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Entropy 2017, 19(4), 165; doi:10.3390/e19040165

An Entropy-Based Approach for Evaluating Travel Time Predictability Based on Vehicle Trajectory Data

1,2
,
1,2
,
3
and
1,2,*
1
Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
2
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
3
Kinder Institute for Urban Research, Rice University, Houston, TX 77005, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Received: 29 January 2017 / Revised: 24 March 2017 / Accepted: 7 April 2017 / Published: 11 April 2017
View Full-Text   |   Download PDF [2716 KB, uploaded 11 April 2017]   |  

Abstract

With the great development of intelligent transportation systems (ITS), travel time prediction has attracted the interest of many researchers, and a large number of prediction methods have been developed. However, as an unavoidable topic, the predictability of travel time series is the basic premise for travel time prediction, which has received less attention than the methodology. Based on the analysis of the complexity of the travel time series, this paper defines travel time predictability to express the probability of correct travel time prediction, and proposes an entropy-based method to measure the upper bound of travel time predictability. Multiscale entropy is employed to quantify the complexity of the travel time series, and the relationships between entropy and the upper bound of travel time predictability are presented. Empirical studies are made with vehicle trajectory data in an express road section to shape the features of travel time predictability. The effectiveness of time scales, tolerance, and series length to entropy and travel time predictability are analyzed, and some valuable suggestions about the accuracy of travel time predictability are discussed. Finally, comparisons between travel time predictability and actual prediction results from two prediction models, ARIMA and BPNN, are made. Experimental results demonstrate the validity and reliability of the proposed travel time predictability. View Full-Text
Keywords: travel time predictability; multiscale entropy; travel time series; vehicle trajectory data travel time predictability; multiscale entropy; travel time series; vehicle trajectory data
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xu, T.; Xu, X.; Hu, Y.; Li, X. An Entropy-Based Approach for Evaluating Travel Time Predictability Based on Vehicle Trajectory Data. Entropy 2017, 19, 165.

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