Ari Wibisono

Traffic big data prediction and visualization using Fast Incremental Model Trees-Drift Detection (FIMT-DD)

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.

Vehicle counting and speed measurement using headlight detection

CCTV is one of the tools that can be used to extract the needed traffic Information. Extracted information from image sequences of CCTV can give us real information about the number of passing vehicles and vehicles speed. In this paper we propose a new method in detecting the number of vehicles and vehicle speed measurement in low light conditions. Headlight detection is used in order to identify the existing vehicle. There are few steps in order to extract the information from CCTV. First for vehicle headlight detection, the vehicles are detected with normalized cross-correlation method and centroid-area-difference. The second step is vehicle tracking. Headlight is used to track the movements of the vehicle. The third step is vehicle counting and vehicle speed measurement; pin-hole and euclidean distance methods are used to estimate the vehicle speed. We have compared the vehicle detection algorithm and vehicle counting-speed measurement. The result shows that the normalized cross correlation method has a higher accuracy than area-centroid difference. The pinhole model also is better in estimating vehicle speed compared to euclidean distance.

Background Subtraction Using Gaussian Mixture Model Enhanced by Hole Filling Algorithm (GMMHF)

There is a necessity in traffic control system using camera to have the capability to discriminate between an object and non-object in the image. One of the procedure to discriminate between those two is usually performed by background subtraction. Gaussian Mixture Model (GMM) is popular method that has been employed to tackle the problem of background subtraction. However, the output of GMM is a rather noisy image which comes from false classification. This situation may arise because several conditions in the video input such as, waving trees, rippling water, and illumination changes. In this paper, an enhanced version of GMM technique which is combined with Hole Filling Algorithm (HF) is proposed to alleviate those problems. The experimental result shows that the proposed method improved the accuracy up to 97.9% and Kappa statistic up to 0.74. This result has outperformed many similar methods that is used for evaluation.

Improved vehicle speed estimation using Gaussian mixture model and hole filling algorithm

Vehicle speed estimation using Closed Circuit Television (CCTV) is one of the interesting issues in the field of computer vision. Various approaches are used to perform automation in vehicle speed estimation using CCTV. In this study, the use of Gaussian Mixture Model (GMM) for vehicle detection has been improved with the hole-filling method (HF). The speed estimation of the vehicles with various scenarios has been done, and gives the best estimation with the deviation of 7.63 Km/hr. GMM fusion with hole-filling algorithm combined with Pinhole models have shown the best results compared with results using other scenarios.

Algal growth rate modeling and prediction optimization using incorporation of MLP and CPSO algorithm

The cause of global warming is the existence of greenhouse gases that trap the emitted infrared wave and cause the increasing of the earths temperature. One of the predominant greenhouse gases in the atmosphere is CO2. Biosequestration by utilizing micro algae is one of the promising method to reduce the concentration of CO2 in the atmosphere. This research focuses on the modeling of the algal growth which is one of the parameter that defines the amount of CO2 which can be fixated by algal. From the observation data, the growth behavior is modeled by regression method, Multilayer Perceptron (MLP) algorithm. To optimize the algorithm, MLP is also combined with The Canonical Particle Swarm Optimization (CPSO). The result shows that modeling using MLP-CPSO is more accurate than the original MLP and MLP-PSO respectively by 25% and 15% in RAE. MLP-CPSO also shows the best performance in RMSE with 0.091 and coefficient correlation (r) with 0.92.

Perceptron rule improvement on FIMT-DD for large traffic data stream

This paper proposed a method to build knowledge from one and a half years of UK traffic data sets. The method used is the Fast Incremental Model Trees - Drift Detection (FIMT-DD) with an improvement on the perceptron rule. In order to predict a traditional data set, we first analyze the model. After we have analyzed the model, we then average it from different arrangements of the datasets. In a stream data set, the approach is different from the traditional data sets. The approach of a stream data set is to take several snapshots during the induction to analyze the accuracy progress of the predicted model. We used the tanh activation function to optimize the perceptron rule. Therefore, we are able to reduce the error of the result. The error measurements that we analyzed in this paper are MAE, RMSE, and SMAPE. From 100,000,000 instances of traffic data, the optimized FIMT-DD algorithm method proves to be successful with smaller error value than standard FIMT-DD algorithm. To be able to measure the performance of the error of our optimized FIMT-DD algorithm, we used evaluate prediction sequential to analyze the data. According to the error measurement results of the MAE, RMSE, and SMAPE, tanh(x) activation function has a good influence to decrease the error value and improve the accuracy. The smaller error values did not only happen in a few experiments instances, but it occurs in the majority of the instances evaluation.

An adaptive selective background learning-hole filling algorithm to improve vehicle detection

Transportation plays an important role in urban development However, the vehicle growth in Indonesia is not supported by the number of road. Due to this fact, traffic congestion is easily occurred, especially in big cities. Intelligent Transportation System (ITS) has huge contribution to decrease the traffic congestion. In ITS, vehicle detection is one of challenging issue for traffic surveillance. In this paper, adaptive selective background learning and hole filling algorithm are applied to improve the vehicle detection. The validity of the proposed method is tested by using three scenarios and two parameters. The scenarios are bad weather close range (BW-CR), normal weather close range (NW-CR) and normal weather wide range (NW-WR). While, the parameters are the time duration of stopped vehicle detection and the pixel accuracy. Then, the proposed method (Adaptive Selective Background Learning-Hole Filling algorithm) is compared by another previous vehicle detection method. Generally, the result shows that the proposed method yields a significant improvement in vehicle detection. ASBL-HF can detect the stopped and moved vehicle with free noises. Moreover, ASBL-HF has the best accuracy. The accuracy value is about 98.2%.

Fundamental diagram estimation using Virtual Detection Zone in smart phones' application and CCTV data

Conventionally, Fundamental Diagrams, which consist of vehicle traffic flow and density pairs, are obtained from intrusive sensor such as inductive loop detectors. However these sensors are uncommon in developing countries as they are embedded in the roads, and consequently expensive to deploy and impractical to implement on busy roads. Our novel method, VDZ with CCTV snap shots can provide the data needed to construct Fundamental Diagrams and able to show zero speeds at jam density, which provide essential parameters for macroscopic traffic model. The results obtained, without the use of any intrusive sensor, have shown agreement with previous traditional method.

Vehicle traffic monitoring using single camera and embedded systems

Traffic congestion is a problem that often occurs in the big cities in Indonesia. It is caused by very rapid increase of vehicle. The offered solution is to monitor the traffic situation automatically. We implemented the method of detecting vehicle during night in four single board computers (SBC) that are: Raspberry Pi B+, Beagleboard Xm, Raspberry Pi 2 and Odroid XU4. Perfomance of Odroid XU4 exceed other single board computers in which the maximum fps obtained 30 frame per second(fps) and the maximum accuracy of vehicle detection reached 98 percent.

Sensor node for data sampling and correlation analysis of CO 2 concentration with air humidity, temperature, and light intensity

Carbon Dioxide gas (CO2) gas contained in our air which has many roles in environment, but in a huge amount it became dangerous. To encounter that, the system for CO2 monitoring is needed. One of the most effective way is using Wireless Sensor Network (WSN). This system capable to monitor concentration of CO2 and another variable using sensor nodes. But not all of that parameters is correlated to concentration of CO2. To make monitoring system runs efficiently, correlation analysis between variables is needed. This research conduct a correlation analysis between concentration of CO2 and humidity, temperature and light intensity from data collected by our own-made digital sensor node. Data gathered for seven days in one location with a fluctuate environment condition. Correlation calculated with Spearmans rho method. The result is CO2 and air humidity have a strong positive correlation with air humidity (0.726), weak negative correlation with light intensity (−0.319), and no correlation with air temperature (−0.008).