Statistics and distribution characteristics analysis of time interval of blackout in Chinese power grid

The two early Cambrian seismic events could be found from sedimentary rocks at Peilingjiao section of Kaihua County, Baishi and Fangcun sections of Changshan County in western Zhejiang, except for Jiangshan area. The seismic event at Baishi outcrop can be correlated to the second seismic event at Peilingjiao section. Taking Fangcun as epicenter of the second seismic event, the magnitude of paleoseism in western Zhejiang is about 7~7.6. According to investigation on regional distribution of seismic events, the two seismic activities should be regulated by large Kaihua-Chun’an fault, but unrelated with Jiangshan-Shaoxing fault or Changshan-Xiaoshan fault. However, the formation time of Kaihua-Chun’an fault has not yet been determinate. Based on controlling on Silurian, the possible formation age was inferred to early Paleozoic. The distribution characteristics of seismites indicate that the Kaihua-Chun’an fault was already being active during early Cambrian and seismic activities may be response to Sinian tectonic events in western Zhejiang. By the way of analysis on paleoseismic rhythm, the time interval of the two seismic events in western Zhejiang is less than 5.0 Ma, which may be the result of early frequent activities of Kaihua-Chun’an fault.

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Probabilistic En Route Sector Traffic Demand Prediction Based upon Statistical Analysis of Error Distribution Characteristics

Journal of Advanced Transportation ◽

10.1155/2018/8184513 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13

Author(s):

Wen Tian ◽

Huiqing Xu ◽

Yixing Guo ◽

Bin Hu ◽

Yi Yao

Keyword(s):

Traffic Congestion ◽

Southern China ◽

Probabilistic Method ◽

Prediction Method ◽

Weather Conditions ◽

Error Distribution ◽

Time Interval ◽

Distribution Characteristics ◽

Traffic Demand ◽

Demand Prediction

In China, air traffic congestion has become increasingly prominent and tends to spread from terminal areas to en route networks. Accurate and objective traffic demand prediction could alleviate congestion effectively. However, the usual demand prediction is based on conjecture method of flying track, and the number of aircraft flying over a sector in a set time interval could be inferred through the location information of any aircraft track. In this paper, we proposed a probabilistic traffic demand prediction method by considering the deviations caused by random events, such as the change of departure or arrival time, the temporary change in route or altitude under severe weather conditions, and unscheduled cancellation for a flight. The probabilistic method quantifies these uncertain factors and presents numerical value with its corresponding probability instead of the deterministic number of aircraft in a sector during a time interval. The analysis results indicate that the probabilistic traffic demand prediction based on error distribution characteristics achieves an effective match with the realistic operation in airspace of central and southern China, which contributes to enhancing the implementation of airspace congestion risk management.

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