# Continuous Fastest Path Planning in Road Networks by Mining Real-Time Traffic Event Information

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5 this network. The length of time points is N t = 24. The average travel time of each user is T avg = 1. The traffic events are designed by three parameters in terms of speed impact E s, recovery time E t, and speed up point E p. The probability of traffic event occurrence is controlled by P TE =.5. The main measurements of all experiments are travel time and latency. Travel time represents the actual travel time of planned path and latency represents the total computation cost from start location to destination. 4.2 Impact of varied traffic event probability This experiment analyzes the travel time and latency when P TE varies. Figure 3 shows that +EP outperforms and in terms of travel time and latency. We observed that the more event occurrences, the better result of +EP. The reason is that the effects of events are predicted by +EP. Conversely, because of more events need to be predicted, the latency also increases when P TE increases. A more interesting finding is that when P TE is very small, the latency of is very high. Because that by P TE increasing, the number of candidate EAF-paths also increases exponentially. Overall, +EP strategy is the best planning strategy under any traffic event probability settings. Travel Time EP Event Probability Latency (Sec.) EP Event probability Figure 3. Travel time and latency with event probability varied. 4.3 Impact of varied the network scale This experiment analyzes the travel time and latency when the network scale varies. Table 2 shows the settings of various road network scales. Figure 4 shows that +EP outperforms and in terms of travel time and latency under various network scales. We observed that by enlarging the network scale, travel time and latency significantly increases. Because that when the network scale enlarges, both of the number of nodes and distances between two nodes also increases. Therefore, the average travel time and frequency of event occurrence are increasing. Overall, +EP strategy is the best planning strategy under any network scales. Table 2. Settings of network scale. Network Scale Network Size 3, 4, 5, 6, 7, # Nodes

6 Travel Time EP Netowrk Scale Letency (Sec.) EP Network Scale Figure 4. Travel time and latency under various network scales. 5. Conclusions and future work In this paper, we have proposed a novel method named Traffic Event Prediction Algorithm (TEPA) for mining the traffic event knowledge which can be used to predict the effects of traffic events. In addition, we have proposed three path planning strategies for continuously planning the fastest path by using real time traffic information. To the best of our knowledge, this is the first work aiming at continuously planning the fastest path while taking consideration both of traffic event knowledge and real time information simultaneously. Through a series of experiments, the proposed method was shown to have excellent performance under various conditions. As to future work, we will try to apply TEPA to other applications such as public vehicle scheduling to enhance the quality of new applications in road networks. References [1] A. Awasthi, Y. Lechevallier, M. Parent, and J. M. Proth. Rule based prediction of fastest paths on urban networks. In Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, September, 25. [2] C. H. Cheong, and M. H. Wong. Mining Popular Paths in a Transportation Database System with Privacy Protection. In Proceedings of the 22nd International Conference on Data Engineering Workshops, April, 26. [3] H. D. Chon, D. Agrawal, and A. El Abbadi. FATES: Finding A Time dependent Shortest path. In Proceedings of the 4th International Conference on Mobile Data Management, January, 23. [4] E. W. Dijkstra. A Note on Two Problems in Connection with Graphs. Numerische Mathematik, Vol. 1, No. 1, December, [5] H. Gonzalez, J. Han, X. Li, M. Myslinska, and J. P. Sondag. Adaptive Fastest Path Computation on a Road Network: A Traffic Mining Approach. In Proceedings of the 14th International Conference on Very Large Data Bases, September, 27. [6] P. E. Hart, N. J. Nilsson, and B. Raphael. A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics, Vol. SSC-4, No. 2, [7] E. Kanoulas, Y. Du, T. Xia, and D. Zhang. Finding Fastest Paths on A Road Network with Speed Patterns. In Proceeding of the 22nd International Conference on Data Engineering, April, 26. [8] N. Lassabe, A. Berro, and Y. Duthen. Improvement of a Shortest Routes Algorithm. In Proceedings of the 1th International IEEE Conference on Intelligent Transportation Systems, September, 27. [9] C. C. Lee, Y. H. Wu, and A. L.-P. Chen. Continuous Evaluation of Fastest Path Queries on Road Networks. International Symposium on Spatial and Temporal Databases, July, 27 [1] E. H.-C. Lu, C.-C. Lin, and V. S. Tseng, Mining the Shortest Path within a Travel Time Constraint in Road Network Environments. In Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems, October, 28. [11] V. S. Tseng and E. H.-C. Lu. Energy-Efficient Real-Time Object Tracking in Multi-Level Sensor Networks by Mining and Predicting Movement Patterns. In Journal of Systems and Software, Volume 82, Issue 4, April, 29. 6

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