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Customer teams with global and regional responsibility work with our customers to develop innovative, customized solutions. To verify the outlier detection results, the black outlier points in Figure 4 are checked according to the raw data. It shows that all vehicles corresponding to outliers are confirmed to have experienced abnormal events. Take Figure 4 a as an example, and the vehicles, corresponding to the outlier points having a comparatively light total vehicle weight and a comparatively long transit time, have experienced breakdown or crash, while outliers have a normal transit time and a comparatively heavy total vehicle weight, and the corresponding vehicles have been overloaded.
In order to verify the clustering results, the toll data during the Spring Festival and the fourth week of February were classified according to the vehicle type, and the real classification results were obtained. The results are presented in Figure 5. Take Figure 5 a as an example, the vehicle type of red points refers to passenger car, vehicle type of yellow points refers to bus, while vehicle type of green points refers to trucks.
Comparing with clustering result of Figure 4 a , it is obvious that the improved algorithm can distinguish the vehicle type correctly. Next, the accuracy of the clustering results was compared to verify the accuracy of the algorithm. Two classical clustering algorithms, namely, the k-means and density-based spatial clustering of applications with noise DBSCAN algorithms, the original fast peak clustering algorithm, and the improved algorithm were, respectively, used to cluster the same toll data.
The classifications results of the four clustering algorithms in both Spring Festival period and the fourth week of February period were compared with the classification of the original vehicle type. The results are depicted in Figure 6. As shown in Figure 6 , the accuracy of the improved fast peak clustering algorithm is notably higher than those of the original fast peak clustering algorithm and the classical k-means [ 32 , 33 ] and DBSCAN [ 34 ] algorithms for data from both the Spring Festival and the fourth week of February, which demonstrates that the improved fast peak clustering algorithm has a higher validity and accuracy than the others.
It also indicates that the algorithm results are highly consistent with the actual traffic situations. The improved algorithm does not increase the time complexity, which is O n 2. It only slightly extends the calculation time than the original algorithm. The time taken by the improved algorithm and others to process all the data is presented in Figure 7.
Although the processing time of the improved algorithm was similar to that of DBSCAN and longer than the original algorithm, it is obviously shorter than the k-means algorithm, and efficiency was not sacrificed due to the increase in accuracy. To conduct an overall analysis of vehicle traffic in the time domain, the following experiment was designed.
First, , pieces of data were randomly sampled by the hour, and pieces of data were obtained. Then, the travel time, travel mileage, and load were visualized according to time, as shown in Figure 8. As shown in Figures 8 a and 8 b , the number of small cars was densely distributed.
Moreover, the mileage and transit time exhibited overall increasing trends over time, respectively, as is shown in Figures 8 c and 8 d. According to information such as the latitude and longitude, it is inferred that the toll station is located near the technology industrial estate.
This provides ideas for analyzing the rationality of data distribution more profoundly. For example, most of vehicles entering and leaving the estate are passenger cars with a comparatively light weight, which are probably used to carry staff members. In addition, trucks with a heavy weight have a high mileage and transit time, which means that there could be a long distance between estate and its raw material production site or commodity storage site.
Abnormal highway events were detected based on the improved fast peak clustering algorithm, which was used to calculate the distance between each data point on the filtered toll data, and the distance matrix was obtained as an input. The results are exhibited in Figure 9. To find the pseudo-center, the distribution of pseudo-centers is presented in Figure Figure 11 presents the final cluster center distribution determined after correcting the cluster centers.
The final clustering result is shown in Figure The red and green points in the figures are the valid data points of clustering, and the black points are abnormal data points. The long transit time between two toll stations that are close to each other may be caused by accidents, parking, clock asynchronization, recording errors, or suspected fee evasion. Data lower than this value are considered to be abnormal data, which may be caused by vehicle speeding, network failure, clock asynchronization, recording errors, or suspected fee evasion.
Anomalies in toll data can be used to accurately track the basic information of the vehicle, station, lane, and personnel associated with an event. Moreover, the possible causes of an incident can be analyzed, and the scope of the incident investigation can be greatly reduced.
For example, during the period of January 9—10, a large amount of abnormal data regarding the duration of traffic appeared at the same entrance or exit. This demonstrates that the vehicle was likely to be speeding or attempting to evade fees, or that software or network failure had occurred, and special verification of the license plate is required.
Two types of data were found by using the fast peak algorithm and outlier detection algorithm, respectively. It demonstrates that the algorithm can quickly and accurately identify abnormal events such as road congestion, system failures, and suspected fee evasion hidden in the toll data. The times of abnormal events were statistically analyzed to examine the distribution of abnormal events in the province. Abnormal event detection was carried out on 70, pieces of data, and a total of 1, abnormal events were obtained.
These events were visualized in the time domain, and the results are presented in Figure It can be seen from Figure 13 that there were two obvious peaks in the occurrence of abnormal events. The reason for this phenomenon may be that the flow of vehicles passing through the toll gate during these two time periods was greatly increased, leading to an increase in abnormal events. In order to determine whether the relationship between the out-time and the occurrence of abnormal events is significant, a statistical test analysis with SPSS software was performed on the data distribution in Figure This shows that the abnormal event detection result is in line with the facts.
The results indicate that, to quickly detect abnormal events in massive toll data, the traffic control department can increase its investigation efforts during these two time periods. This paper focused on a highway event detection method based on the fast peak clustering algorithm. The main conclusions of this research are as follows: 1 An outlier detection and data-filling algorithm for multidimensional data based on the sum of similar coefficients is proposed.
A case analysis of highway events based on the proposed fast peak clustering algorithm can be conducted to accurately locate the vehicles, stations, and other related information. The scope of the investigation of abnormal events can be narrowed to a great extent.
Abnormal events such as long-term stay and vehicle overload hidden in the toll data can be easily identified. However, this research focused on the analysis of historical data and did not include integration with the toll system to complete real-time data analysis. In future research, due to the complicated research directions and problems involved in highway operation management, the proposed algorithm must be further improved and optimized.
Moreover, the algorithm can be combined with other data sources, such as the correlation analysis of operational indicators, to more accurately determine specific reasons for the occurrence of events. The data utilized in this research were obtained from the Shaanxi Provincial Department of Transportation of China. They contain sensitive information about the owners and therefore cannot be shared publicly.
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Received 10 Jul Revised 05 Jan Accepted 13 Feb Published 20 Feb Abstract Aiming at the mining of traffic events based on large amounts of highway data, this paper proposes an improved fast peak clustering algorithm to process highway toll data.
Introduction With the gradual improvement in the highway network and the arrival of the information era, the data generated by intelligent toll systems [ 1 ], intelligent road detection systems, and other facilities have formed a certain scale [ 2 ].
Methodology The primary methodology undertaken in this study is presented in Figure 1. Figure 1. General implementation of the traffic event detection method undertaken in this study. Table 1. Table 2. Figure 2. The process of the outlier detection algorithm based on the sum of similar coefficients. Figure 3. Figure 4. The clustering results of the improved algorithm: a the Spring Festival period; b the fourth week of February.
Figure 5. Results of toll data classified by vehicle type: a the Spring Festival period; b the fourth week of February. Figure 6. Figure 7. Comparison of the computation time of four clustering algorithms. Figure 8. Distributions of vehicle traffic characteristics in the time domain: a InLoad; b OutLoad; c mileage; d transit time.
Figure 9. Figure ID Transit time h Weight kg 0. Table 3. References A. Oklilas, A. Fitriyani, and A. View at: Google Scholar D. Liang, L. Fan, and Z. View at: Google Scholar R. Zhou, L. Zhong, N. Zhao et al. Zhang, Y. Cai, and Y. View at: Google Scholar J. Weng, L. Liu, and B. Williams, J. Hildreth, and M. Li and W. View at: Google Scholar H. Lu, S. Luo, and R. View at: Google Scholar F.
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