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Development of a Hybrid Prediction Model for Freeway Incident Duration: A Case Study in Maryland

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Abstract

This paper presents a methodology for estimating the incident duration and identifying its critical contributing factors in the state of Maryland. The incident database from the Maryland State Highway (MDSHA) and Police Accident Report database between years 2003 and 2005 were used for model development. This study employed a hybrid model to develop the primary estimation system, consisting of a Rule-Based Tree Model (RBTM), Multinomial Logit Model (MNL), and Naïve Bayesian Classifier (NBC). Through the extensive data analysis and model estimation, we have identified some critical relationships between the set of key factors and the resulting incident duration. The proposed model along with research findings can play a vital role for traffic agencies to establish an advanced traveler information system, and to provide the incident-induced delay to both pre-trip and the en-route drivers.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Maryland at College Park, 1173 G. Martin Hall at UMCP, College Park, MD, 20742, USA

    Woon Kim & Gang-Len Chang

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  1. Woon Kim
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  2. Gang-Len Chang
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Correspondence to Woon Kim.

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Kim, W., Chang, GL. Development of a Hybrid Prediction Model for Freeway Incident Duration: A Case Study in Maryland. Int. J. ITS Res. 10, 22–33 (2012). https://doi.org/10.1007/s13177-011-0039-8

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  • DOI: https://doi.org/10.1007/s13177-011-0039-8

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