A hybrid deep learning approach for urban expressway travel time prediction considering spatial-temporal features

2017 ◽  
Author(s):  
Zhihao Zhang ◽  
Peng Chen ◽  
Yunpeng Wang ◽  
Guizhen Yu
Keyword(s):  
Deep Learning ◽  
Travel Time ◽  
Learning Approach ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Urban Expressway ◽  
Temporal Features

scholarly journals Travel Time Prediction and Explanation with Spatio-Temporal Features: A Comparative Study

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 106
Author(s):  
Irfan Ahmed ◽  
Indika Kumara ◽  
Vahideh Reshadat ◽  
A. S. M. Kayes ◽  
Willem-Jan van den Heuvel ◽  
...  
Keyword(s):  
Travel Time ◽  
Prediction Algorithm ◽  
Data Sets ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Temporal Features ◽  
Box Models ◽  
Challenging Tasks ◽  
Spatio Temporal ◽  
Accident Detection

Travel time information is used as input or auxiliary data for tasks such as dynamic navigation, infrastructure planning, congestion control, and accident detection. Various data-driven Travel Time Prediction (TTP) methods have been proposed in recent years. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm. The existing studies that empirically compare different TTP models only use a few models with specific features. Moreover, there is a lack of research on explaining TTPs made by black-box models. Such explanations can help to tune and apply TTP methods successfully. To fill these gaps in the current TTP literature, using three data sets, we compare three types of TTP methods (ensemble tree-based learning, deep neural networks, and hybrid models) and ten different prediction algorithms overall. Furthermore, we apply XAI (Explainable Artificial Intelligence) methods (SHAP and LIME) to understand and interpret models’ predictions. The prediction accuracy and reliability for all models are evaluated and compared. We observed that the ensemble learning methods, i.e., XGBoost and LightGBM, are the best performing models over the three data sets, and XAI methods can adequately explain how various spatial and temporal features influence travel time.

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