scholarly journals Link travel time prediction for decentralized route guidance architectures

2000 ◽  
Vol 1 (1) ◽  
pp. 4-14 ◽  
Author(s):  
K.E. Wunderlich ◽  
D.E. Kaufman ◽  
R.L. Smith
Keyword(s):  
Travel Time ◽  
Route Guidance ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Link Travel Time

scholarly journals A dynamic shortest path algorithm using multi-step ahead link travel time prediction

2005 ◽  
Vol 39 (1) ◽  
pp. 5-18 ◽  
Author(s):  
Young-Ihn Lee ◽  
Seungjae Lee ◽  
Shinhae Lee ◽  
Jeunggyu Chon
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Shortest Path Algorithm ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Link Travel Time

scholarly journals Modelling travel time uncertainty in urban networks based on floating taxi data

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Dietmar Bauer ◽  
Mirsad Tulic ◽  
Wolfgang Scherrer
Keyword(s):  
Travel Time ◽  
Prediction Errors ◽  
Travel Times ◽  
Model Errors ◽  
Travel Time Prediction ◽  
Data Set ◽  
Time Uncertainty ◽  
Time Prediction ◽  
Travel Time Uncertainty ◽  
Link Travel Time

Abstract The prediction of the uncertainty of route travel time predictions for all possible routes in an urban road network is of importance for example for logistics. Such predictions need to take the essential features of the data set as well as the underlying traffic dynamics into account.In this paper a large floating taxi data set is used in order to derive predictions of route travel time uncertainty based on link travel time uncertainty predictions. Prediction errors, that is actual travel times minus predicted travel times, are differentiated from model errors, that is measured travel times minus predicted travel times. These two errors are related, but not identical, as model errors contain measurement noise while the prediction errors do not. Detailed models for the variance of the link travel time prediction errors as well as the correlation between the model errors for different links are derived. The models are validated in depth using two different validation data sets.Estimates for the variance of prediction errors are obtained. The standardized model error distributions show a remarkable stability, such that modelling the variance appears to be sufficient for quantifying the uncertainty of the model errors.Furthermore we show that the model errors for adjacent links are highly correlated but correlations fade with increasing distance. Additionally usage of the road network plays a role with high correlation for links along common routes and low correlations for links along seldom used routes. We assume identical features for the prediction errors which is partly validated based on additional data.The paper provides a way to estimate the complete distribution of route travel time prediction errors for any given route in the street network.

Vehicle Travel Time Prediction in Spatio-Temporal Space

2012 ◽  
Vol 253-255 ◽  
pp. 1662-1665
Author(s):  
Qing Li ◽  
William H. K. Lam ◽  
Mei Lam Tam
Keyword(s):  
Travel Time ◽  
Travel Times ◽  
Spatial Correlations ◽  
Travel Time Prediction ◽  
Time Data ◽  
Time Prediction ◽  
Temporal Space ◽  
Temporal And Spatial ◽  
Spatio Temporal ◽  
Link Travel Time

It is recognized that travel times on a link are temporally correlated with its travel times of previous time periods. Also, the link travel time are spatially correlated by travel times on its neighboring links. Based on such temporal and spatial correlations, a new method is proposed for travel time prediction in urban roads. The proposed method is capable of rapidly predicting the link travel time in the near future. For validation of the proposed method, the temporal and spatial variance-covariance of travel times on related links are employed together with historical travel time data. It is found that the proposed method is able to provide more accurate travel time prediction.

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