An application of the Sequential Monte Carlo to increase the accuracy of travel time estimation in urban areas

Author(s):  
Amnir Hadachi ◽  
Christele Lecomte ◽  
Stephane Mousset ◽  
Abdelaziz Bensrhair
2009 ◽  
Vol 42 (15) ◽  
pp. 383-390
Author(s):  
W.K. Mak ◽  
F. Viti ◽  
S.P. Hoogendoorn ◽  
A. Hegyi

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhiming Gui ◽  
Haipeng Yu

Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.


Author(s):  
Wen Zhang ◽  
Yang Wang ◽  
Xike Xie ◽  
Chuancai Ge ◽  
Hengchang Liu

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