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1. LARGE TRACK IRREGULARITY DIVIATIONS OF TRACK QUALITY INDEX | |||
Wang Jiannan,Wang Xiaoliang | |||
Transportation Engineering 20 March 2013 | |||
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Abstract:In this paper, track quality index(TQI), as a comprehensive evaluation of track status, provides an important basis for the conservation and maintenance of railway tracks. Currently implemented TQI is obtained by the dispersion degree of seven main track state parameters. When most track state parameters are excellent, while individual larger deviations appears, track quality index can not accurately reflect the security risks of the track. A set of new correction factors are introduced into the track quality index algorithm. In the situations mentioned, corrected TQI can detects the quality of the track defects. At the same time, the results in other cases are similar to the original algorithm, which means the original classification standard still works. | |||
TO cite this article:Wang Jiannan,Wang Xiaoliang. LARGE TRACK IRREGULARITY DIVIATIONS OF TRACK QUALITY INDEX[OL].[20 March 2013] http://en.paper.edu.cn/en_releasepaper/content/4531513 |
2. Car Delay Time near a Curbside Stop under Mixed Traffic Condition | |||
YANG Xiaobao,HUAN Mei,FAN Aihua,DONG Ling,LI Ming | |||
Transportation Engineering 01 January 2013 | |||
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Abstract:This paper proposes a delay model to cars under mixed traffic flow near the curbside stop based on queuing theory. Bus stream system is described by an M/M/k queue and the service counter is the bus stop. The conflict between different streams at the stop is developed by the advanced Markovian model with unequal arrival rates and unequal service rates for customers of two major types. The delay to cars near the stop under mixed traffic flow can be derived by the queuing models. The average car delay is estimated as the sum of the average delay at the conflict points between different streams, and the delay resulting from following the slower bicycles that merged into the motorized lane. Car delay model is the function of three types of traffic streams with buses, cars and bicycles. The proposed model may be applicable to design and operational analysis of bus stops in other Asian developing countries. | |||
TO cite this article:YANG Xiaobao,HUAN Mei,FAN Aihua, et al. Car Delay Time near a Curbside Stop under Mixed Traffic Condition[OL].[ 1 January 2013] http://en.paper.edu.cn/en_releasepaper/content/4511337 |
3. Empirical mode decomposition of traffic time series | |||
Jianhai Yue,Keqiang Dong,Pengjian Shang | |||
Transportation Engineering 09 February 2010 | |||
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Abstract:We apply an important tool to extract the traffic cycle signal from traffic data. An alternative to traditional analysis is a non-linear empirical mode decomposition (EMD) method. This method is adaptive and therefore highly efficient at identifying embedded structures, even those with small amplitudes. Using this analysis, the traffic time series are completely decomposed into five non-stationary temporal modes including a 24-hour cycle signal, a 1-week cycle signal and a trend. It indicates that EMD can be used to analyze and interpret the traffic flow | |||
TO cite this article:Jianhai Yue,Keqiang Dong,Pengjian Shang. Empirical mode decomposition of traffic time series[OL].[ 9 February 2010] http://en.paper.edu.cn/en_releasepaper/content/40111 |
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