Browsing by Author "Sheu, HB"

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A fuzzy clustering approach to real-time demand-responsive bus dispatching control
(2005-03-16)
Sheu, HB
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運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
Quick response (QR) to passenger needs is a key objective for advanced public transportation systems (APTS), and it has become increasingly important for contemporary metropolitan bus operations to gain a competitive advantage over private transportation. This paper presents a real-time control methodology for demand-responsive bus operations that respond quickly to passenger needs. The proposed method primarily involves two levels of functionality: (1) short-term forecasting of passenger demands using time-series prediction models, and (2) identification of service strategies coupled with the associated bus service segments using fuzzy clustering technologies in response to variances in passenger demand attributes and traffic conditions. The proposed bus operations method identifies the demand-responsive vehicle service strategies primarily according to the predicted up-to-date attributes of passengers' demands, rather than deterministic passenger arrival rates, which were generally used in previous literature. In addition, the variation of traffic conditions along bus lines is considered in the proposed method. Results from numerical studies using real data of passengers' demands, including passenger volume at each bus stop and the passenger origin-destination (O-D) patterns, are presented to demonstrate the effectiveness of the proposed method for real-world applications. (C) 2004 Elsevier B.V All rights reserved.
A fuzzy clustering-based approach to automatic freeway incident detection and characterization
(2002-06-16)
Sheu, HB
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
Automatic incident detection and characterization is urgently required in the development of advanced technologies used for reducing non-recurrent traffic congestion on freeways. This paper presents a new method which is constructed primarily on the basis of the fuzzy clustering theories to identify automatically freeway incidents. The proposed approach is capable of distinguishing the time-varying patterns of incident-induced traffic states from the patterns of incident-free traffic states, and characterizing incidents with respect to the onset and end time steps of incidents, incident location, the temporal and spatial change patterns of incident-related traffic variables in response to the impacts of incidents on freeway traffic flows in real time. Lane traffic count and density are the two major types of input data, which can be readily collected from point detectors. Based on the spatial and temporal relationships of the collected raw traffic data, several time-varying state variables are defined, and then evaluated quantitatively and qualitatively to determine the decision variables used for real-time incident characterization. Utilizing the specified decision variables, the proposed fuzzy clustering-based algorithm executes recurrently three major procedures: (1) identification of traffic flow conditions, (2) recognition of incident occurrence, and (3) incident characterization. In this study, data used for model tests are generated from the CORSIM traffic simulator. Our preliminary test results indicate that the proposed approach is promising, and, in expectation, can be integrated with any published real-time incident detection technologies. Importantly, this study may contribute significantly to the applications of fuzzy clustering techniques, and stimulate more related research. (C) 2002 Elsevier Science B.V. All rights reserved.
A multi-layer demand-responsive logistics control methodology for alleviating the bullwhip effect of supply chains
(2005-03-16)
Sheu, HB
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
This paper presents a multi-layer demand-responsive logistics control strategy for alleviating, effectively and efficiently, the bullwhip effect of a supply chain. Utilizing stochastic optimal control methodology, the proposed method estimates the time-varying demand-oriented logistics system states, which originate directly and indirectly downstream to the targeted member of a supply chain, and associate these estimated demands with estimates of different time-varying weights under the goal of systematically optimizing the logistical performance of chain members. In addition, an experimental design is conducted where the proposed method is evaluated with the two specified criteria. Numerical results indicate that the proposed method permits alleviating, to a great extent, the bullwhip effect in comparison with the existing logistics management strategies. Furthermore, the methodology presented in this study is expected to help address issues regarding the uncertainty and complexity of the distortion of demand-related information existing broadly among supply chain members for an efficient supply chain coordination. (C) 2003 Elsevier B.V. All rights reserved.