Short‐Term Traffic Flow Forecasting of Urban Rail Transit Based on Fractal Theory
2011
- 32Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage32
- Abstract Views32
Article Description
Based on the analysis of the short-term traffic flow, this paper aims to explore that the flow has a feather of randomness and nonlinearity, which has the chaotic characteristics meanwhile. As a result, the chaotic theory will probably be used to make a short-term traffic flow forecasting. Fractal interpolation is adopted in the paper to simulate data forecasting, which is based on the original data from Shanghai Metro Line 9. Compared with the actual results, the predicted data is close to it within the error band, which shows that the fractal interpolation can be used in short-term traffic flow forecasting.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know