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Demand Prediction of Railway Emergency Resources Based on Case-Based Reasoning
- Source :
- Journal of Advanced Transportation, Vol 2021 (2021)
- Publication Year :
- 2021
- Publisher :
- Hindawi-Wiley, 2021.
-
Abstract
- The demand prediction of emergency resources is helpful for rational allocation and optimization of emergency resources for railway rescue when emergency incident occurs. In this paper, a case base containing China railway traffic accident that has occurred since 1978 is established, and the case-based reasoning (CBR) method is applied in railway emergency resource demand predicting research. The core case attributes of railway emergencies are described. In view of the attribute types of railway emergency cases, five types of attributes, including enumeration, numerical, interval, character and fuzzy type, are considered, and the local similarity calculation models of different attributes are given. In order to avoid the problem of missing attribute in the traditional nearest neighbor algorithm, a global case similarity calculation method based on structural similarity and attribute similarity is designed. The empirical results show that case 3 is the most similar to the target case, and the calculating quantities of the proposed model are closer to the actual usage quantity and more accurate in the demand prediction of railway emergency resources, compared with the traditional empirical method. The relative errors of demand forecasts for the 9 resources have been, respectively, reduced by 15.9884%, 15.1471%, 6.4286%, 17.1429%, 66.6667%, 38.8889%, 27.5%, 0%, and 17.7778%. Therefore, the proposed model is both reasonable and applicable. The research results are of great significance to effectively deal with railway emergencies.
- Subjects :
- Transportation engineering
TA1001-1280
Transportation and communications
HE1-9990
Subjects
Details
- Language :
- English
- ISSN :
- 01976729 and 20423195
- Volume :
- 2021
- Database :
- Directory of Open Access Journals
- Journal :
- Journal of Advanced Transportation
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.f748824a535142b0b1d35477791a6f30
- Document Type :
- article
- Full Text :
- https://doi.org/10.1155/2021/6666631