Long Query Period Study

The trajectory data of all previous experiments are recorded every 5 seconds. In this exper-iment, we would like to examine the performance of the CQ-DFS framework when the data period is longer. We simply retain the odd points and omit the even points of each trajectory that generates a dataset with 10 seconds per period. The performance of the experiment is reported in Figure 5.7. We apply the query range size between 70K and 140K m² where 140K m² query range size usually covers around 80 road segments, which takes around 9.5 seconds of response time. Any larger size would be too much for the dataset with 10 sec-onds per period. From Figure 5.7, we observe that in Figures 5.7(a) and 5.7(b), DynamicFan performs the best, followed by StaticFan120, Circular, and StaticFan45 one after another. In Figures 5.7(c) and 5.7(d), CSFan, SFan, and Fan perform better than FanA_ci and FanD_ci. All average recalls of each shape could only achieve at most 0.8 even when the query size reaches 140K m², which reveals that the current long period dataset is much more complicated than the original one. This is acceptable because user’s location reporting becomes more scattered,

(a) Avg. precision of different shapes.

(b) Avg. recall of different shapes.

(c) Avg. precision of different fans. (d) Avg. recall of different fans.

Figure 5.7: Experimental results of long period dataset.

which implies that moving behaviors would be more difficult to predict. This experimental result also presents us with the suggestion that the CQ-DFS framework could be applied to different periods of trajectories by trying different query range sizes.

Chapter 6

Conclusion

In this paper, we study the real-time traffic status estimation service provided by the CarWeb system which tries to return the traffic information of a set of road segments that the user is very likely to pass by next. We would like the estimation service to be received by the users before their next location updating, and meanwhile we would like the estimation service to be accurate such that it does cover the road segment that the users actually pass by next.

In order to fulfill these two requirements, we propose the Continuous Query with Dynamic Fan-Shape framework (CQ-DFS ). It implements the traffic estimation service as a continuous range query with each query instance formed dynamically based on the users’ last reporting locations and their moving behaviors. A comprehensive set of experiments has been conducted via using real vehicle trajectories to demonstrate the superior performance of CQ-DFS.

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