A platform for research: civil engineering, architecture and urbanism
Combined PCA and NB to Predict Traffic Incident Duration
Abstract An accurate prediction of incident duration plays an important role in obtaining traffic information for travelers timely and making appropriate decisions for traffic managers. As the characteristics are significantly different from each other, models were established for each type of incident, i.e. stopped-vehicle incidents, lost-load incidents and accidents. After data pretreatment, Principal component analysis (PCA) was carried out for each type of incident. Afterward, Naive Bayes (NB) model was applied for the data processed after PCA to predict incident durations. The experimental results indicated that the model obtained high prediction accuracy for those incidents which lasted less than 60 min and the prediction performance of accidents worked best. Besides, the prediction accuracy was 77.46%, 82.08% and 86.34% for each type of incident within 20 min’ error, respectively. In conclusion, the results showed that the combined model of PCA and NB is a promising application to predict incident duration.
Combined PCA and NB to Predict Traffic Incident Duration
Abstract An accurate prediction of incident duration plays an important role in obtaining traffic information for travelers timely and making appropriate decisions for traffic managers. As the characteristics are significantly different from each other, models were established for each type of incident, i.e. stopped-vehicle incidents, lost-load incidents and accidents. After data pretreatment, Principal component analysis (PCA) was carried out for each type of incident. Afterward, Naive Bayes (NB) model was applied for the data processed after PCA to predict incident durations. The experimental results indicated that the model obtained high prediction accuracy for those incidents which lasted less than 60 min and the prediction performance of accidents worked best. Besides, the prediction accuracy was 77.46%, 82.08% and 86.34% for each type of incident within 20 min’ error, respectively. In conclusion, the results showed that the combined model of PCA and NB is a promising application to predict incident duration.
Combined PCA and NB to Predict Traffic Incident Duration
Lao, Yechun (author) / Chen, Shuyan (author) / Song, Ningning (author)
2017-01-01
11 pages
Article/Chapter (Book)
Electronic Resource
English
Modeling Traffic Incident Duration Using Quantile Regression
British Library Online Contents | 2016
|Traffic Incident Duration Prediction Based on K-Nearest Neighbor
British Library Conference Proceedings | 2013
|Analysing freeway traffic-incident duration using an Australian data set
British Library Online Contents | 2012
|