Travel Time Forecasting with Combination of Spatial-Temporal and Time Shifting Correlation in CNN-LSTM Neural Network

Author(s):  
Wenjing Wei ◽  
Xiaoyi Jia ◽  
Yang Liu ◽  
Xiaohui Yu
2008 ◽  
Author(s):  
G. Ghiani ◽  
D. Gullì ◽  
F. Mari ◽  
R. Simino ◽  
R. Trunfio

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.


2016 ◽  
Vol 12 (2) ◽  
pp. 9043835 ◽  
Author(s):  
Guangyu Zhu ◽  
Li Wang ◽  
Peng Zhang ◽  
Kang Song

2019 ◽  
Vol 120 ◽  
pp. 426-435 ◽  
Author(s):  
Niklas Christoffer Petersen ◽  
Filipe Rodrigues ◽  
Francisco Camara Pereira

2020 ◽  
Vol 1651 ◽  
pp. 012190
Author(s):  
Fangyi Deng ◽  
Pei Su ◽  
Bingxue Luo ◽  
Peng Wu ◽  
Yan Guo

2007 ◽  
Vol 11 (1) ◽  
pp. 14-29 ◽  
Author(s):  
Dong-ho Kim ◽  
Dongjoo Park ◽  
Jeong-hyun Rho ◽  
Seungkirl Baek ◽  
Seong Namkoong

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