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Autonomic Navigation System Based on Predicted Traffic and VANETs

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Abstract

To avoid an expected traffic jam, drivers make detours based on limited information; however, the majority following the alike routes may result in an unexpected congestion. Conventional navigation approaches are unable to respond to the unexpected congestion because these approaches do not consider the routes taken by other vehicles. Navigation systems that utilize global traffic information can improve gas consumption, CO2 emissions and travel time. Therefore, in this paper, the authors propose an autonomic navigation system (ANS) operating over vehicular ad-hoc networks (VANETs). The proposed ANS adopts a hierarchical algorithm to plan vehicle routes. The proposed ANS imitates the human nervous system when managing the navigation system, in which vehicles monitor traffic via VANETs. Moreover, this paper proposes a time-dependent routing algorithm that uses a novel traffic prediction method based on the routes of vehicles. This paper adopts EstiNet as simulator tool that dominates hundreds or thousands of VANET-based vehicles routing in two maps, Manhattan area, and Taipei city. The results show that the proposed ANS improves the average speed by 60.02 % when compared with the shortest path first (SPF) algorithm and by 15.49 % when compared with the distributed method of a traffic simulation in the Manhattan area. The proposed ANS also improves the average speed by 30.5 % when compared with the SPF algorithm and by 15.8 % when compared with the distributed method of a traffic simulation in the Taipei area. Furthermore, to emulate real environments, there is a scenario in which only a portion of the vehicles complies with the proposed ANS.

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References

  1. Yang, J. Y., Chou, L. D., & Chang, Y. J. (2015). Electric vehicle navigation system based on power consumption. IEEE Transactions on Vehicular Technology, PP(99), 1–1. doi:10.1109/TVT.2015.2477369.

    Google Scholar 

  2. Chou, L.-D., Yang, J.-Y., Hsieh, Y.-C., Chang, D.-C., & Tung, C.-F. (2011). Intersection-based routing protocol for VANETs. Wireless Personal Communications, 60(1), 105–124. doi:10.1007/s11277-011-0257-z.

    Article  Google Scholar 

  3. Chen, L.-W., Shih, H.-W., Tsai, M.-F., & Deng, D.-J. (2015). Finding lane positions of vehicles: Infrastructure-less cooperative lane positioning based on vehicular sensor networks. IEEE Vehicular Technology Magazine, 10(4), 70–80. doi:10.1109/MVT.2015.2479249.

    Article  Google Scholar 

  4. Mun, C., Choi, J., Kim, Y., Baek, M., Seo, G., & Ko, K. (2015). Cell planning and deployment for ieee 802.11p/wave network. IEEE Intelligent Transportation Systems Magazine, 7(4), 49–57. doi:10.1109/MITS.2015.2474976.

    Article  Google Scholar 

  5. Shaghaghi, E., Jalooli, A., Aboki, R., Marefat, A., & Noor, R. M. (2014). Intelligent traffic signal control for urban central using vehicular ad-hoc network. In IEEE Asia Pacific conference on wireless and mobile (pp. 281–286). doi:10.1109/APWiMob.2014.6920297.

  6. Chen, Y., Michael, G. H. B., & Klaus, B. (2007). Reliable pretrip multipath planning and dynamic adaptation for a centralized road navigation system. IEEE Transactions on Intelligent Transportation Systems, 8(1), 14–20. doi:10.1109/TITS.2006.889437.

    Article  Google Scholar 

  7. Fontanelli, S., Bini, E., & Santi, P. (2010). Dynamic route planning in vehicular networks based on future travel estimation. In IEEE vehicular networking conference (pp. 126–133). doi:10.1109/VNC.2010.5698247.

  8. Guha, R. K., & Wai, C. (2009). A distributed traffic navigation system using vehicular communication. In IEEE vehicular networking conference (pp. 1–8). doi:10.1109/VNC.2009.5416381.

  9. Ding, J. W., Wang, C. F., Meng, F. H., & Wu, T. Y. (2010). Real-time vehicle route guidance using vehicle-to-vehicle communication. IET Communications, 4(7), 870–883. doi:10.1049/iet-com.2009.0163.

    Article  MathSciNet  Google Scholar 

  10. Sub-r-pa, C., & Chakraborty, G. (2015). Dynamic distribute route recommendation system for multiple destinations. In International conference on electrical engineering/electronics, computer, telecommunications and information technology (pp. 1–5). doi:10.1109/ECTICon.2015.7207064.

  11. Zhu, T., Kong, X., Lv, W., Zhang, Y., & Du, B. (2010). Travel time prediction for float car system based on time series. In International conference on advanced communication technology (Vol. 2, pp. 1503–1508).

  12. Gan, H. (2010). Graphical route information panel for the urban freeway network in Shanghai, China. IET Intelligent Transport Systems, 4(3), 212–220. doi:10.1049/iet-its.2010.0017.

    Article  Google Scholar 

  13. Yang, M., Liu, Y., & You, Z. (2010). The reliability of travel time forecasting. IEEE Transactions on Intelligent Transportation Systems, 11(1), 162–171. doi:10.1109/TITS.2009.2037136.

    Article  Google Scholar 

  14. Lin, C.-T., & Chou, L.-D. (2013). A novel economy reflecting short-term load forecasting approach. Energy Conversion and Management, 65, 331–342. doi:10.1016/j.enconman.2012.08.001.

    Article  Google Scholar 

  15. Yang, J.-Y., Chou, L.-D., Tung, C.-F., Huang, S.-M., & Wang, T.-W. (2013). Average-speed forecast and adjustment via VANETs. IEEE Transactions on Vehicular Technology, 62(9), 4318–4327. doi:10.1109/TVT.2013.2267210.

    Article  Google Scholar 

  16. Ji, H., Xu, A., Sui, X., & Li, L. (2010). The applied research of Kalman in the dynamic travel time prediction. In International conference on geoinformatics (pp. 1–5). doi:10.1109/GEOINFORMATICS.2010.5567722.

  17. RITA bureau of transportation statistics. (2011). http://www.rita.dot.gov/.

  18. Yang, Y., & Li, B. (2009). Real-time traffic data management for dynamic vehicle navigation system. In International conference on geoinformatics (pp. 1–5). doi:10.1109/GEOINFORMATICS.2009.5292989.

  19. Dobson, S., Sterritt, R., Nixon, P., & Hinchey, M. (2010). Fulfilling the vision of autonomic computing. Computer, 43(1), 35–41. doi:10.1109/MC.2010.14.

    Article  Google Scholar 

  20. Kephart, J. O., & Chess, D. M. (2003). The vision of autonomic computing. Computer, 36(1), 41–50. doi:10.1109/MC.2003.1160055.

    Article  MathSciNet  Google Scholar 

  21. Dobson, S., Denazis, S., Fernández, A., Gaïti, D., Gelenbe, E., Massacci, F., et al. (2006). A survey of autonomic communications. ACM Transactions on Autonomous and Adaptive Systems, 1(2), 223–259. doi:10.1145/1186778.1186782.

    Article  Google Scholar 

  22. Samaan, N., & Karmouch, A. (2009). Towards autonomic network management: An analysis of current and future research directions. IEEE Communications Surveys & Tutorials, 11(3), 22–36. doi:10.1109/SURV.2009.090303.

    Article  Google Scholar 

  23. Heng, L., Work, D. B., & Gao, G. X. (2015). GPS Signal authentication from cooperative peers. IEEE Transactions on Intelligent Transportation Systems, 16(4), 1794–1805. doi:10.1109/TITS.2014.2372000.

    Article  Google Scholar 

  24. Plumet, F., Petres, C., Romero-Ramirez, M. A., Gas, B., & Ieng, S. H. (2015). Toward an autonomous sailing boat. IEEE Journal of Oceanic Engineering, 40(2), 397–407. doi:10.1109/JOE.2014.2321714.

    Article  Google Scholar 

  25. Dzikowicz, B. R., Hefner, B. T., & Leasko, R. A. (2015). Underwater acoustic navigation using a beacon with a spiral wave front. IEEE Journal of Oceanic Engineering, 40(1), 177–186. doi:10.1109/JOE.2013.2293962.

    Article  Google Scholar 

  26. Mahmoud, A., Noureldin, A., & Hassanein, H. S. (2015). VANETs positioning in urban environments: A novel cooperative approach. In IEEE vehicular technology conference (pp. 1–7). doi:10.1109/VTCFall.2015.7391188.

  27. Saremi, F., Fatemieh, O., Ahmadi, H., Wang, H., Abdelzaher, T., Ganti, R., et al. (2016). Experiences with greengps fuel-efficient navigation using participatory sensing. IEEE Transactions on Mobile Computing, 15(3), 672–689. doi:10.1109/TMC.2015.2421939.

    Article  Google Scholar 

  28. Vegni, A. M., & Loscri, V. (2015). A survey on vehicular social networks. IEEE Communications Surveys & Tutorials, 17(4), 2397–2419. doi:10.1109/COMST.2015.2453481.

    Article  Google Scholar 

  29. Hershberger, J., Maxel, M., & Suri, S. (2007). Finding the K shortest simple paths: A new algorithm and its implementation. ACM Transactions on Algorithms, 3(4), 45. doi:10.1145/1290672.1290682.

    Article  MathSciNet  Google Scholar 

  30. Gao, Y. (2010). An improved shortest route algorithm in vehicle navigation system. In International conference on advanced computer theory and engineering (Vol. 2, pp. V2-363–V362-366). doi:10.1109/ICACTE.2010.5579284.

  31. Li, Q., Zeng, Z., & Yang, B. (2009). Hierarchical model of road network for route planning in vehicle navigation systems. IEEE Intelligent Transportation Systems Magazine, 1(2), 20–24. doi:10.1109/MITS.2009.933860.

    Article  Google Scholar 

  32. Kaparias, I., & Bell, M. G. H. (2009). Testing a reliable in-vehicle navigation algorithm in the field. IET Intelligent Transport Systems, 3(3), 314–324. doi:10.1049/iet-its.2008.0075.

    Article  Google Scholar 

  33. Kaparias, I., Bell, M. G. H., & Belzner, H. (2008). A new measure of travel time reliability for in-vehicle navigation systems. Journal of Intelligent Transportation Systems, 12(4), 202–211. doi:10.1080/15472450802448237.

    Article  MATH  Google Scholar 

  34. Tonguz, O. K. (2011). Biologically inspired solutions to fundamental transportation problems. IEEE Communications Magazine, 49(11), 106–115. doi:10.1109/MCOM.2011.6069717.

    Article  Google Scholar 

  35. Alhalabi, S. M., Al-Qatawneh, S. M., & Samawi, V. W. (2008). Developing a route navigation system using genetic algorithm. In International conference on information and communication technologies: From theory to applications (pp. 1–6). doi:10.1109/ICTTA.2008.4529965.

  36. Wei, C.-H., & Lee, Y. (2007). Development of freeway travel time forecasting models by integrating different sources of traffic data. IEEE Transactions on Vehicular Technology, 56(6), 3682–3694. doi:10.1109/TVT.2007.901965.

    Article  Google Scholar 

  37. Song, T., Xia, W., Song, T., & Shen, L. (2010). A cluster-based directional routing protocol in VANET. In IEEE 12th international conference on communication technology (pp. 1172–1175). doi:10.1109/icct.2010.5689132.

  38. Benslimane, A., Taleb, T., & Sivaraj, R. (2011). Dynamic clustering-based adaptive mobile gateway management in integrated VANET-3G heterogeneous wireless networks. IEEE Journal on Selected Areas in Communications, 29(3), 559–570. doi:10.1109/jsac.2011.110306.

    Article  Google Scholar 

  39. Zhang, Y., & Cao, G. (2011). V-PADA: Vehicle-platoon-aware data access in VANETs. IEEE Transactions on Vehicular Technology, 60(5), 2326–2339. doi:10.1109/TVT.2011.2148202.

    Article  Google Scholar 

  40. Blum, J., & Azim, E. (2004). The threat of intelligent collisions. IT Professional, 6(1), 24–29. doi:10.1109/MITP.2004.1265539.

    Article  Google Scholar 

  41. Ding, Q., Li, X., Jiang, M., & Zhou, X. (2010). Reputation management in vehicular ad hoc networks. In International conference on multimedia technology (pp. 1–5). doi:10.1109/ICMULT.2010.5632149.

  42. Umedu, T., Isu, K., Higashino, T., & Toh, C. K. (2010). An intervehicular-communication protocol for distributed detection of dangerous vehicles. IEEE Transactions on Vehicular Technology, 59(2), 627–637. doi:10.1109/TVT.2009.2035041.

    Article  Google Scholar 

  43. Traffic flow theory—A state of the art report. (2011). http://www.tfhrc.gov/its/tft/tft.htm.

  44. Estinet. (2016). http://www.estinet.com/.

  45. OpenStreetMap. (2016). http://www.openstreetmap.org/.

  46. Zhang, G., & Wang, Y. (2011). Optimizing minimum and maximum green time settings for traffic actuated control at isolated intersections. IEEE Transactions on Intelligent Transportation Systems, 12(1), 164–173. doi:10.1109/TITS.2010.2070795.

    Article  Google Scholar 

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Acknowledgments

This work was supported in part by the National Science Council of the Republic of China under Grant NSC 99-2221-E-008-041-MY3.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Central University, Jhongly City, 320, Taoyuan County, Taiwan, ROC

    Jyun-Yan Yang, Li-Der Chou, Li-Ming Tseng & Yi-Ming Chen

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  1. Jyun-Yan Yang
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Correspondence to Li-Der Chou.

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Yang, JY., Chou, LD., Tseng, LM. et al. Autonomic Navigation System Based on Predicted Traffic and VANETs. Wireless Pers Commun 92, 515–546 (2017). https://doi.org/10.1007/s11277-016-3555-7

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