Browsing by Author "Chiang, Tsun-Chieh"

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Efficient Broadcast Mechanism for Cooperative Collision Avoidance Using Power Control
(2009)
Jeng, Andy An-Kai
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Jan, Rong-Hong
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Chen, Chien
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Chiang, Tsun-Chieh
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資訊工程學系
Improving driver's safety has been an active research area in wireless communication. In particular, the vehicle cooperative collision avoidance (CCA) is one of the most important issues in safety applications. A variety of broadcast protocols has been proposed for vehicular network. However, there is only a few of them dedicatedly designed for the CCA system. In this paper, we propose a novel broadcast mechanism for CCA using the power control technique. The power control rule is based on the safe distance between vehicles. Simulation results show that our approach can significantly reduce the delivery delay and avoid car collision.
G-Constellations: G-Sensor Motion Tracking Systems
(2010)
Yi, Chih-Wei
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Su, Chao-Min
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Chai, Wen-Tien
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Huang, Jiun-Long
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Chiang, Tsun-Chieh
In most inertial motion tracking systems, motion directions are detected and measured by direction sensors such as magnetometers and gyroscopes. In this paper, we propose a motion tracking system, called g-sensor constellations, in which only g-sensors but no direction sensors are used. The g-sensor constellation is a loose coupling g-sensor system with rigid geometric topology. As few as three g-sensors are needed for motion tracking, including direction detection. The system is easy to be installed. No complicated calibrations are needed and the necessary information is the distances between sensors. The proposed framework can improve the accuracy of dead reckoning systems and help in the analyzing of traffic accidents and developing new human-computer interfaces. In our experiments, a g-sensor constellation composed of three g-sensors, which are located at the vertices of an equilateral triangle with edges of 0.3m and communicate with the processing unit via Bluetooth links, is built to verify the proposed technique.
Mining Overdispersed and Autocorrelated Vehicular Traffic Volume
(2013)
Daraghmi, Yousef-Awwad
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Yi, Chih-Wei
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Chiang, Tsun-Chieh
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資訊工程學系
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Department of Computer Science
Vehicular congestion is a major problem in urban cities and is managed by real time control of traffic that requires accurate modeling and forecasting of traffic volumes. Traffic volume is a time series that has complex characteristics such as autocorrelation, trend, seasonality and overdispersion. Several data mining methods have been proposed to model and forecast traffic volume for the support of congestion control strategies. However, these methods focus on some of the characteristics and ignore others. Some methods address the autocorrelation and ignore the overdispersion and vice versa. In this research, we propose a data mining method that can consider all characteristics by capturing the volume autocorrelation, trend, and seasonality and by handling the overdispersion. The proposed method adopts the Holt-Winters-Taylor (HWT) count data method. Data from Taipei city are used to evaluate the proposed method which outperforms other methods by achieving a lower root mean square error.
Negative Binomial Additive Models for Short-Term Traffic Flow Forecasting in Urban Areas
(2014-04-01)
Daraghmi, Yousef-Awwad
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Yi, Chih-Wei
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Chiang, Tsun-Chieh
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資訊工程學系
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Department of Computer Science
Parallel, coordinated, and network-wide traffic management requires accurate and efficient traffic forecasting models to support online, real-time, and proactive dynamic control. Forecast accuracy is impacted by a critical characteristic of traffic flow, i.e., overdispersion. Efficiency depends on the time complexity of forecasting algorithms. Therefore, this paper proposes a novel spatiotemporal multivariate forecasting model that is based on the negative binomial additive models (NBAMs). Negative binomial is utilized to handle overdispersion, and additive models are used to efficiently smooth nonlinear spatial and temporal variables. To evaluate the model, it is applied to real-world data collected from Taipei City and compared with other forecasting models. The results indicate that the proposed model is an accurate and efficient approach in forecasting traffic flow in urban context where flow is overdispersed, autocorrelated, and influenced by upstream and downstream roads as well as the daily seasonal patterns, namely, low-, moderate-, and high-traffic seasons.
Space-time Multivariate Negative Binomial Regression for Urban Short-Term Traffic Volume Prediction
(2012-01-01)
Daraghmi, Yousef-Awwad
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Yi, Chih-Wei
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Chiang, Tsun-Chieh
;
資訊工程學系
;
Department of Computer Science
The accuracy of short-term traffic volume prediction in urban areas depends on the traffic volume characteristics and how prediction models address these characteristics. In this paper, we propose a space-time multivariate Negative Binomial (NB) regression for short-term traffic volume prediction in urban areas. The NB regression spatially correlates multiple overdispersed traffic volumes on multiple roads. We add the temporal correlation of volumes by allowing each volume to correlate with its values at previous time segments. Data consisting of traffic volumes collected in Taipei city are used to verify the model. The root mean square error is used to compare the proposed model with the Holt-Winters (HW) and Multivariate Structural Time series (MST) models. The results show that the proposed model is more accurate than the HW and MST models in all traffic conditions. The proposed model also determines causal interactions among spatial variables which assists in identifying roads affecting the prediction accuracy. Upstream roads are always significant, distant roads are always insignificant and downstream roads are significant during rush hours only.