Aging roads and poor road-maintenance systems result a large number of potholes, whose numbers increase over time. Potholes jeopardize road safety and transportation efficiency. Moreover, they are often a contributing factor to car accidents. To address the problems associated with potholes, the locations and size of potholes must be determined quickly. Sophisticated road-maintenance strategies can be developed using a pothole database, which requires a specific pothole-detection system that can collect pothole information at low cost and over a wide area. However, pothole repair has long relied on manual detection efforts. Recent automatic detection systems, such as those based on vibrations or laser scanning, are insufficient to detect potholes correctly and inexpensively owing to the unstable detection of vibration-based methods and high costs of laser scanning-based methods. Thus, in this paper, we introduce a new pothole-detection system using a commercial black-box camera. The proposed system detects potholes over a wide area and at low cost. We have developed a novel pothole-detection algorithm specifically designed to work with the embedded computing environments of black-box cameras. Experimental results are presented with our proposed system, showing that potholes can be detected accurately in real-time.

Existing traffic information acquisition systems suffer from high cost and low scalability. To address these problems, the application of wireless sensor networks (WSNs) has been studied, as WSN-based systems are highly scalable and have a low cost of installing and replacing the systems. Magnetic, acoustic and accelerometer sensors have been considered for WSN-based traffic surveillance, but the use of ultrasonic sensors has not been studied. The limitations of WSN-based systems make it necessary to employ power saving methods and vehicle detection algorithms with low computational complexity. In this paper, we model and analyze optimal power saving methodologies for an ultrasonic sensor and present a computationally-efficient vehicle detection algorithm using ultrasonic data. The proposed methodologies are implemented and evaluated with a tiny microprocessor on real roads. The evaluation results show that the low computational complexity of our algorithm does not compromise the accuracy of vehicle detection.

Existing systems for traffic information acquisition have high costs and low scalability owing to their characteristics such as large size, wired power supplies, and wired communication. To achieve low costs and high scalability, the use of traffic information acquisition systems based on wireless sensor networks (WSNs) has been suggested. However, WSN-based systems have important issues, such as low computing power, limited battery capacity, and high transmission delay. Existing studies on WSN-based acquisition systems have not considered all three of these problems together. Moreover, most studies have focused on theoretical problems rather than practical ones. Therefore, we propose a new system that considers all three limitations of WSN-based systems.In our experiments, we installed our system on real roads for an accurate evaluation. The results show that our system has a high detection accuracy, low power consumption, and low transmission delay.

Recently, the convergence of information technology with biotechnology, nano-technology, or other technologies has been creating a new paradigm. In the field of transportation, intelligent transport systems (ITSs) ⎯ a convergence of information technologies and transportation systems ⎯ have been studied. The VSL is one ITS technologies that aims to improve the safety and efficiency of transportation while controlling the speed limit according to traffic circumstances. Existing studies for VSL algorithms have considered only one station to control the traffic. However, it is not appropriate for an urban freeway to be installed with many stations. In this paper, a new VSL algorithm is proposed to enhance the effectiveness of VSL for multiple stations. It is based on the cooperation of stations and the real-time road information. The proposed algorithm consists of 4 steps: first is a "searching bottleneck station," second is a "calculating a size of congestion," third is a "calculating the number of controlled stations," fourth is a "calculating VSL." In our experiments, the microscopic traffic simulator VISSIM performed our modeling works. The results show that the proposed algorithm improves safety on roads with minimum additional travel time.

Damage to road surfaces in the form of cracks and potholes increases over time and is compounded by poor maintenance systems. Potholes in particular can cause serious problems, including flat tires, damaged wheels, and car accidents. In a previous study, a pothole detection algorithm that used features of two-dimensional images to detect potholes was developed accurately. However, the algorithm yielded wrong detection in the case of similar objects, such as patches, stains, and shades. In particular, complicated shapes and random variations of similar objects led to misdetection. In this study, a pothole detection algorithm is proposed; it uses motion and the intensity of features to distinguish potholes accurately from similar objects. The motion feature is the source of primary information in the proposed algorithm and provides clear and noise-tolerant data for the extraction of potholes from the background region. The proposed algorithm consists of two steps of segmentation and decision and is much simpler than the authors’ previous method. Experimental results show that the proposed algorithm outperforms prevalent pothole detection algorithms as well as the algorithm from the previous study.