Data mining method for incident duration prediction

Communications in Computer and Information Science

Volumn 224 CCIS, Issue PART 1, 2011, Pages 484-492

  Shen, Luou ^a   Huang, Min ^b  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b WUHAN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Bayesian Network; Data Mining; Incident Duration Prediction; Incident Management

Indexed keywords

ACCURATE PREDICTION; ANALYSIS RESULTS; BAYESIAN NETWORK MODELS; DATA MINING METHODS; DATA MINING TECHNIQUES; INCIDENT DURATION; INCIDENT MANAGEMENT; PREDICTION MODEL; REAL TIME; TRAFFIC INCIDENTS;

BAYESIAN NETWORKS; DISTRIBUTED PARAMETER NETWORKS; FORECASTING; INFERENCE ENGINES; INFORMATION DISSEMINATION; INFORMATION SCIENCE; INTELLIGENT NETWORKS; MATHEMATICAL MODELS;

DATA MINING;

EID: 80052377506     PISSN: 18650929     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-23214-5_64     Document Type: Conference Paper

References (14)

1. Lindley, J.: Urban freeway congestion: quantification of the problem and effectiveness of potential solutions. ITE Journal 57, 27-32 (1987)
2. Giuliano, G.: Incident characteristics, frequency, and duration on a high volume urban freeway. Transportation Research Part A 23, 387-396 (1989)
3. Ozbay, K., Kachroo, P.: Incident Management in Intelligent Transportation Systems. Artech House, Boston (1999)
4. Khattak, A.J., Schofer, J.L., Wang, M.: A simple time-sequential procedure for predicting freeway incident duration. IVHS Journal 2, 113-138 (1995)
5. Garib, A., Radwan, A.E., Al-Deek, H.: Estimating magnitude and duration of incident delays. Journal of Transportation Engineering, ASCE 123, 459-466 (1997)
6. Nam, D., Mannering, F.: An exploratory hazard-based analysis of highway incident Duration. Transportation Research Part A 34(2), 85-102 (2000)
7. Qi, Y., Teng, H.: An information-based time sequential approach to online incident duration prediction. Journal of Intelligent Transportation Systems 12, 1-12 (2008)
8. Smith, K., Smith, B.: Forecasting the clearance time of freeway accidents. Publication STL-2001-012. Center for Transportation Studies, University of Virginia (2002)
9. Stephen, B., David, F., Travis, W.S.: Naïve Bayesian Classifier for Incident Duration Prediction. In: Proceeding of Transportation Research Board 86th Annual Meeting, Transportation Research Board, Washington D.C (2007)
10. Jiyang, B., Zhang, X., Sun, L.: Traffic incident duration prediction grounded on Bayesian decision method-based tree algorithm. Journal of Tongji University (Natural Science) (2008)
11. Kim, W., Natarajan, S., Chang, G.: Empirical analysis and modeling of freeway incident duration. In: Proceeding of 11th International IEEE Conference on Intelligent Transportation System, Beijing, pp. 453-457 (2008)
12. Wei, C., Lee, Y.: Sequential forecast of incident duration using Artificial Neural Network models. Accident Analysis and Prevention 399, 44-954 (2007)
13. David, H.: A tutorial on learning with Bayesian networks. Report: MSR-TR-95-06, Microsoft Research (1995)
14. Witten, I.H., Frank, E.: Data Mining Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann Publishers, San Francisco (2005)