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

Transcription

1 Continuous Fastest Path Planning in Road Networks by Mining Real-Time Traffic Event Information Eric Hsueh-Chan Lu Chi-Wei Huang Vincent S. Tseng Institute of Computer Science and Information Engineering National Cheng-Kung University, Tainan, Taiwan, R.O.C. Abstract In recent years, a number of studies had been done on the issues of fastest navigation path planning due to wide applications. Most of previous studies focused on the fastest path planning by mining historical traffic logs. However, the real time traffic situations in the road network always vary continuously due to the occurrences of traffic events. Therefore, a better planning strategy should take into account the effects of traffic events to avoid the traffic congestions. In this paper, we propose a novel prediction-based method named Traffic Event Prediction Algorithm (TEPA) for mining the traffic event knowledge which can be used to predict the effects of traffic events from historical traffic logs. In addition, we propose three continuous path planning strategies for finding the fastest path according to the real time traffic information. Finally, through a series of experiments, the proposed method was shown to have excellent performance under various system conditions. Keywords: Navigation system; Fastest path planning; Traffic event; Data mining. 1. Introduction Global Positioning System (GPS) related devices have developed quickly and the services on the devices have gained more and more. Due to Google develops G-Phone as the beginning of GPS device services. Hence, we observe that GPS related services in the road networks are expected to be more popular in near futures. When mobile users move among the road networks, their moving paths and the traffic situations will be recorded in the historical traffic log in which many useful information can be found. The information can help us to improve several GPS navigation applications such as navigation systems, traffic analyses, and traffic predictions. Although a number of studies had discussed the issue of path planning strategies which include the shortest path and the fastest path in the road networks. However, the shortest path may not always be the fastest path because of the road network is dynamic. Therefore, the real time information such as traffic events should be considered when the system is planning the navigation paths. Unfortunately, the planning strategy may fall into the local optimal problem even though the real time information has been considered. For example, the location along a path has a traffic event. We may choose to avoid it and run other paths. However, the events may be eliminated before the user arrived here. Therefore, a better planning strategy should take into account the effects of traffic events and real time information, simultaneously. 1

2 In this paper, we propose 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, three path planning strategies are proposed to continuously find the fastest path by using real time traffic information. The contributions of this paper are as follows: First, TEPA is a parameter-less and automatic algorithm for discovering the effects of traffic events. Secondly, the proposed path planning strategy achieves lower computation cost which can fit for mobile devices. Through empirical evaluations under various system conditions, the proposed method is shown to perform excellently in terms of precise planning and system performance. The remaining of this paper is organized as follows. We briefly review the related work in Section 2. In Section 3, we describe the proposed algorithm, namely TEPA. The empirical evaluation for performance study is made in Section 4. The conclusions and future work are given in Section Related work Most of studies had discussed the path planning problems which include the shortest path [4][6][8] and the fastest path [1][3][7][1] in the road networks. In [4], Dijkstra proposed Dijkstra s algorithm to find the shortest distance path between two nodes. In [6], Hart proposed A* algorithm which uses a heuristic function to evaluate from starting location to destination location. The main concept of fastest path is to transform the edge cost from road distance to time related factor. In [1], Awasthi proposed a rule based method for evaluating the fastest path on urban networks. In [7], Kanoulas proposed a traffic speed pattern named CapeCod by classifying the time based on traffic flow. The paper used A* algorithm to solve the fastest problem in various departure time. In [1], Lu proposed a mining algorithm for travel time evaluation. The paper used the mined knowledge to predict the future traffic situations. In recent years, a number of studies have been made about real time path navigations [2][5][9]. The effect factors for road situations during navigation query will be different. In [2], Cheong proposed the popular path. In [5], Gonzalez considered some environmental factors that may reflect the influence of the velocity of vehicle, and then these factors are used to build the decision tree. In [9], Lee proposed a strategy used the graphic bounding concept to reduce the computation cost in real time navigation. In [11], Tseng thought a better mobile mechanism should take into account both of precise prediction and real-time efficiency. 3. Proposed method In this section, we describe the proposed method for recommending the fastest navigation path in details. The main system framework consists of two phases: traffic event mining and fastest path planning. First, The TEPA algorithm is proposed to mine the traffic event knowledge. Next, the mined knowledge can be used to predict the effect of traffic event. Finally, the fastest path is continuously planned by integrating the real time traffic information and the traffic event knowledge, when mobile users input queries. 2

3 3.1 The TEPA algorithm The objective of TEPA algorithm is to mine the knowledge of all traffic events. The TEPA algorithm is divided into four steps: 1) Clustering of road networks, 2) Determining the length of event recovery time, 3) Filtering of noise information, and 4) Determining the sequence of event effect. The TEPA algorithm is shown in Figure 1 and the main steps are described as follows. Table 1. An example of traffic log. Road Traffic Sequence Event Sequence AB 49, 45, 41, 46, 5, 52, 51, 49, 3, 47, 49, 5 (1, 4, a), (8, 1, b) AC 6, 61, 62, 57, 54, 52, 58, 62, 61, 6, 61, 62 (3, 7, a) AD 55, 57, 51, 47, 52, 57, 56, 35, 54, 55, 56, 54 (2, 5, a), (7, 9, b) BD 65, 44, 63, 65, 64, 66, 65, 4, 3, 43, 64, 65 (1, 3, b), (7, 1, a) In the first step, all of the roads in the network would be clustered into several clusters according to their road scales. For example, all of the freeways would be clustered into a cluster because their scales are the same. The purpose of this step is to discriminate the different effects on the different scales of roads for any of the events. Table 1 shows a traffic log example of a road cluster which contains 4 roads, where the column of traffic sequence represents the average velocities at 12 time points and the column of event sequence represents the event occurrence time, event elimination time, and event type. For example, the item (1, 4, a) in Table 1 represents the road AB occurs the event a between time interval 1 to 4. In the second step, all of event recovery time in a road cluster would be determined by choosing the most frequent length of recovery time. In Table 1, the number of event a occurrences is 4, and the lengths are 4, 5, 4, and 4, respectively. Therefore, the recovery time of event a is 4. In the third step, the traffic sequences whose velocity trends are significant dissimilar to other sequences would be pruned. In Table 1, there are 3 event a sequences whose recovery time lengths are 4, and the corresponding velocity sequences are {49, 45, 41, 46}, {57, 51, 47, 52}, and {65, 4, 3, 43}, respectively. The sequence {65, 4, 3, 43} would be pruned, the reason is that the average dissimilarity measured by cosine similarity between itself and other sequences is too large. Therefore, only sequences {49, 45, 41, 46} and {57, 51, 47, 52} provide the knowledge for event a. In the final step, the sequence of the event effect can be mined by averaging the trends of velocity sequences. In Table 1, the trends are {-4, -4, 5} for {49, 45, 41, 46} and {-6, -4, 5} for {57, 51, 47, 52}. The sequence of event a s effect is {-5, -4, 5}. The event knowledge which includes recovery time lengths and sequences of event effects in every road clusters would be mined by the processes of step 2 to step 4. After obtaining all event knowledge, the knowledge can be used to predict the effect of traffic event. For example, if event a occurred to the road AB at time point 2 and the current velocity of road AB is 55, the following changes of velocity can be predicted as {5, 46, 51} because of the effect knowledge for event a is {-5, -4, 5}. The predictive mechanism can improve the quality of navigation path planning. 3

4 TEPA Algorithm 1: Clustering of roads in the network. 2: For Each road cluster c 3: For Each traffic event e 4: Determine the recovery time length of e in c 5: For Each sequence s for e in c from traffic log 6: Filtering noise sequences 7: Determine the effect of e in c 8: Add effect of e in c to event knowledge base 9: Return event knowledge base Figure 1. The TEPA algorithm. B A C D G Event 1 I H E F Event 2 Event 3 J Figure 2. Traffic event occurrences. 3.2 Continuous fastest path planning strategies After obtaining the event knowledge, next step is to plan the fastest path when mobile users input a destination. We propose three continuous path planning strategies: 1) Brute Force (), 2) Bounding Path (), and 3) Bounding Path with Event Prediction (+EP). Figure 2 shows an example for traffic event occurrence in the road network. The start location is A. The destination is J. Assume that all of the three events occurred when the user has been arrived at location C. Before explaining the planning strategies, a new navigation path called Event Avoidance Fastest Path (EAF-Path) is defined as the fastest path chosen from the paths whose number of event occurrences as less as possible. The initial paths of all strategies are EAF-Paths ACEHJ. When the user has been arrived at location C, the three traffic events occur in the network. strategy always recalculates the new EAF-path according to the current traffic information immediately. In Figure 2, all of the events would make working. When any of traffic events occur in the location passed by the user, strategy recalculates the new EAF-path immediately. In Figure 2, only Event 2 would make working. Under the same condition with strategy, +EP strategy recalculates the fastest path planned by the event knowledge mined by TEPA algorithm instead of the EAF-path. In Figure 2, +EP strategy will predict the effect of Event 2 and decide avoid it or not, but strategy will always avoid it. 4. Experimental results In this section, we conducted a series of experiments to evaluate the performance for the proposed TEPA algorithm and path planning strategies under various system conditions. All of the experiments implemented in Java JDK 1.5 on an Intel Xeon CPU X GHz machine with 4GB of memory running Microsoft Windows XP. 4.1 Simulation model To evaluate the performance of the proposed methods, we used a simulator that simulates a road network generate the workload data. In the base experimental model, the network is modeled as a road network with size N s = 5, * 5,. There are N n = 1 nodes in this network. The load of traffic congestion is controlled by λ = 2 [3]. Each user in the network may move by adhering to a certain event (P E =.5) or randomly [1]. There are N L = 1k users in 4

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