Publicação
Hot-Spot Identification: a Categorical Binary Model Approach
| Resumo: | An alternative methodology is presented for hot-spot identification based on a probabilistic model. In this method, the ranking criterion for hot-spot identification conveys the probability of a site's being a hot spot or not being a hot spot. A binary choice model is used to link the outcome to a set of factors that characterize the risk of the sites under analysis on the basis of two categories (0/1) for the dependent variable. The proposed methodology consists of two main steps. After a threshold value for the number of accidents is set to distinguish hot spots from safe sites (Category 1 or 0, respectively), a binary model based on this classification is applied. This model allows the construction of a site list ordered by using the probability of a site's being a hot spot. In the second step, the selection strategy can target a fixed number of sites with the greatest probability or all sites exceeding a specific probability, such as .5. To demonstrate the proposed methodology, simulated urban intersection data from Porto, Portugal, covering 5 years are used. The results of the binary model show a good fit. To evaluate and compare the probabilistic method with other commonly used methods, the performance of each method is tested by its power to detect true hot spots. The test results indicate the superiority of the proposed method. This method is simple to apply, and critical issues such as assumptions of a prior distribution effect and the regression-to-the-mean phenomenon are overcome. Further, the model provides a realistic and intuitive perspective. |
|---|---|
| Autores principais: | Sara Ferreira |
| Outros Autores: | António Fidalgo Couto |
| Assunto: | Engenharia civil, Engenharia civil Civil engineering, Civil engineering |
| Ano: | 2013 |
| País: | Portugal |
| Tipo de documento: | livro |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade do Porto |
| Idioma: | inglês |
| Origem: | Repositório Aberto da Universidade do Porto |
| _version_ | 1867768296479129600 |
|---|---|
| author | Sara Ferreira |
| author2 | António Fidalgo Couto |
| author2_role | author |
| author_facet | Sara Ferreira António Fidalgo Couto |
| author_role | author |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Sara Ferreira\"},{\"Person.name\":\"António Fidalgo Couto\"}] |
| datacite.creators.creator.creatorName.fl_str_mv | Sara Ferreira António Fidalgo Couto |
| datacite.date.Accepted.fl_str_mv | 2013-01-01T00:00:00Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| datacite.subjects.subject.fl_str_mv | Engenharia civil, Engenharia civil Civil engineering, Civil engineering |
| datacite.titles.title.fl_str_mv | Hot-Spot Identification: a Categorical Binary Model Approach |
| dc.creator.none.fl_str_mv | Sara Ferreira António Fidalgo Couto |
| dc.date.Accepted.fl_str_mv | 2013-01-01T00:00:00Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/10216/96269 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| dc.subject.none.fl_str_mv | Engenharia civil, Engenharia civil Civil engineering, Civil engineering |
| dc.title.fl_str_mv | Hot-Spot Identification: a Categorical Binary Model Approach |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_2f33 |
| description | An alternative methodology is presented for hot-spot identification based on a probabilistic model. In this method, the ranking criterion for hot-spot identification conveys the probability of a site's being a hot spot or not being a hot spot. A binary choice model is used to link the outcome to a set of factors that characterize the risk of the sites under analysis on the basis of two categories (0/1) for the dependent variable. The proposed methodology consists of two main steps. After a threshold value for the number of accidents is set to distinguish hot spots from safe sites (Category 1 or 0, respectively), a binary model based on this classification is applied. This model allows the construction of a site list ordered by using the probability of a site's being a hot spot. In the second step, the selection strategy can target a fixed number of sites with the greatest probability or all sites exceeding a specific probability, such as .5. To demonstrate the proposed methodology, simulated urban intersection data from Porto, Portugal, covering 5 years are used. The results of the binary model show a good fit. To evaluate and compare the probabilistic method with other commonly used methods, the performance of each method is tested by its power to detect true hot spots. The test results indicate the superiority of the proposed method. This method is simple to apply, and critical issues such as assumptions of a prior distribution effect and the regression-to-the-mean phenomenon are overcome. Further, the model provides a realistic and intuitive perspective. |
| dirty | 0 |
| eu_rights_str_mv | restrictedAccess |
| format | book |
| id | rabertoup_ab4d714dbe62ebdf90254b3c19fa2413 |
| identifier.url.fl_str_mv | https://hdl.handle.net/10216/96269 |
| instacron_str | UP |
| institution | Universidade do Porto |
| instname_str | Universidade do Porto |
| language | eng |
| network_acronym_str | rabertoup |
| network_name_str | Repositório Aberto da Universidade do Porto |
| oai_identifier_str | oai:repositorio-aberto.up.pt:10216/96269 |
| organization_str_mv | urn:organizationAcronym:up |
| person_str_mv | Sara Ferreira António Fidalgo Couto |
| publishDate | 2013 |
| reponame_str | Repositório Aberto da Universidade do Porto |
| repository_id_str | urn:repositoryAcronym:rabertoup |
| service_str_mv | urn:repositoryAcronym:rabertoup |
| spelling | An alternative methodology is presented for hot-spot identification based on a probabilistic model. In this method, the ranking criterion for hot-spot identification conveys the probability of a site's being a hot spot or not being a hot spot. A binary choice model is used to link the outcome to a set of factors that characterize the risk of the sites under analysis on the basis of two categories (0/1) for the dependent variable. The proposed methodology consists of two main steps. After a threshold value for the number of accidents is set to distinguish hot spots from safe sites (Category 1 or 0, respectively), a binary model based on this classification is applied. This model allows the construction of a site list ordered by using the probability of a site's being a hot spot. In the second step, the selection strategy can target a fixed number of sites with the greatest probability or all sites exceeding a specific probability, such as .5. To demonstrate the proposed methodology, simulated urban intersection data from Porto, Portugal, covering 5 years are used. The results of the binary model show a good fit. To evaluate and compare the probabilistic method with other commonly used methods, the performance of each method is tested by its power to detect true hot spots. The test results indicate the superiority of the proposed method. This method is simple to apply, and critical issues such as assumptions of a prior distribution effect and the regression-to-the-mean phenomenon are overcome. Further, the model provides a realistic and intuitive perspective.application/pdfengHot-Spot Identification: a Categorical Binary Model ApproachSara FerreiraAntónio Fidalgo CoutoHandlehttps://hdl.handle.net/10216/9626920132013-01-01T00:00:00Zhttp://purl.org/coar/access_right/c_16ecrestricted accessEngenharia civil, Engenharia civilCivil engineering, Civil engineering237030 byteshttp://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://repositorio-aberto.up.pt/bitstream/10216/96269/2/68132.pdfliteraturehttp://purl.org/coar/resource_type/c_2f33book |
| spellingShingle | Hot-Spot Identification: a Categorical Binary Model Approach Sara Ferreira Engenharia civil, Engenharia civil Civil engineering, Civil engineering |
| status | SINGLETON |
| subject.fl_str_mv | Engenharia civil, Engenharia civil Civil engineering, Civil engineering |
| title | Hot-Spot Identification: a Categorical Binary Model Approach |
| title_full | Hot-Spot Identification: a Categorical Binary Model Approach |
| title_fullStr | Hot-Spot Identification: a Categorical Binary Model Approach |
| title_full_unstemmed | Hot-Spot Identification: a Categorical Binary Model Approach |
| title_short | Hot-Spot Identification: a Categorical Binary Model Approach |
| title_sort | Hot-Spot Identification: a Categorical Binary Model Approach |
| topic | Engenharia civil, Engenharia civil Civil engineering, Civil engineering |
| topic_facet | Engenharia civil, Engenharia civil Civil engineering, Civil engineering |
| url | https://hdl.handle.net/10216/96269 |
| visible | 1 |