Title
An Entropy-Based Approach for Evaluating Travel Time Predictability Based on Vehicle Trajectory Data.
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.
Year
DOI
Venue
2017
10.3390/e19040165
ENTROPY
Keywords
Field
DocType
travel time predictability,multiscale entropy,travel time series,vehicle trajectory data
Econometrics,Data mining,Mathematical optimization,Predictability,Upper and lower bounds,Autoregressive integrated moving average,Intelligent transportation system,Predictive modelling,Travel time,Trajectory,Empirical research,Mathematics
Journal
Volume
Issue
ISSN
19
4
1099-4300
Citations 
PageRank 
References 
2
0.38
4
Authors
4
Name
Order
Citations
PageRank
Tao Xu120.38
Xianrui Xu241.47
Yujie Hu321.05
Xiang Li411011.84