. ACM (2012)

    Google Scholar 

  79. You, C.-W., Lane, N.D., Chen, F., Wang, R., Chen, Z., Bao, T.J., Montes-de Oca, M., Cheng, Y., Lin, M., Torresani, L., et al.: CarSafe app: alerting drowsy and distracted drivers using dual cameras on smartphones. In: Proceeding of the 11th annual international conference on Mobile systems, applications, and services, pp. 13–26. ACM (2013)

    Google Scholar 

  80. Baskar, L.D., De Schutter, B., Hellendoorn, J., Papp, Z.: Traffic control and intelligent vehicle highway systems: a survey. IET Intell. Trans. Syst. 5(1), 38–52 (2011)

    Article  Google Scholar 

  81. Tideman, M., van Der Voort, M., van Arem, B., Tillema, F.: A review of lateral driver support systems. In: Proceedings of the 10th IEEE Intelligent Transportation Systems Conference (ITSC), Seattle, Washington, USA, pp. 992–999. IEEE (2007)

    Google Scholar 

  82. Kim, S., Lewis, M.E., White III, C.C.: Optimal vehicle routing with real-time traffic information. IEEE Trans. Intell. Transp. Syst. 6(2), 178–188 (2005)

    Article  Google Scholar 

  83. Chung, K., Rudjanakanoknad, J., Cassidy, M.J.: Relation between traffic density and capacity drop at three freeway bottlenecks. Transp. Res. Part B Methodol. 41(1), 82–95 (2007)

    Article  Google Scholar 

  84. Koonce, P., Rodegerdts, L., Lee, K., Quayle, S., Beaird, S., Braud, C., Bonneson, J., Tarnoff, P., Urbanik, T.: Traffic signal timing manual. Technical report (2008)

    Google Scholar 

  85. Keyvan-Ekbatani, M., Kouvelas, A., Papamichail, I., Papageorgiou, M.: Exploiting the fundamental diagram of urban networks for feedback-based gating. Transp. Res. Part B Methodol. 46(10), 1393–1403 (2012)

    Article  Google Scholar 

  86. Papageorgiou, M., Diakaki, C., Dinopoulou, V., Kotsialos, A., Wang, Y.: Review of road traffic control strategies. Proc. IEEE 91(12), 2043–2067 (2003)

    Article  Google Scholar 

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

    Article  Google Scholar 

  88. Flynn, M.R., Kasimov, A.R., Nave, J.-C., Rosales, R.R., Seibold, B.: Self-sustained nonlinear waves in traffic flow. Phys. Rev. E 79(5), 056113 (2009)

    Article  MathSciNet  Google Scholar 

  89. Papageorgiou, M., Kosmatopoulos, E., Papamichail, I.: Effects of variable speed limits on motorway traffic flow. Transp. Res. Rec. J. Transp. Res. Board 2047(1), 37–48 (2008)

    Article  Google Scholar 

  90. Geng, Y., Cassandras, C.: New “Smart Parking” system based on resource allocation and reservations. IEEE Trans. Intell. Transp. Syst. 14, 1129–1139 (2013)

    Article  Google Scholar 

  91. Xuan, Y., Argote, J., Daganzo, C.F.: Dynamic bus holding strategies for schedule reliability: optimal linear control and performance analysis. Transp. Res. Part B Methodol. 45(10), 1831–1845 (2011)

    Article  Google Scholar 

  92. Fajardo, D., Au, T.-C., Waller, S.T., Stone, P., Yang, D.: Automated intersection control: performance of a future innovation versus current traffic signal control. Transp. Res. Rec. J. Transp. Res. Board 2259(1), 223–232 (2011)

    Article  Google Scholar 

  93. Kavathekar, P., Chen, Y.: Vehicle platooning: a brief survey and categorization. In: Proceedings of the ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (2011)

    Google Scholar 

  94. Chen, B., Cheng, H.H.: A review of the applications of agent technology in traffic and transportation systems. IEEE Trans. Intell. Transp. Syst. 11(2), 485–497 (2010)

    Article  Google Scholar 

  95. Rajamani, R.: Vehicle Dynamics and Control. Springer, Berlin (2011)

    Google Scholar 

  96. Timotheou, S., Panayiotou, C.G., Polycarpou, M.M.: Towards distributed online cooperative traffic signal control using the cell transmission model. In: Proceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems, The Hague, Netherlands, pp. 1737–1742. IEEE (2013)

    Google Scholar 

  97. Zhao, D., Dai, Y., Zhang, Z.: Computational intelligence in urban traffic signal control: A survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(4), 485–494 (2012)

    Article  Google Scholar 

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

    Google Scholar 

  99. Farges, J.L., Henry, J.J., Tufal, J.: The PRODYN real time traffic algorithm. In: Proceedings of the 4th IFAC/IFIP/IFORS Symposium on Control in Transportation Systems, Baden-Baden, Germany (1983)

    Google Scholar 

  100. Mirchandani, P., Head, L.: A real-time traffic signal control system: architecture, algorithms, and analysis. Transp. Res. Part C Emerg. Technol. 9(6), 415–432 (2001)

    Article  Google Scholar 

  101. Bingham, E.: Reinforcement learning in neurofuzzy traffic signal control. Eur. J. Oper. Res. 131(2), 232–241 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  102. Srinivasan, D., Choy, M.C., Cheu, R.L.: Neural networks for real-time traffic signal control. IEEE Trans. Intell. Transp. Syst. 7(3), 261–272 (2006)

    Article  Google Scholar 

  103. Abdulhai, B., Pringle, R., Karakoulas, G.J.: Reinforcement learning for true adaptive traffic signal control. J. Transp. Eng. 129(3), 278–285 (2003)

    Article  Google Scholar 

  104. El-Tantawy, S., Abdulhai, B., Abdelgawad, H.: Multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC): methodology and large-scale application on downtown Toronto. IEEE Trans. Intell. Transp. Syst. 14(3), 1140–1150 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  106. Gomes, G., Horowitz, R.: Optimal freeway ramp metering using the asymmetric cell transmission model. Transp. Res. Part C Emerg. Technol. 14(4), 244–262 (2006)

    Article  Google Scholar 

  107. Cox, A., Prager, F., Rose, A.: Transportation security and the role of resilience: a foundation for operational metrics. Transp. Policy 18(2), 307–317 (2011)

    Article  Google Scholar 

  108. Jenkins, B.M., Butterworth, B.R.: Explosives and Incendiaries Used in Terrorist Attacks on Public Surface Transportation: A Preliminary Empirical Examination. Technical Report. Mineta Transportation Institute, College of Business, San Jose State University (2010)

    Google Scholar 

  109. Abkowitz, M., List, G., Radwan, A.E.: Critical issues in safe transport of hazardous materials. J. Transp. Eng. 115(6), 608–629 (1989)

    Article  Google Scholar 

  110. Kara, B.Y., Verter, V.: Designing a road network for hazardous materials transportation. Transp. Sci. 38(2), 188–196 (2004)

    Article  Google Scholar 

  111. Gillani, S., Shahzad, F., Qayyum, A., Mehmood, R.: A survey on security in vehicular ad hoc networks. Communication Technologies for Vehicles, pp. 59–74, Springer, Berlin (2013)

    Google Scholar 

  112. Tchepnda, C., Moustafa, H., Labiod, H., Bourdon, G.: Security in vehicular networks. In: Moustafa, H., Zhang, Y. (eds.), Vehicular Networks, Techniques, Standards and Applications, pp. 331–353. CRC Press, Boca Raton (2009)

    Google Scholar 

  113. Loukas, G., Gan, D., Vuong, T.: A review of cyber threats and defence approaches in emergency management. Future Internet 5(2), 205–236 (2013)

    Article  Google Scholar 

  114. Koscher, K., Czeskis, A., Roesner, F., Patel, S., Kohno, T., Checkoway, S., McCoy, D., Kantor, B., Anderson, D., Shacham, H., et al.: Experimental security analysis of a modern automobile. In: Proceedings of the IEEE Symposium on Security and Privacy (SP), pp. 447–462. IEEE (2010)

    Google Scholar 

  115. O’Hanlon, B.W., Psiaki, M.L., Bhatti, J.A., Shepard, D.P., Humphreys, T.E.: Real-time GPS spoofing detection via correlation of encrypted signals. Navigation 60(4), 267–278 (2013)

    Article  Google Scholar 

  116. Jensen, M., Gruschka, N., Herkenhöner, R.: A survey of attacks on web services. Comput. Sci.-Res. Dev. 24(4), 185–197 (2009)

    Article  Google Scholar 

  117. Kaufman, C., Perlman, R., Speciner, M.: Network Security: Private Communication in a Public World. Prentice Hall Press, Upper Saddle River (2002)

    Google Scholar 

  118. Golle, P., Greene, D., Staddon, J.: Detecting and correcting malicious data in VANETs. In Proceedings of the 1st ACM international workshop on Vehicular ad hoc networks, pp. 29–37. ACM (2004)

    Google Scholar 

  119. Chen, C., Zhang, J., Cohen, R., Ho, P.-H.: A trust modeling framework for message propagation and evaluation in VANETs. In: Proceedings of the 2nd International Conference on Information Technology Convergence and Services (ITCS), pp. 1–8. IEEE (2010)

    Google Scholar 

  120. Patwardhan, A., Joshi, A., Finin, T., Yesha, Y.: A data intensive reputation management scheme for vehicular ad hoc networks. In; Proceedings of the Third Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, pp. 1–8. IEEE (2006)

    Google Scholar 

  121. Minhas, U.F., Zhang, J., Tran, T., Cohen, R.: Towards expanded trust management for agents in vehicular ad hoc networks. Int. J. Comput. Intell. Theory Pract. 5(1), 3–15 (2010)

    Google Scholar 

  122. Ma, S., Wolfson, O., Lin, J.: A survey on trust management for intelligent transportation systems. In: Proceedings of the 4th ACM SIGSPATIAL International Workshop on Computational Transportation Science, pp. 18–23. ACM (2011)

    Google Scholar 

  123. Warner, J.S., Johnston, R.G.: GPS spoofing countermeasures. Homel. Secur. J.10, 22–30 (2003)

    Google Scholar 

  124. ETSI: Intelligent Transport Systems (ITS) security: threat, vulnerability and risk analysis. Technical report, European Telecommunications Standards Institute, Sophia, Antipolis, France (2010)

    Google Scholar 

  125. Xiong, H., Guan, Z., Hu, J., Chen, Z.: Anonymous authentication protocols for vehicular ad hoc networks: an overview. In: Sen, J. (ed.) Applied Cryptography and Network Security, p. 53. Intech, Rijeka, Croatia (2012)

    Google Scholar 

  126. Zhao, M., Walker, J., Wang, C.-C.: Security challenges for the intelligent transportation system. In: Proceedings of the First International Conference on Security of Internet of Things, pp. 107–115. ACM (2012)

    Google Scholar 

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Authors and Affiliations

  1. KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus

    Stelios Timotheou, Christos G. Panayiotou & Marios M. Polycarpou

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  1. Stelios Timotheou
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  2. Christos G. Panayiotou
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  3. Marios M. Polycarpou
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Correspondence to Stelios Timotheou .

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  1. KIOS Research Center for Intelligent Systems and Networks, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus

    Elias Kyriakides

  2. KIOS Research Center for Intelligent Systems and Networks, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus

    Marios Polycarpou

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Timotheou, S., Panayiotou, C.G., Polycarpou, M.M. (2015). Transportation Systems: Monitoring, Control, and Security. In: Kyriakides, E., Polycarpou, M. (eds) Intelligent Monitoring, Control, and Security of Critical Infrastructure Systems. Studies in Computational Intelligence, vol 565. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44160-2_5

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