Browsing by Author "Lan, Chien-Lun"

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A hybrid shortest path algorithm for navigation system
(2007)
Cho, Hsun-Jung
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Lan, Chien-Lun
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
Combined with Geographic Information System (GIS) and Global Positioning System (GPS), the vehicle navigation system had become a quite popular product in daily life. A key component of the navigation system is the Shortest Path Algorithm. Navigation in real world must face a network consists of tens of thousands nodes and links, and even more. Under the limited computation capability of vehicle navigation equipment, it is difficult to satisfy the real-time response requirement that user expected. Hence, this study focused on shortest path algorithm that enhances the computation speed with less memory requirement. Several well-known algorithms such as Dijkstra, A* and hierarchical concepts were integrated to build hybrid algorithms that reduce searching space and improve searching speed. Numerical examples were conducted on Taiwan highway network that consists of more than four hundred thousands of links and nearly three hundred thousands of nodes. This real network was divided into two connected sub-networks (layers). The upper layer is constructed by freeways and expressways- the lower layer is constructed by local networks. Test origin-destination pairs were chosen randomly and divided into three distance categories; short, medium and long distances. The evaluation of outcome is judged by actual length and travel time. The numerical example reveals that the hybrid algorithm proposed by this research might be tens of thousands times faster than traditional Dijkstra algorithm; the memory requirement of the hybrid algorithm is also much smaller than the tradition algorithm. This outcome shows that this proposed algorithm would have an advantage over vehicle navigation system.
Hybrid shortest path algorithm for vehicle navigation
(2009-08-01)
Cho, Hsun-Jung
;
Lan, Chien-Lun
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
Vehicle navigation is one of the important applications of the single-source single-target shortest path algorithm. This application frequently involves large scale networks with limited computing power and memory space. In this study, several heuristic concepts, including hierarchical, bidirectional, and A(*), are combined and used to develop hybrid algorithms that reduce searching space, improve searching speed, and provide the shortest path that closely resembles the behavior of most road users. The proposed algorithms are demonstrated on a real network consisting 374,520 nodes and 502,485 links. The network is preprocessed and separated into two connected subnetworks. The upper layer of network is constructed with high mobility links, while the lower layer comprises high accessibility links. The proposed hybrid algorithms are implemented on both PC and hand-held platforms. Experiments show a significant acceleration compared to the Dijkstra and A(*) algorithm. Memory consumption of the hybrid algorithm is also considerably less than traditional algorithms. Results of this study showed the hybrid algorithms have an advantage over the traditional algorithm for vehicle navigation systems.
Method for identification of traffic lane boundary
(2010-06-24)
Jou, Yow-Jen
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Cho, Hsun-Jung
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Chen, Yu-Kuang
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Huang, Heng
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Hsu, Chia-Chun
The invention provides a method for identification of traffic lane boundary. Firstly the microwave signal is received, and the noise reduction is treated for the microwave signal. Then the frequency domain information is employed to calculate the legal set of closed interval, in order to form the frequency span information. Finally, the probability density function model is employed to calculate the frequency span information in order to identify the traffic lane boundary.
Parallel chain convergence of time dependent origin-destination matrices with gibbs sampler
(2006)
Jou, Yow-Jen
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Ch, Hsun-Jung
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Lan, Chien-Lun
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Hsu, Chia-Chun
;
資訊管理與財務金融系註：原資管所+財金所
An effective method of O-D estimation by the state-space model has been introduced by Jon. Coupled with Gibbs sampler and Kalman filter, the state-space model can generated precious O-D matrices without any prior information while other studies assume that the transition matrix is known or at least approximately known. The Gibbs sampler, a particular type of Markov Chain Monte Carlo method, is one of the iterative simulation methods. To monitor of convergence of this iterative simulation, a parallel chain technique is implemented in this paper. By the numerical example, the convergence of the different chains would be clearly pointed out. The comparison of simulation and real data also shows that satisfying results can be obtained by the model.
Path Flow Estimation Using Time Varying Coefficient State Space Model
(2009)
Jou, Yow-Jen
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Lan, Chien-Lun
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資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
The dynamic path flow information is very crucial in the field of transportation operation and management, i.e., dynamic traffic assignment, scheduling plan, and signal timing. Time-dependent path information, which is important in many aspects, is nearly impossible to be obtained. Consequently, researchers have been seeking estimation methods for deriving valuable path flow information from less expensive traffic data, primarily link traffic counts of surveillance systems. This investigation considers a path flow estimation problem involving the time varying coefficient state space model, Gibbs sampler, and Kalman filter. Numerical examples with part of a real network of the Taipei Mass Rapid Transit with real O-D matrices is demonstrated to address the accuracy of proposed model. Results of this study show that this time-varying coefficient state space model is very effective in the estimation of path flow compared to time-invariant model.
Signal recognition by nonparametric discrimination of functional data for vehicle transportation system
(2007)
Jou, Yow-Jen
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Lu, Yung-Tsai
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Lan, Chien-Lun
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Lee, Hsia
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資訊管理與財務金融系註：原資管所+財金所
Traffic congestion is a serious and general problem in our daily life. Real time vehicle information is essential to the advanced traffic management. The recognition and statistics of traffic flow among different types of vehicles would be contributive to improve traffic block. This paper considers the dataset recorded by the radar microwave detector with the intensity of reflecting waves and the types of vehicles. The data is treated as functional data and then classification would be proposed to be performed by nonparametfic discrimination of functional data with three forms of Proximity. The proximity with lower misclassification rate would be adopted for the data. The results show that the misclassification rate is pretty satisfactory if the number of groups is two and as the number of group increases the misclassification rate increases as expected.
Statistical estimation of dynamic Tobit models
(2006)
Jon, Yow-Jen
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Chang, Wan-Ru
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Lan, Chien-Lun
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資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
This paper considers the estimation of the parameters of the dynamic linear models where the dependent variable is discrete and truncated to the right of a known constant. Due to censoring, some dependent variables cannot be observed. Two approaches are considered: Newton-Raphson algorithm is used for the Poisson regression dynamic Tobit model and the SML-GHK simulator is used for the estimation of dynamic Tobit model whose lagged dependent variable is latent. Both approaches give similar prediction results in terms of the average relative prediction error.
Time Dependent Origin-destination Estimation from Traffic Count without Prior Information
(2009-06-01)
Cho, Hsun-Jung
;
Jou, Yow-Jen
;
Lan, Chien-Lun
;
運輸與物流管理系註：原交通所+運管所
;
資訊管理與財務金融系註：原資管所+財金所
Existing research works on time-dependent origin-destination (O-D) estimation focus on the surveillance data usually assume the prior information of the O-D matrix (or transition matrix) is known (or at least partially known). In this paper, we relax such assumption by combining Gibbs sampler and Kalman filter in a state space model. A solution algorithm with parallel chain convergence control is proposed and implemented. To enhance its efficiency, a parallel structure is suggested with efficiency and speedup demonstrated using PC-cluster. Two numerical examples (one for Taipei Mass Rapid Transit network and the other for Taiwan Area National Freeway network) are included to show the proposed model could be effective of time-dependent origin-destination estimation.
User-oriented travel time prediction using grey system and real-time vehicle detector data
(2005)
Jou, Yow-Jen
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Lan, Chien-Lun
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統計學研究所
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Institute of Statistics
Real-time travel information is increasingly important for ITS. Many high-valued time-sensitive applications can take advantage from this real-time information. This paper describes an approach to estimate travel time with Grey System. Combined with real-time vehicle detector data, wireless communication ability and global positioning system, individual driving preference is then measured and been used to adjust the estimation model. An empirical result is shown at the end of this paper and shows a satisfying result.