The fastest services of path finding were provided by numerous web maps along with local search engines for a long instance. User behavior of driving typically differs in their progressing driving experiences hence a good quality routing service should believe three aspects such as routes, traffic, as well as drivers, which are far away from extent of shortest or fastest path computing. In our approach of finding driving directions, Variance Entropy Clustering method is used to find out allocation of travel time between two landmarks in dissimilar time period. Two stage routing algorithm used to find fastest and safest route to reach destination. Rough and refined are the two routing stage algorithm used. Label setting algorithm used to find shortest distance between source and destination. Interactive voting map matching algorithm used to construct Landmark graph. GPS equipped taxis are employed as mobile sensors searching traffic rhythm of a city in physical world. If instantaneous sensor data are obtainable for several road segments, our method is combined to make available improved routes for end users

Path finding, User behavior, GPS-equipped taxis, Qualified divers. GPS, Driving Directions, Data mining, Spatial databases and GIS, Time-dependent fast route, Taxitrajectories, Road Network

[1] J. Yuan, Y. Zheng, C. Zhang, and X. Xie, �An Interactive-Voting Based MapMatching Algorithm,� Proc. Int�l Conf. Mobile Data Management (MDM), Pages 43-52, ISBN:978-0-7695-4048-1 2010.
[2] Y. Lou, C. Zhang, Y. Zheng, X. Xie, W. Wang, and Y. Huang �Map-Matching for Low-Sampling-Rate GPS Trajectories,� Proc.Int�l Conf. Advances in Geographic Information Systems (GIS), ISBN:978-1-60558-649- 6/09/11, November 4-6, 2009.
[3] J. Yuan, Y. Zheng, C. Zhang, W. Xie, G. Sun, H. Yan, and X. Xie, �T-Drive: Driving Directions Based on Taxi Trajectories,� Proc.18th SIGSPATIAL Int�l Conf. Advances in Geographic Information Systems (GIS), Page No(99-108), ISBN: 978-1-4503-0428-3, 2010.
[4] E. Kanoulas, Y. Du, T. Xia, and D. Zhang, �Finding Fastest Paths on a Road Network with Speed Patterns,� Proc. Int�l Conf. Data Eng. (ICDE), ISBN: 0-7695-2570-9 2006.
[5] Y. Zheng, L. Liu, L. Wang, and X. Xie, �Learning Transportation Mode from Raw GPS Data for Geographic Applications on the Web,� Proc. 17th Int�l Conf. World Wide Web (WWW), 2008.
[6] C. de Fabritiis, R. Ragona, and G. Valenti, �Traffic Estimation and Prediction Based on Real Time Floating Car Data,� Proc. 11th Int�l IEEE Conf. Intelligent Transportation Systems (ITSC �08), pp. 197-203, Oct. 2008.
[7] K. Cooke and E. Halsey, �The Shortest Route through a Network with Time-Dependent Internodal Transit Times,� J. Math. Analysis Applications, vol. 14, pp. 492-498, 1998.
[8] S. Dreyfus, �An Appraisal of Some Shortest-Path Algorithms,� Operations Research, vol. 17, no. 3, pp. 395-412, 1969.
[9] U. Demiryurek, F. Banaei-Kashani, and C. Shahabi, �A Case for Time-Dependent Shortest Path Computation in Spatial Networks,� Proc. Int�l Conf. Advances in Geographic Information Systems (GIS), 2010.
[10] A. Thiagarajan, L. Ravindranath, K. LaCurts, S. Madden, H. Balakrishnan, S. Toledo, and J. Eriksson, �Vtrack: Accurate, Energy-Aware Road Traffic Delay Estimation Using Mobile Phones,� Proc. Seventh ACM Conf. Embedded Networked Sensor Systems, 2009.
[11] Jing Yuan, Yu Zheng, Xing Xie, Guangzhong Sun, "T-Drive: Enhancing Driving Directions with Taxi Drivers` Intelligence", IEEE Transactions on Knowledge & Data Engineering, vol.25, no. 1, pp. 220-232, Jan. 2013, doi:10.1109/TKDE.2011.200.
[12] G. Sivaiah1 and P Krishna Rao �A Comprehensive Survey on Providing Efficient Driving Directions Using GPS and Driver`s Ability� International Journal of Computer Sciences and Engineering, Vol.-2(7), PP (79-82) July 2014, E-ISSN: 2347-2693.