The Most Preferred Route of the Car Navigation System: A Systems Engineering Approach

Abstract:

The typical route planning of on-board car navigation systems (CNS) attempts to find the shortest route without considering users preferences and driving contexts. However it is more effective for a user to find the most preferred route rather than the shortest one. We propose a systems engineering approach for finding the most preferred route by considering and tracking the requirements of CNS route planning from the business point of view. Our approach consists of 4 baselines: customer baseline, system baseline component baseline, and design baseline. The architecture of a route planning engine is suggested according to the baselines.

• Periodical: Advanced Materials Research (Volumes 712-715)
• Pages: 2680-2685
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.712-715.2680
• Online since: June 2013
• Authors: Kyung Il Choe
• Keywords: Car Navigation System, Most Preferred Route, Route Planning, Systems Engineering

References

[1] H. Kanoh, Dynamic route planning for car navigation systems using virus genetic algorithms, Int. J. Know.-Based Intel. Eng. Sys. 11-1 (2007) 65-78.

DOI: 10.3233/kes-2007-11105

[2] D. Schultes, Route Planning in Road Networks, Ph.D. dissertation, Karlsruhe University, 2008.

[3] F. Wen, M. Gen, Intelligent approach to dynamic route selection in car navigation systems, Asia Conf. Intel. Mfg. Log. Sys. (2008) 107-114.

[4] B.-K. Kim, J.-B. Jo, J.-R. Kim, M. Gen, Optimal route search in car navigation systems by multi-objective genetic algorithms. Int. J. Info. Sys. Log. Mgmt. 4-2 (2009) 9-18.

[5] R. Sedgewick, Algorithms, Addision-Wesley, Massachusetts, 1988.

[6] J. Letchner, J. Krumm, E. Horvitz, Trip router with individualized preferences (TRIP): incorporating personalization into route planning, Proc. IAAI (2006) 1795-1800.

[7] A. Lindkvist, M. Gistell, M. Weimann, P. Geen, DRIVE II Project V2054: Evaluation and Result of the Field Trials, technical report, Swedish National Road Administration, 1995.

[8] F. Svahn, In-car navigation usage: an end-user survey on existing systems, Proc. IRIS 27 (2004).

[9] Y. Jeong, W. Kim, A novel TPEG application for location based service using terrestrial-DMB, IEEE Trans. Cons. Elec. 52-1 (2006) 281-286.

DOI: 10.1109/tce.2006.1605059

[10] K.-I. Choe, A pilot study on TPEG-based route search of the car navigation system (in Korean), J. Info. Ind. Eng. 21-1 (2013) 29-38.

[11] S.-H. Ok, W.-J. Seo, J.-H. Ahn, S. Kang, B. Moon, An ant colony optimization for the preference-based shortest path search, J. Chin. Inst. Engr. 34-2 (2011) 181-196.

DOI: 10.1080/02533839.2011.565574

[12] B. Liu, Route finding by using knowledge about the road network, IEEE Trans. Sys. Man Cyber. – Part A. 29-4 (1997) 436-448.

DOI: 10.1109/3468.594911

[13] J. Yuan, X. Xie, T-Drive: enhancing driving directions with taxi drivers' intelligence, IEEE Trans. Know. Data Eng. 25-1 (2013) 220-232.

DOI: 10.1109/tkde.2011.200

[14] A. Mokhtari, O. Pivert, A. Hadjali, Integrating complex user preferences into a route planner: a fuzzy-set-based approach, Proc. IFSA-EUSFLAT (2009) 501-506.

[15] A. Kossiakoff, W. Sweet, J. Seymour, S. Biemer, Systems Engineering: Principles and Practice, second ed., Wiley, New Jersey, 2011.

[16] D. Verma, Systems Engineering – a Perspective, http://www.incose.org. 2005.

[17] W.J. Larson, D. Kirkpatrick, J.J. Sellers, L.D. Thomas, D. Verma, Applied Space Systems Engineering, McGraw-Hill, Boston, 2009.

[18] S. Rogers, P. Langley, Personalized driving route recommendation, Proc. Amer. Assoc. Arti. Intel. Workshop Recom. Sys. (1998) 96-100.

[19] B. D. Ziebart, A.L. Maas, A.K. Dey, J.A. Bagnell, Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior, Proc. UbiComp '08 (2008) 322-331.

DOI: 10.1145/1409635.1409678

[20] O. Mazhelis, Real-time recognition of personal routes using instance-based learning, IEEE Intel. Vehi. Symp. (2011) 619-624.

DOI: 10.1109/ivs.2011.5940441