scholarly journals Urban Link Travel Time Prediction Based on a Gradient Boosting Method Considering Spatiotemporal Correlations

2016 ◽  
Vol 5 (11) ◽  
pp. 201 ◽  
Author(s):  
Faming Zhang ◽  
Xinyan Zhu ◽  
Tao Hu ◽  
Wei Guo ◽  
Chen Chen ◽  
...  
Keyword(s):  
Travel Time ◽  
Gradient Boosting ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Boosting Method ◽  
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 A Freeway Travel Time Prediction Method Based on An XGBoost Model

2021 ◽  
Vol 13 (15) ◽  
pp. 8577
Author(s):  
Zhen Chen ◽  
Wei Fan
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Model Performance ◽  
Route Planning ◽  
Prediction Method ◽  
Gradient Boosting ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Vehicle Data ◽  
Comparison Results

Travel time prediction plays a significant role in the traffic data analysis field as it helps in route planning and reducing traffic congestion. In this study, an XGBoost model is employed to predict freeway travel time using probe vehicle data. The effects of different parameters on model performance are investigated and discussed. The optimized model outputs are then compared with another well-known model (i.e., Gradient Boosting model). The comparison results indicate that the XGBoost model has considerable advantages in terms of both prediction accuracy and efficiency. The developed model and analysis results can greatly help the decision makers plan, operate, and manage a more efficient highway system.

scholarly journals Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses

2021 ◽  
Vol 13 (13) ◽  
pp. 7454
Author(s):  
Bo Qiu ◽  
Wei (David) Fan
Keyword(s):  
Machine Learning ◽  
Travel Time ◽  
Numerical Results ◽  
Machine Learning Algorithms ◽  
Prediction Algorithm ◽  
Gradient Boosting ◽  
Travel Time Prediction ◽  
Short Term ◽  
Time Prediction ◽  
Extreme Gradient Boosting

Due to the increasing traffic volume in metropolitan areas, short-term travel time prediction (TTP) can be an important and useful tool for both travelers and traffic management. Accurate and reliable short-term travel time prediction can greatly help vehicle routing and congestion mitigation. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm using the available data. In this study, the travel time data was provided and collected from the Regional Integrated Transportation Information System (RITIS). Then, the travel times were predicted for short horizons (ranging from 15 to 60 min) on the selected freeway corridors by applying four different machine learning algorithms, which are Decision Trees (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory neural network (LSTM). Many spatial and temporal characteristics that may affect travel time were used when developing the models. The performance of prediction accuracy and reliability are compared. Numerical results suggest that RF can achieve a better prediction performance result than any of the other methods not only in accuracy but also with stability.

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