Incident Duration Prediction Based on Latent Gaussian Naive Bayesian classifier
Tóm tắt
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.
Tài liệu tham khảo
J. Lindley, Urban freeway congestion: quantification of the problem and effectiveness of potential solutions, ITE journal, 57 (1987) 23–51.
W. Chow, A study of traffic performance models under an incident condition, Highway Research Record, 567(1974) 31–36.
T. Olmstead. Pitfall to avoid when estimating incident-induced delay by using deterministic queuing models, Transportation Research Record: Journal of the Transportation Research Board, 1683 (1999) 38–46.
C. Wirasinghe. Determination of traffic delays from shock-wave analysis, Transportation Research, 12 (5) (1978) 343–348.
H. Mongeot H and J. Lesort. Analytical Expressions of Incident-Induced Flow Dynamics Perturbations: Using Macroscopic Theory and Extension of Lighthill-Whitham Theory, Transportation Research Record: Journal of the Transportation Research Board, 1710 (2000) 58–68.
V. Sisiopiku, X. Li and etc. Dynamic Traffic Assignment Modeling for Incident Management, Transportation Research Record: Journal of the Transportation Research Board, 1994 (2007) 110–116.
T. F. Golob, W. W. Recker and J. D. Leonard. An analysis of the severity and incident duration of truck-involved freeway accidents, Accident Analysis & Prevention, 19(5) (1987) 375–395.
G. Giuliano. Incident characteristics, frequency, and duration on a high volume urban freeway. Transportation Research Part A: General, 23 (5) (1989) 387–396.
E. C. Sullivan. New Model for Predicting Freeway Incidents and Incident Delays, Journal of Transportation Engineering, 123 (4) (1997) 267–275.
A. Khattak, J. Schofer and M. H. Wang. A Simple Time Sequential Procedure For Predicting Freeway Incident Duration, Journal of Intelligent Transportation Systems, 2 (1995) 113–138.
A. Garib, A. E. Radwan and H. Al-Deek. Estimating Magnitude and Duration of Incident Delays, Journal of Transportation Engineering, 123 (6) (1997) 459–466.
K. Ozbay, P. Kachroo. Incident Management in Intelligent Transportation Systems (Boston: Artech House, 1999).
D. Nam, F. Mannering. An exploratory hazard-based analysis of highway incident duration, Transportation Research Part A: Policy and Practice, 34 (2) (2000) 85–102.
B. L. Smith, B. M. Williams and O. R. Keith. Comparison of Parametric and Nonparametric Models for Traffic Flow Forecasting, Transportation Research Part C, 10(4)(2002) 303–321.
S. Boyles, D. Fajardo and S. T. Waller. A naive Bayesian classifier for incident duration prediction, in Proc. 86th Meeting of Transportation Research Board (2007).
K. Ozbay and N. Noyan. Estimation of incident clearance times using Bayesian Networks approach, Accident Analysis & Prevention, 38(3) (2006) 542–555.
A. K. A. Castro, P. R. Pinheiro, M. C. D. Pinheiro et al. Towards the Applied Hybrid Model in Decision Making:A Neuropsychological Diagnosis of Alzheimer’s Disease Study Case, International Journal of Computational Intelligence Systems (IJCIS), 4(1) (2011):89–9.
L. Fu and L. Rilett. Real-time estimation of incident delay in dynamic and stochastic networks, Transportation Research Record: Journal of the Transportation Research Board, 1603(1997) 99–105.
K. P. Murphy. The bayes net toolbox for matlab, Computing science and statistics, 33(2000) 1024–1034.