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CANS: context-aware traffic estimation and navigation system

CANS: context-aware traffic estimation and navigation system

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Acquiring real-time traffic information is a basic requirement for dynamic vehicular navigation systems. The majority of the current navigation systems are based on static traffic information. Building on mobile crowdsensing technology, the authors propose context-aware traffic estimation and navigation system (CANS), a context-aware system that can estimate traffic state without any requirement for expensive infrastructure. Using only available equipment, it can provide dynamic navigation service to drivers. The proposed system consists of three main components: local traffic estimation, global traffic aggregation, and navigation. In this system, vehicles estimate local traffic state using vehicular contextual information including speed and acceleration by relying on fuzzy logic, and transmit the information to the urban server. The server integrates the received local traffic information from different vehicles and estimates the global traffic state, providing the traffic-aware navigation system to drivers. CANS performance is evaluated for an urban scenario in a traffic flow in Birjand, Iran. The experiment is conducted for evaluating CANS in both traffic congestion estimation and navigation. The results show an accurate estimation of traffic states along urban roads. Compared with previous approaches, CANS overrides them for its reduced travel time.

References

    1. 1)
      • F. Li , Y. Wang .
        1. Li, F., Wang, Y.: ‘Routing in vehicular ad hoc networks: A survey’, IEEE Veh. Technol. Mag., 2007, 2, (2), pp. 1222.
        . IEEE Veh. Technol. Mag. , 2 , 12 - 22
    2. 2)
      • H. Hartenstein , K.P. Laberteaux .
        2. Hartenstein, H., Laberteaux, K.P.: ‘A tutorial survey on vehicular ad hoc networks’, IEEE Commun. Mag., 2008, 46, (6), pp. 164171.
        . IEEE Commun. Mag. , 6 , 164 - 171
    3. 3)
      • A. Boukerche , H.A. Oliveira , E.F. Nakamura .
        3. Boukerche, A., Oliveira, H.A., Nakamura, E.F., et al: ‘Vehicular ad hoc networks: a new challenge for localization-based systems’, Comput. Commun., 2008, 31, (12), pp. 28382849.
        . Comput. Commun. , 12 , 2838 - 2849
    4. 4)
      • J. Rybicki , B. Scheuermann , W. Kiess .
        4. Rybicki, J., Scheuermann, B., Kiess, W., et al: ‘Challenge: peers on wheels-a road to new traffic information systems’. Proc. of the 13th Annual ACM Int. Conf. on Mobile Computing and Networking (MobiCom), Montreal, Canada, 2007, pp. 215221.
        . Proc. of the 13th Annual ACM Int. Conf. on Mobile Computing and Networking (MobiCom) , 215 - 221
    5. 5)
      • J. Liu , J. Wan , Q. Wang .
        5. Liu, J., Wan, J., Wang, Q., et al: ‘A survey on position-based routing for vehicular ad hoc networks’, Telecommun. Syst., 2016, 62, (1), pp. 1530.
        . Telecommun. Syst. , 1 , 15 - 30
    6. 6)
      • H. Moustafa , Y. Zhang . (2009)
        6. Moustafa, H., Zhang, Y.: ‘Vehicular networks: techniques, standards, and applications’ (Auerbach Publications, 2009).
        .
    7. 7)
      • S. Olariu , M.C. Weigle . (2009)
        7. Olariu, S., Weigle, M.C.: ‘Vehicular networks: from theory to practice’ (CRC Press, 2009).
        .
    8. 8)
      • V. Jindal , P. Bedi .
        8. Jindal, V., Bedi, P.: ‘Vehicular Ad-Hoc networks: introduction, standards, routing protocols and challenges’, Int. J. Comput. Sci. Issues, 2016, 13, (2), pp. 4455.
        . Int. J. Comput. Sci. Issues , 2 , 44 - 55
    9. 9)
      • H. Vahdat-Nejad , A. Ramazani , T. Mohammadi .
        9. Vahdat-Nejad, H., Ramazani, A., Mohammadi, T., et al: ‘A survey on context-aware vehicular network applications’, Veh. Commun., 2016, 3, pp. 4357.
        . Veh. Commun. , 43 - 57
    10. 10)
      • Y. Cho , J. Rice .
        10. Cho, Y., Rice, J.: ‘Estimating velocity fields on a freeway from low-resolution videos’, IEEE Trans. Intell. Transp. Syst., 2006, 7, (4), pp. 463469.
        . IEEE Trans. Intell. Transp. Syst. , 4 , 463 - 469
    11. 11)
      • B.T. Morris , M.M. Trivedi .
        11. Morris, B.T., Trivedi, M.M.: ‘Learning, modeling, and classification of vehicle track patterns from live video’, IEEE Trans. Intell. Transp. Syst., 2008, 9, (3), pp. 425437.
        . IEEE Trans. Intell. Transp. Syst. , 3 , 425 - 437
    12. 12)
      • Y. Xia , X. Shi , G. Song .
        12. Xia, Y., Shi, X., Song, G., et al: ‘Towards improving quality of video-based vehicle counting method for traffic flow estimation’, Signal Process., 2016, 120, pp. 672681.
        . Signal Process. , 672 - 681
    13. 13)
      • B. Coifman .
        13. Coifman, B.: ‘Improved velocity estimation using single loop detectors’, Transport. Res. A Policy Pract, 2001, 35, (10), pp. 863880.
        . Transport. Res. A Policy Pract , 10 , 863 - 880
    14. 14)
      • B. Coifman , S. Dhoorjaty , Z.-H. Lee .
        14. Coifman, B., Dhoorjaty, S., Lee, Z.-H.: ‘Estimating median velocity instead of mean velocity at single loop detectors’, Transport. Res. C Emerging Technol., 2003, 11, (3), pp. 211222.
        . Transport. Res. C Emerging Technol. , 3 , 211 - 222
    15. 15)
      • C.C. Sun , G.S. Arr , R.P. Ramachandran .
        15. Sun, C.C., Arr, G.S., Ramachandran, R.P., et al: ‘Vehicle reidentification using multidetector fusion’, IEEE Trans. Intell. Transp. Syst., 2004, 5, (3), pp. 155164.
        . IEEE Trans. Intell. Transp. Syst. , 3 , 155 - 164
    16. 16)
      • W.L. Leow , D. Ni , H. Pishro-Nik .
        16. Leow, W.L., Ni, D., Pishro-Nik, H.: ‘A sampling theorem approach to traffic sensor optimization’, IEEE Trans. Intell. Transp. Syst., 2008, 9, (2), pp. 369374.
        . IEEE Trans. Intell. Transp. Syst. , 2 , 369 - 374
    17. 17)
      • B.R. Hellinga , L. Fu .
        17. Hellinga, B.R., Fu, L.: ‘Reducing bias in probe-based arterial link travel time estimates’, Transport. Res. C Emerging Technol., 2002, 10, (4), pp. 257273.
        . Transport. Res. C Emerging Technol. , 4 , 257 - 273
    18. 18)
      • Y. Li , M. McDonald .
        18. Li, Y., McDonald, M.: ‘Link travel time estimation using single GPS equipped probe vehicle’. Proc. 5th Int. IEEE Conf. on Intelligent Transportation Systems, 2002, pp. 932937.
        . Proc. 5th Int. IEEE Conf. on Intelligent Transportation Systems , 932 - 937
    19. 19)
      • Y. Zhu , Z. Li , H. Zhu .
        19. Zhu, Y., Li, Z., Zhu, H., et al: ‘A compressive sensing approach to urban traffic estimation with probe vehicles’, IEEE Trans. Mob. Comput., 2013, 12, (11), pp. 22892302.
        . IEEE Trans. Mob. Comput. , 11 , 2289 - 2302
    20. 20)
      • J. Wan , D. Zhang , S. Zhao .
        20. Wan, J., Zhang, D., Zhao, S., et al: ‘Context-aware vehicular cyber-physical systems with cloud support: architecture, challenges, and solutions’, IEEE Commun. Mag., 2014, 52, (8), pp. 106113.
        . IEEE Commun. Mag. , 8 , 106 - 113
    21. 21)
      • R. Bauza , J. Gozalvez , J. Sanchez-Soriano .
        21. Bauza, R., Gozalvez, J., Sanchez-Soriano, J.: ‘Road traffic congestion detection through cooperative vehicle-to-vehicle communications’. Proc. 35th IEEE Conf. on Local Computer Networks (LCN), 2010, pp. 606612.
        . Proc. 35th IEEE Conf. on Local Computer Networks (LCN) , 606 - 612
    22. 22)
      • X. Jiang , D.H. Du .
        22. Jiang, X., Du, D.H.: ‘BUS-VANET: a bus vehicular network integrated with traffic infrastructure’, IEEE Intell. Transport. Syst. Mag., 2015, 7, (2), pp. 4757.
        . IEEE Intell. Transport. Syst. Mag. , 2 , 47 - 57
    23. 23)
      • Y. Huang , J. Wang , C. Jiang .
        23. Huang, Y., Wang, J., Jiang, C., et al: ‘Vehicular network based reliable traffic density estimation’. Vehicular Technology Conf. (VTC Spring), IEEE, 2016.
        . Vehicular Technology Conf. (VTC Spring), IEEE
    24. 24)
      • G.D. Abowd , A.K. Dey , P.J. Brown . (1999)
        24. Abowd, G.D., Dey, A.K., Brown, P.J., et al: ‘Towards a better understanding of context and context-awareness’, in Gellersen, H.-W. (Ed.): ‘Handheld and ubiquitous computing’ (Springer Berlin Heidelberg, 1999), pp. 304307.
        .
    25. 25)
      • A.K. Dey .
        25. Dey, A.K.: ‘Understanding and using context’, Personal Ubi. Comp., 2001, 5, (1), pp. 47.
        . Personal Ubi. Comp. , 1 , 4 - 7
    26. 26)
      • A. Ramazani , H. Vahdat-Nejad .
        26. Ramazani, A., Vahdat-Nejad, H.: ‘A new context-aware approach to traffic congestion estimation’. 4th Int. eConf. on Computer and Knowledge Engineering (ICCKE), IEEE, Mashhad, Iran, 2014, pp. 504508.
        . 4th Int. eConf. on Computer and Knowledge Engineering (ICCKE), IEEE , 504 - 508
    27. 27)
      • R.K. Ganti , F. Ye , H. Lei .
        27. Ganti, R.K., Ye, F., Lei, H.: ‘Mobile crowdsensing: current state and future challenges’, IEEE Commun. Mag., 2011, 49, (11), pp. 3239.
        . IEEE Commun. Mag. , 11 , 32 - 39
    28. 28)
      • B. Guo , Z. Yu , X. Zhou .
        28. Guo, B., Yu, Z., Zhou, X., et al: ‘From participatory sensing to mobile crowd sensing’. IEEE Int. Conf. on Pervasive Computing and Communications Workshops (PERCOM Workshops), Budapest, 2014, pp. 593598.
        . IEEE Int. Conf. on Pervasive Computing and Communications Workshops (PERCOM Workshops) , 593 - 598
    29. 29)
      • I. Leontiadis , G. Marfia , D. Mack .
        29. Leontiadis, I., Marfia, G., Mack, D., et al: ‘On the effectiveness of an opportunistic traffic management system for vehicular networks’, IEEE Trans. Intell. Transp. Syst., 2011, 12, (4), pp. 15371548.
        . IEEE Trans. Intell. Transp. Syst. , 4 , 1537 - 1548
    30. 30)
      • N. Zarei , M.A. Ghayour , S. Hashemi . (2013)
        30. Zarei, N., Ghayour, M.A., Hashemi, S.: ‘Road traffic prediction using context-aware random forest based on volatility nature of traffic flows’, in Selamat, A., Thanh Nguyen, N., Haron, H. (Eds.): ‘Intelligent information and database systems’ (Springer Berlin Heidelberg, 2013), pp. 196205.
        .
    31. 31)
      • P. Raphiphan , A. Zaslavsky , P. Prathombutr .
        31. Raphiphan, P., Zaslavsky, A., Prathombutr, P., et al: ‘Context aware traffic congestion estimation to compensate intermittently available mobile sensors’. Tenth Int. Conf. on Mobile Data Management: Systems, Services and Middleware (MDM'09), IEEE, Taipei, 2009, pp. 405410.
        . Tenth Int. Conf. on Mobile Data Management: Systems, Services and Middleware (MDM'09), IEEE , 405 - 410
    32. 32)
      • N. Kim , H.S. Lee , K.J. Oh .
        32. Kim, N., Lee, H.S., Oh, K.J., et al: ‘Context-aware mobile service for routing the fastest subway path’, Expert Syst. Applic., 2009, 36, (2), pp. 33193326.
        . Expert Syst. Applic. , 2 , 3319 - 3326
    33. 33)
      • Y. Wang , J. Jiang , T. Mu .
        33. Wang, Y., Jiang, J., Mu, T.: ‘Context-aware and energy-driven route optimization for fully electric vehicles via crowdsourcing’, IEEE Trans. Intell. Transp. Syst., 2013, 14, (3), pp. 13311345.
        . IEEE Trans. Intell. Transp. Syst. , 3 , 1331 - 1345
    34. 34)
      • J.H. Lilly . (2010)
        34. Lilly, J.H., ‘corpMamdani Fuzzy Systems’: ‘Fuzzy control and identification’ (John Wiley & Sons, 2010), pp. 2745.
        .
    35. 35)
      • I. Baturone , A. Barriga , C. Jimenez-Fernandez . (2000)
        35. Baturone, I., Barriga, A., Jimenez-Fernandez, C., et al: ‘Microelectronic design of fuzzy logic-based systems’ (CRC press, 2000).
        .
    36. 36)
      • I. Sabek , M. Youssef , A.V. Vasilakos .
        36. Sabek, I., Youssef, M., Vasilakos, A.V.: ‘ACE: an accurate and efficient multi-entity device-free WLAN localization system’, IEEE Trans. Mob. Comput., 2015, 14, (2), pp. 261273.
        . IEEE Trans. Mob. Comput. , 2 , 261 - 273
    37. 37)
      • K. Subbu , C. Zhang , J. Luo .
        37. Subbu, K., Zhang, C., Luo, J., et al: ‘Analysis and status quo of smartphone-based indoor localization systems’, IEEE Wirel. Commun., 2014, 21, (4), pp. 106112.
        . IEEE Wirel. Commun. , 4 , 106 - 112
    38. 38)
      • J. Liu , J. Wan , Q. Wang .
        38. Liu, J., Wan, J., Wang, Q., et al: ‘A time-recordable cross-layer communication protocol for the positioning of vehicular cyber-physical systems’, Future Gener. Comput. Syst., 2016, 56, pp. 438448.
        . Future Gener. Comput. Syst. , 438 - 448
    39. 39)
      • E.W. Dijkstra .
        39. Dijkstra, E.W.: ‘A note on two problems in connexion with graphs’, Numer. Math., 1959, 1, (1), pp. 269271.
        . Numer. Math. , 1 , 269 - 271
    40. 40)
      • D. Krajzewicz , J. Erdmann , M. Behrisch .
        40. Krajzewicz, D., Erdmann, J., Behrisch, M., et al: ‘Recent development and applications of SUMO-simulation of urban mobility’, Int. J. Adv. Syst. Meas., 2012, 5, (3 and 4), pp. 128138.
        . Int. J. Adv. Syst. Meas. , 128 - 138
    41. 41)
      • (2016)
        41. OpenStreetMap homepage. http://www.openstreetmap.org/, accessed April2016.
        .
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