Suttipong Thajchayapong

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This study investigates mobility patterns in microcellular wireless networks, based on measurements from the 802.11 based system that blankets the Carnegie Mellon University campus. We characterize the distribution of dwell time, which is the length of time that a mobile device remains in a cell until the next handoff, and sign-on interarrival time, which(More)
This paper proposes a novel anomaly detection and classification algorithm that combines the spatiotemporal changes in the variability of microscopic traffic variables, namely, relative speed, intervehicle time gap, and lane changing. When applied to real-world scenarios, the proposed algorithm can use the variances of statistics of microscopic traffic(More)
This paper proposes a novel anomaly classification algorithm that can be deployed in a distributed manner and utilizes microscopic traffic variables shared by neighboring vehicles to detect and classify traffic anomalies under different traffic conditions. The algorithm, which incorporates multiresolution concepts, is based on the likelihood estimation of a(More)
This paper presents an ongoing development of a method for releasing pattern of traffic lights, where the primary aim is to reduce traffic congestion in Bangkok, Thailand. The method applies theoretical principles of Fuzzy Logic, where the highlight is on the prominent technicality of decision making under ambiguous logic, similar to the human mind. This(More)
While many studies of admission control schemes assume exponentially distributed cell residual time, field measurements have shown that cell residual time, in reality, follows a heavy-tailed distribution; in particular, a Pareto distribution. These measurements were made in Wireless Andrew, an enterprise-wide broadband microcellular wireless network at(More)
This paper presents a fuzzy logic-based traffic incident detection system to detect a lane-blocking traffic incident that usually causes of traffic congestion. The proposed system uses fuzzy logic to identify traffic status as normal and abnormal. Macroscopic and microscopic traffic variables, namely, mean speed and standard deviation of inter-arrival time(More)
Drivers error is one of the key factor that contributes to road traffic accidents, often in the form of distracted driving which is when drivers are engaged in a secondary task other than driving. Other than the traditional calling and texting, the multi-capabilities of modern smartphones nowadays enable us to realise a wide range of tasks such as emailing,(More)
This paper presents a system to detect lane-blocking traffic incidents which are amongst major causes of traffic jam. The proposed system uses fuzzy logic to identify traffic status as normal and abnormal. Mean speed and standard deviation of inter-arrival time are used as inputs to the fuzzy inference system (FIS), and then, the majority voting is applied(More)