Benny Hardjono

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.

Mobile phones as traffic sensors with map matching and privacy considerations

In the recent years, it has become readily more accepted that smart mobile phones with GPS or A-GPS enabled device, or even Cell-ID enabled, among the commuters, can be used as traffic sensor, which complements other traditional sensors. This development is pursued in the efforts of reducing or avoiding traffic jams. Consequently, this paper attempts to find a novel way to map match 2D local map with actual GPS traces from mobile phones. From a number of experiments, it has been found that Virtual Detection Zone method can be used to obtain 100% map matching, as it ensures matching by comparing the GPS data to a set of pre-determined check points (circular VDZ, preferably with a radius of 50–185m). Furthermore, from this study, it is proposed to increase privacy, firstly by using OTP to doubly-lock the sensors data. Secondly, by ensuring that valid data is only delivered from a significant distance of the users private locations, and thirdly, by splitting the data to two parts before using OTP.

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.

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.