Volume 4, Issue 3, May 2011, Pages 345 - 352
Incident Duration Prediction Based on Latent Gaussian Naive Bayesian classifier Dawei Li , Lin Chen , Jiangshan Ma
Dawei Li, Lin Cheng, Jiangshan Ma
Received 27 February 2011, Accepted 20 April 2011, Available Online 1 May 2011.
- https://doi.org/10.2991/ijcis.2011.4.3.8How to use a DOI?
- incident management; incident duration; Bayesian networks; LGNB classifier
- The probability distribution of duration is a critical input for predicting the potential impact of traffic incidents. Most of the previous duration prediction models are discrete, which divide duration into several intervals. However, sometimes the continuous probability distribution is needed. Therefore a continuous model based on latent Gaussian naive Bayesian (LGNB) classifier is developed in this paper, assuming duration fits a lognormal distribution. The model is calibrated and tested by incident records from the Georgia Department of Transportation. The results show that LGNB can describe the continuous probability distribution of duration well. According to the evidence sensitivity analysis of LGNB, the four classes of incidents classified by LGNB can be interpreted by the level of severity and complexity.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Dawei Li AU - Lin Cheng AU - Jiangshan Ma PY - 2011 DA - 2011/05 TI - Incident Duration Prediction Based on Latent Gaussian Naive Bayesian classifier Dawei Li , Lin Chen , Jiangshan Ma JO - International Journal of Computational Intelligence Systems SP - 345 EP - 352 VL - 4 IS - 3 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.2011.4.3.8 DO - https://doi.org/10.2991/ijcis.2011.4.3.8 ID - Li2011 ER -