access icon free CANS: context-aware traffic estimation and navigation system

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.

Inspec keywords: fuzzy logic; road traffic control; traffic information systems; ubiquitous computing

Other keywords: travel time reduction; local traffic state estimation; vehicle acceleration; dynamic navigation service; CANS; dynamic vehicular navigation systems; traffic congestion estimation; urban roads; vehicle speed; vehicular contextual information; fuzzy logic; real-time traffic information acquisition; global traffic aggregation; mobile crowdsensing technology; context-aware traffic estimation-and-navigation system; local traffic estimation; local traffic information; urban server; Iran; Birjand

Subjects: Knowledge engineering techniques; Mobile, ubiquitous and pervasive computing; Traffic engineering computing

References

    1. 1)
      • 23. Huang, Y., Wang, J., Jiang, C., et al: ‘Vehicular network based reliable traffic density estimation’. Vehicular Technology Conf. (VTC Spring), IEEE, 2016.
    2. 2)
      • 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.
    3. 3)
      • 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.
    4. 4)
      • 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.
    5. 5)
      • 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.
    6. 6)
      • 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.
    7. 7)
      • 27. Ganti, R.K., Ye, F., Lei, H.: ‘Mobile crowdsensing: current state and future challenges’, IEEE Commun. Mag., 2011, 49, (11), pp. 3239.
    8. 8)
      • 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.
    9. 9)
      • 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.
    10. 10)
      • 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.
    11. 11)
      • 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.
    12. 12)
      • 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.
    13. 13)
      • 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.
    14. 14)
      • 2. Hartenstein, H., Laberteaux, K.P.: ‘A tutorial survey on vehicular ad hoc networks’, IEEE Commun. Mag., 2008, 46, (6), pp. 164171.
    15. 15)
      • 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.
    16. 16)
      • 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.
    17. 17)
      • 41. OpenStreetMap homepage. http://www.openstreetmap.org/, accessed April2016.
    18. 18)
      • 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.
    19. 19)
      • 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.
    20. 20)
      • 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.
    21. 21)
      • 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.
    22. 22)
      • 34. Lilly, J.H., ‘corpMamdani Fuzzy Systems’: ‘Fuzzy control and identification’ (John Wiley & Sons, 2010), pp. 2745.
    23. 23)
      • 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.
    24. 24)
      • 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.
    25. 25)
      • 1. Li, F., Wang, Y.: ‘Routing in vehicular ad hoc networks: A survey’, IEEE Veh. Technol. Mag., 2007, 2, (2), pp. 1222.
    26. 26)
      • 39. Dijkstra, E.W.: ‘A note on two problems in connexion with graphs’, Numer. Math., 1959, 1, (1), pp. 269271.
    27. 27)
      • 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.
    28. 28)
      • 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.
    29. 29)
      • 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.
    30. 30)
      • 13. Coifman, B.: ‘Improved velocity estimation using single loop detectors’, Transport. Res. A Policy Pract, 2001, 35, (10), pp. 863880.
    31. 31)
      • 6. Moustafa, H., Zhang, Y.: ‘Vehicular networks: techniques, standards, and applications’ (Auerbach Publications, 2009).
    32. 32)
      • 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.
    33. 33)
      • 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.
    34. 34)
      • 35. Baturone, I., Barriga, A., Jimenez-Fernandez, C., et al: ‘Microelectronic design of fuzzy logic-based systems’ (CRC press, 2000).
    35. 35)
      • 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.
    36. 36)
      • 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.
    37. 37)
      • 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.
    38. 38)
      • 9. Vahdat-Nejad, H., Ramazani, A., Mohammadi, T., et al: ‘A survey on context-aware vehicular network applications’, Veh. Commun., 2016, 3, pp. 4357.
    39. 39)
      • 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.
    40. 40)
      • 7. Olariu, S., Weigle, M.C.: ‘Vehicular networks: from theory to practice’ (CRC Press, 2009).
    41. 41)
      • 25. Dey, A.K.: ‘Understanding and using context’, Personal Ubi. Comp., 2001, 5, (1), pp. 47.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2016.0180
Loading

Related content

content/journals/10.1049/iet-its.2016.0180
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading