How long will the traffic flow time series keep efficacious to forecast the future?

PengCheng Yuan and XuXun Lin

Physica A: Statistical Mechanics and its Applications, 2017, vol. 467, issue C, 419-431

Abstract: This paper investigate how long will the historical traffic flow time series keep efficacious to forecast the future. In this frame, we collect the traffic flow time series data with different granularity at first. Then, using the modified rescaled range analysis method, we analyze the long memory property of the traffic flow time series by computing the Hurst exponent. We calculate the long-term memory cycle and test its significance. We also compare it with the maximum Lyapunov exponent method result. Our results show that both of the freeway traffic flow time series and the ground way traffic flow time series demonstrate positively correlated trend (have long-term memory property), both of their memory cycle are about 30 h. We think this study is useful for the short-term or long-term traffic flow prediction and management.

Keywords: Traffic flow time series; Long memory; Modified rescaled range analysis; Maximum Lyapunov exponent (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:467:y:2017:i:c:p:419-431

DOI: 10.1016/j.physa.2016.10.020

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