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Detecting Outliers for Improving the...

Lu, Yi-Chi.

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Detecting Outliers for Improving the Quality of Incident Duration Prediction.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Detecting Outliers for Improving the Quality of Incident Duration Prediction./
作者:	Lu, Yi-Chi.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	95 p.
附註:	Source: Masters Abstracts International, Volume: 83-02.
Contained By:	Masters Abstracts International83-02.
標題:	Transportation. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28315647
ISBN:	9798534653335

Detecting Outliers for Improving the Quality of Incident Duration Prediction.
Lu, Yi-Chi.

Detecting Outliers for Improving the Quality of Incident Duration Prediction. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 95 p.

Source: Masters Abstracts International, Volume: 83-02.

Thesis (M.S.)--University of Maryland, College Park, 2021.

This item must not be sold to any third party vendors.

To circumvent the needs of domain expertise and the excessive data for developing a knowledge-based prediction system such as the I-95 incident duration estimation model, this study has developed an efficient transferability analysis method to assess the applicability of adopting the prediction rules from an existing well-developed model to a different highway. The proposed analysis method has considered the common nature of incident response operations and local-specific incident characteristics in assessing the transferability of available knowledge-based rules for estimating the required clearance duration of different types of incidents. Evaluation of the proposed method with the I-695 incident records clearly shows that the prediction model developed with such an effective transferring method can achieve the same level of performance as with the original rule-searching and refinement method.Since most incident records for model development are collected on-line during the emergency incident response process, some of the key data are likely to be misrecorded which inevitably causes many existing models to yield undesirable performance, especially with respect to those incidents with insufficient records or excessive long duration. As such, this study has also developed a two-phase outlier detection process for identifying outliers and removing those viewed as faulty records from the dataset for model calibration and model evaluation. Using the I-695 incident records for a case study, the resulting performance of the proposed two-phase outlier detection process has proved its promising property for filtering faculty data from the incident records prior to the use for model development.

ISBN: 9798534653335Subjects--Topical Terms:

555912
Transportation.
Subjects--Index Terms:

Incident response operations

Detecting Outliers for Improving the Quality of Incident Duration Prediction.
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520 $a To circumvent the needs of domain expertise and the excessive data for developing a knowledge-based prediction system such as the I-95 incident duration estimation model, this study has developed an efficient transferability analysis method to assess the applicability of adopting the prediction rules from an existing well-developed model to a different highway. The proposed analysis method has considered the common nature of incident response operations and local-specific incident characteristics in assessing the transferability of available knowledge-based rules for estimating the required clearance duration of different types of incidents. Evaluation of the proposed method with the I-695 incident records clearly shows that the prediction model developed with such an effective transferring method can achieve the same level of performance as with the original rule-searching and refinement method.Since most incident records for model development are collected on-line during the emergency incident response process, some of the key data are likely to be misrecorded which inevitably causes many existing models to yield undesirable performance, especially with respect to those incidents with insufficient records or excessive long duration. As such, this study has also developed a two-phase outlier detection process for identifying outliers and removing those viewed as faulty records from the dataset for model calibration and model evaluation. Using the I-695 incident records for a case study, the resulting performance of the proposed two-phase outlier detection process has proved its promising property for filtering faculty data from the incident records prior to the use for model development.
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