Short-term travel time prediction by deep learning: A comparison of different LSTM-DNN models

2017 ◽  
Author(s):  
Yangdong Liu ◽  
Yizhe Wang ◽  
Xiaoguang Yang ◽  
Linan Zhang
Keyword(s):  
Deep Learning ◽  
Travel Time ◽  
Travel Time Prediction ◽  
Short Term ◽  
Time Prediction

Freeway Short-Term Travel Time Prediction Based on Dynamic Tensor Completion

2015 ◽  
Vol 2489 (1) ◽  
pp. 97-104 ◽  
Author(s):  
Huachun Tan ◽  
Qin Li ◽  
Yuankai Wu ◽  
Wuhong Wang ◽  
Bin Ran
Keyword(s):  
Travel Time ◽  
Tensor Completion ◽  
Travel Time Prediction ◽  
Short Term ◽  
Time Prediction

scholarly journals A hybrid method for short-term freeway travel time prediction based on wavelet neural network and Markov chain

2018 ◽  
Vol 45 (2) ◽  
pp. 77-86 ◽  
Author(s):  
Hang Yang ◽  
Yajie Zou ◽  
Zhongyu Wang ◽  
Bing Wu
Keyword(s):  
Markov Chain ◽  
Travel Time ◽  
Prediction Accuracy ◽  
Travel Time Prediction ◽  
Time Data ◽  
Short Term ◽  
Time Prediction ◽  
Travel Time Data ◽  
Forecasting Performance ◽  
Fitting Problem

Short-term travel time prediction is an essential input to intelligent transportation systems. Timely and accurate traffic forecasting is necessary for advanced traffic management systems and advanced traveler information systems. Despite several short-term travel time prediction approaches have been proposed in the past decade, especially for hybrid models that consist of machine learning models and statistical models, few studies focus on the over-fitting problem brought by hybrid models. The over-fitting problem deteriorates the prediction accuracy especially during peak hours. This paper proposes a hybrid model that embraces wavelet neural network (WNN), Markov chain (MAR), and the volatility (VOA) model for short-term travel time prediction in a freeway system. The purpose of this paper is to provide deeper insights into underlining dynamic traffic patterns and to improve the prediction accuracy and robustness. The method takes periodical analysis, error correction, and noise extraction into consideration and improve the forecasting performance in peak hours. The proposed methodology predicts travel time by decomposing travel time data into three components: a periodic trend presented by a modified WNN, a residual part modeled by Markov chain, and the volatility part estimated by the modified generalized autoregressive conditional heteroscedasticity model. Forecasting performance is investigated with freeway travel time data from Houston, Texas and examined by three measures: mean absolute error, mean absolute percentage error, and root mean square error. The results show that the travel times predicted by the WNN-MAR-VOA method are robust and accurate. Meanwhile, the proposed method is able to capture the underlying periodic characteristics and volatility nature of travel time data.

Sign in / Sign up

Export Citation Format

Share Document