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Information extraction using distributed sensors has been widely used to obtain information knowledge from various regions or areas. Vehicle traffic data extraction is one of the ways to gather information in order to get the traffic condition information. This research intends to predict and visualize the traffic conditions in a particular road region. Traffic data was obtained from Department of Transport UK. These data are collected using hundreds of sensors for 24 h. Thus, the size of data is very huge. In order to get the behavior of the traffic condition, we need to analyze the huge dataset which was obtained from the sensors. The uses of conventional data mining methods are not sufficient to use, due to the process of knowledge building that should store data temporary in the memory. The fact that data is continuously becoming larger over time, therefore we need to find a method that could automatically adapt to process data in the form of streams. We use method called FIMT-DD (Fast Incremental Model Trees-Drift Detection) to analyze and predict the very large traffic dataset. Based on the prediction system that we have developed, we also visualize the prediction of traffic flow condition within generated sensor point in the real map simulation. [ABSTRACT FROM AUTHOR]

Feature selection is a way of reducing the features of data such that, when the classification algorithm runs, it produces better accuracy. In general, conventional feature selection is quite unstable when faced with changing data characteristics. It would be inefficient to implement individual feature selection in some cases. Ensemble feature selection exists to overcome this problem. However, with the advantages of ensemble feature selection, some issues like stability, threshold, and feature aggregation still need to be overcome. We propose a new framework to deal with stability and feature aggregation. We also used an automatic threshold to see whether it was efficient or not; the results showed that the proposed method always produces the best performance in both accuracy and feature reduction. The accuracy comparison between the proposed method and other methods was 0.5–14% and reduced more features than other methods by 50%. The stability of the proposed method was also excellent, with an average of 0.9. However, when we applied the automatic threshold, there was no beneficial improvement compared to without an automatic threshold. Overall, the proposed method presented excellent performance compared to previous work and standard ReliefF. [ABSTRACT FROM AUTHOR]

To measure the distance of an object, we propose a distance sensor that combines a tunable aperture operated by the thermal expansion of silicon chevron beams with a commercial camera similar to the automotive black box. The distance sensor is composed of a tunable aperture, a lens with an 8 mm focal length, and a 1/4-inch full high definition image sensor, where a tunable aperture is attached to the camera lens. To estimate the distance to the object, the blur difference is expressed in the form of a depth map using the depth from defocus algorithm. The mean error of distance estimation was 0.29 m. The area of the tunable aperture in the distance sensor is 1.18 mm2 at OFF state and 2.79 mm2 when the voltage is applied. The distance sensor can display an image and a distance simultaneously and can be applied to autonomous vehicles, drones, and robots. [ABSTRACT FROM AUTHOR]

Traffic prediction techniques are classified as having parametric, non-parametric, and a combination of parametric and non-parametric characteristics. The extreme learning machine (ELM) is a non-parametric technique that is commonly used to enhance traffic prediction problems. In this study, a modified probability approach, continuous conditional random fields (CCRF), is proposed and implemented with the ELM and then utilized to assess highway traffic data. The modification is conducted to improve the performance of non-parametric techniques, in this case, the ELM method. This proposed method is then called the distance-to-mean continuous conditional random fields (DM-CCRF). The experimental results show that the proposed technique suppresses the prediction error of the prediction model compared to the standard CCRF. The comparison between ELM as a baseline regressor, the standard CCRF, and the modified CCRF is displayed. The performance evaluation of the techniques is obtained by analyzing their mean absolute percentage error (MAPE) values. DM-CCRF is able to suppress the prediction model error to ~ 17.047 % , which is twice as good as that of the standard CCRF method. Based on the attributes of the dataset, the DM-CCRF method is better for the prediction of highway traffic than the standard CCRF method and the baseline regressor. [ABSTRACT FROM AUTHOR]