The rule-based incident duration prediction model (IDPM), covering Interstate highways I-95, I-495, and I-695, has been adopted by the Maryland Department of Transportation State Highway Administration in its daily responses to non-recurrent congestion. In light of its effectiveness and robustness in practice, expanding such a system to all other highways emerges as desirable but a challenging task, because of the need to integrate field operators’ expertise in generating prediction rules and the dependence on sufficient incident records for key parameter calibration. To circumvent such a data-demanding and time-consuming process for knowledge acquisition and refinement for extending the IDPM’s spatial coverage, this study has proposed a knowledge transferability analysis (KTA) method, featuring its automated process to assess, select, and transfer existing prediction rules to perform incident duration estimate for the new target highway. Evaluation of the proposed KTA with the incident records from Maryland I-70, using both transferred and customized local rules, reveals that it can achieve accuracy of 87% with the training dataset (i.e., 2016–2018) and 82% with the test dataset (i.e., 2019), comparable to the current system’s performance but demanding much fewer incident records for model calibration and significantly less effort for system expansion.
Extending the I-95 Rule-Based Incident Duration System With an Automated Knowledge Transferability Model
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 2676 , 8 ; 221-235
2022-03-03
Article (Journal)
Electronic Resource
English
Transferability of Freeway Incident Detection Algorithms
Online Contents | 1996
|Transferability of Freeway Incident Detection Algorithms
Transportation Research Record | 1996
|Transferability of Freeway Incident Detection Algorithms
Online Contents | 1996
|Transferability of Freeway Incident Detection Algorithms
Transportation Research Record | 1996
|Transferability of Freeway Incident Detection Algorithms
British Library Conference Proceedings | 1996
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