# Querying and Extracting Timeline Information from Road Traffic Sensor Data
## 5 Timeline Query Index and Analytical Query Processing
### TQ-index Construction
#### Congestion Event Detection
This is explained by the fact that the different amounts of congestion events are substantial for each month. At the varied date range setting, the execution time increases linearly as we increase the number of dates. Again, these trends are consistent with the results of \(Q_{1}\) and \(Q_{2}\) for the varied date setting. Figure 23: Query performance \(Q_{2}\) on the “Seattle” dataset. Figure 16:0 Query performance \(Q_{3}\) on the “Busan” dataset. Affected Congestion Query Processing

We investigate the performance of the affected congestion query processing. A user specifies a road linkID and a time of traffic congestion event as the parameters of \(Q_{4}\). Figure 26 shows the results. Figure 26(a),b show that only a small amount of execution time is required to process the affected congestion query. For the \"Busan\" dataset, the execution times for five different roads remain almost the same. The execution times for the \"Seattle\" dataset are also quite stable, without a significant increase, even though we increase the size of the dataset. ### Varying Dataset Size

In this experiment, we analyze the performance of our QET system when we increase the size of the raw traffic sensor data. We set a date range of five days and select a date period from the 23 rd day to the 27 th day for each month. All road links for five roads of the \"Busan\" dataset and all road links of highways for the \"Seattle\" dataset are used in this experiment. The evaluation results for query types \(Q_{1}\), \(Q_{2}\), and \(Q_{3}\) are summarized in Figure 27, and the results for \(Q_{4}\) are described in Figure 16:1 We find that it takes much less time (less than 0.1 ms) to process type \(Q_{4}\) queries. Thus, we separate the results in the graph and the table. Figure 16:2 Query performance \(Q_{4}\). Figure 16:3 Query performance \(Q_{3}\) on “Seattle” dataset. In Figure 27, we observe that execution times for queries in the three types (\(Q_{1}\), \(Q_{2}\), and \(Q_{3}\)) remained almost the same as the dataset size increased. This can be explained by the efficiency of a TQ-index. We can effectively search _LocationIndex_ and _TimeIndex_ by taking user specified parameters, such as dates and road links. Another observation is that \(Q_{3}\) always shows inferior performance for both the \"Busan\" and \"Seattle\" datasets. We need to calculate the length of each congestion event by checking all adjacent road links to identify the longest congestion specified by the query type \(Q_{3}\). This additional computation causes significant performance degradation in the execution of query. Effects of the Number of Traffic Congestion Events

In the previous experiments, the query processing time increased with the date range. Figure 28 shows the correlation between the number of traffic congestion events and the execution time for \(Q_{1}\), \(Q_{2}\), and \(Q_{3}\). We used all 24 months of data for the \"Busan\" dataset and all six months of data for the \"Seattle\" dataset. The trends in execution time follows the pattern of the number of traffic events. We think that the number of traffic congestion events can affect the query processing time. \begin{table}

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Table 4: Varying the dataset size for \(Q_{4}\). Figure 27: Varying dataset size. ## 7 Conclusions

In this paper, we presented a road traffic analytical query processing system called QET to query and analyze road traffic sensor data. For effective analytical query processing, the QET system employs: (1) a timeline model to extract traffic congestion events from raw traffic sensor data; (2) TQ-index to maintain the timeline model; and (3) efficient query processing algorithms to support the analytical queries. We conducted a comprehensive performance evaluation of the QET system using real datasets, Busan ITS sensor data, and Seattle freeway data. Our experimental results showed that the timeline modeling and the TQ-index of the QET system were effective in maintaining traffic congestion events and processing analytical queries by searching only relevant information.