Browsing by Author "Huang, Yi-San"

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A REFINED PARSIMONY PROCEDURE TO INVESTIGATING NONLINEAR TRAFFIC DYNAMICS
(2005)
Lan, Lawrence W.
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Huang, Yi-San
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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
This paper refines the parsimony procedure developed by Lan et al. (2005) and revisits the nonlinear features of real-world traffic dynamics in different contexts. The refined procedure begins with filtering out the periodic and quasi-periodic patterns by Fourier power spectrum, then filtering out the equilibrium (fixed) points by largest Lyapunov exponent, and finally distinguishing chaoticity from stochasticity by iterated function system clumpiness maps between original and surrogate flow time series data. An empirical analysis of traffic flow data measured with various time intervals (20-second, one-minute, three-minute and nine-minute) during different times of day (midnight, morning, afternoon and evening) are examined. The results show that different nonlinear patterns may emerge or vanish, depending on the measured intervals and times of day. Traffic patterns in the morning (6:00 to 9:00 am) and evening (18:00 to 21:00 pm) periods are close to chaotic phenomena. During the afternoon off-peak period (12:00 to 15:00 pm), the pattern is close to stochastic phenomenon. The 20-second traffic pattern at midnight (0:00 to 3:00 am) is close to equilibrium point due to copious zero flows.
CONFINED SPACE FUZZY PROPORTION MODEL FOR SHORT-TERM TRAFFIC FLOW PREDICTION
(2004)
Lan, Lawrence W.
;
Lin, Feng-Yu
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Huang, Yi-San
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
This paper develops a confined space fuzzy proportion (CSFP) model to predict the short-term traffic flow dynamics. The rationale for prediction reasoning is based on the "fuzzy proportion" inference from similar historical trajectories that are within a "confined space" defined by a spatial threshold in the reconstructed state space. To perform the inference, we assign to each trajectory with unequal weight that is proportional to the relative distance to the latest (present) data vector. The United States I-35 Freeway one-minute flow data are used to construct and validate the proposed model. We employ Theil inequality coefficient (U) and its decomposed proportions to evaluate the prediction performance and to analyze the sources of prediction errors. The empirical results have demonstrated extremely high predictive capability of this CSFP model. The small bias and variance proportions of U statistic further indicate that the prediction model can successfully capture the trends and conspicuous fluctuations of one-minute traffic flow variations.
EXPLORATION OF TRAFFIC PHASES
(2008)
Huang, Yi-San
;
Lan, Lawrence W.
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
Without considering the traffic stream evolution, conventional traffic stream diagrams may provide incomplete information for real-time traffic management and control applications. This study aims to add evolutionary information into the traffic stream diagrams by ordering chronologically the paired-traffic parameters and to explore the emerged traffic phases and their transitions. The flow, speed, and occupancy raw data directly extracted from dual-loop detectors on freeway at various times of day are investigated. Our investigations show that traffic stream characteristics do exhibit various phases, including free-flow phase, synchronized-flow phase, and breakdown phenomenon-an unstable phase that plays a transitive role between free-flow phase and synchronized-flow phase. Our exploration of traffic phases with consideration of traffic stream evolution has shed some light on the development of advanced real-time traffic management/control strategies.
Investigation of temporal freeway traffic patterns in reconstructed state spaces
(2008-02-01)
Lan, Lawrence W.
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Sheu, Jiuh-Biing
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Huang, Yi-San
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
The characterization of the dynamics of traffic states remains fundamental to seeking for the solutions of diverse traffic problems. To gain more insights in traffic dynamics in the temporal domain, this paper explored traffic patterns in higher-dimensional state spaces, where we attempted to map the one-dimensional traffic series into appropriate multidimensional spaces by Takens' algorithm. After such a state space reconstruction, we then made use of the largest Lyapunov exponent to depict the rate of expansion or contraction of traffic state trajectories in the reconstructed spaces. The correlation dimension was further estimated to examine if the traffic state trajectories exhibited chaotic-like or stochastic-like motions. An empirical study using flow, speed, and occupancy time-series data as well as the speed-flow, speed-occupancy, and flow-occupancy paired data collected from dual-loop detectors on a freeway of Taiwan was conducted. The numerical results revealed that different nonlinear traffic patterns could emerge depending on the observed time-scale, history data and time-of-day. In addition, with consideration of sequential order and spatiotemporal features, more information about traffic dynamical evolution was extracted. (c) 2007 Elsevier Ltd. All rights reserved.
Short-term prediction of traffic dynamics with real-time recurrent learning algorithms
(2009)
Sheu, Jiuh-Biing
;
Lan, Lawrence W.
;
Huang, Yi-San
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
Short-term prediction of dynamic traffic states remains critical in the field of advanced traffic management systems and related areas. In this article, a novel real-time recurrent learning (RTRL) algorithm is proposed to address the above issue. We dabble in comparing pair predictability of linear method versus RTRL algorithms and simple non-linear method versus RTRL algorithms individually using a first-order autoregressive time-series AR(1) and a deterministic function. A field study tested with flow, speed and occupancy series data collected directly from dual-loop detectors on a freeway is conducted. The numerical results reveal that the performance of RTRL algorithms in predicting short-term traffic dynamics is satisfactorily accepted. Furthermore, it is found that the dynamics of short-term traffic states characterised in different time intervals, collected in diverse time lags and times of day may have significant effects on the prediction accuracy of the proposed algorithms.
SOME EVIDENCES OF CHAOTIC PHENOMENA IN SHORT-TERM TRAFFIC FLOW DYNAMICS: SURROGATE DATA APPROACH
(2004)
Lan, Lawrence W.
;
Lin, Feng-Yu
;
Huang, Yi-San
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
This paper employs wide-ranging indexes, including geometric plots and statistics, to examine whether chaotic phenomena exhibit in the short-term traffic flow dynamics. A field study is carried out using the one-minute flow time series data drawn from the United States 1-35 Freeway. The distinction of chaoticity and randomness is made between the original traffic data and their surrogates. We find that the geometric plots including iterated-function system clumpiness map, correlation function plot, and power spectrum have displayed apparent difference between the original and surrogate data and that the statistics including delay time, Hurst exponent, and relative complexity have also revealed noticeable discrepancy between them. These indexes have demonstrated strong evidences of chaotic, not random, phenomena in the nature of freeway one-minute traffic time series dynamics.