Comparison of different Bayesian methods for estimating error bars with incident duration prediction

2021 ◽  
pp. 1-17
Author(s):  
Banishree Ghosh ◽  
Justin Dauwels
Keyword(s):  
Bayesian Methods ◽  
Incident Duration ◽  
Error Bars ◽  
Duration Prediction

Traffic Incident Duration Prediction using BERT Representation of Text

2021 ◽  
Author(s):  
Prashansa Agrawal ◽  
Antony Franklin ◽  
Digvijay Pawar ◽  
Srijith PK
Keyword(s):  
Traffic Incident ◽  
Incident Duration ◽  
Duration Prediction

scholarly journals A Hybrid Method for Traffic Incident Duration Prediction Using BOA-Optimized Random Forest Combined with Neighborhood Components Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Qiang Shang ◽  
Derong Tan ◽  
Song Gao ◽  
Linlin Feng
Keyword(s):  
Random Forest ◽  
Confusion Matrix ◽  
Prediction Method ◽  
Bayesian Optimization ◽  
Incident Management ◽  
Bayesian Optimization Algorithm ◽  
Traffic Incident ◽  
Incident Duration ◽  
Components Analysis ◽  
Duration Prediction

Predicting traffic incident duration is important for effective and real-time traffic incident management (TIM), which helps to minimize traffic congestion, environmental pollution, and secondary incident related to this incident. Traffic incident duration prediction methods often use more input variables to obtain better prediction results. However, the problems that available variables are limited at the beginning of an incident and how to select significant variables are ignored to some extent. In this paper, a novel prediction method named NCA-BOA-RF is proposed using the Neighborhood Components Analysis (NCA) and the Bayesian Optimization Algorithm (BOA)-optimized Random Forest (RF) model. Firstly, the NCA is applied to select feature variables for traffic incident duration. Then, RF model is trained based on the training set constructed using feature variables, and the BOA is employed to optimize the RF parameters. Finally, confusion matrix is introduced to measure the optimized RF model performance and compare with other methods. In addition, the performance is also tested in the absence of some feature variables. The results demonstrate that the proposed method not only has high accuracy, but also exhibits excellent reliability and robustness.

Text analysis in incident duration prediction

2013 ◽  
Vol 37 ◽  
pp. 177-192 ◽  
Author(s):  
Francisco C. Pereira ◽  
Filipe Rodrigues ◽  
Moshe Ben-Akiva
Keyword(s):  
Text Analysis ◽  
Incident Duration ◽  
Duration Prediction
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