Modelos de previsão de acidentes de trânsito em interseções semaforizadas de Fortaleza
DOI:
https://doi.org/10.4237/transportes.v20i2.558Abstract
As interseções viárias urbanas concentram grande parte dos acidentes de trânsito em virtude do elevado grau de interação entre usuários, veículos e via. Os modelos de previsão de acidentes são modelos de regressão que relacionam a frequência dos acidentes de trânsito com atributos geométricos e operacionais da via e lidam, com relativo sucesso, com o elevado grau de aleatoriedade desse fenômeno. O objetivo deste trabalho é desenvolver modelos de previsão de acidentes de trânsito para interseções semaforizadas da cidade de Fortaleza, com ênfase na metodologia para a construção de modelos simples e confiáveis. Os modelos foram estimados com uma amostra de 101 interseções em função do volume médio diário anual, número de faixas, número de aproximações e tipo de separador central. O modelo contendo o fluxo e número de faixas apresentou desempenho satisfatório para a predição do número total de acidentes de trânsito nas interseções semaforizadas avaliadas.
Palavras-chave: modelos de previsão de acidentes, acidentes de trânsito, modelos lineares generalizados, modelagem de segurança viária.
Abstract: Urban road intersections concentrate a significant portion of traffic accidents due to the high degree of interaction between users, vehicles and route. Accident prediction models are regression models that establish a relationship between the frequency of traffic accidents and geometric and operational attributes of the road, dealing, with relative success, with the high degree of randomness of this phenomenon. The objective of this work is to develop traffic accident prediction models for signalized intersections located in the city of Fortaleza, with emphasis on the methodology for the development of simple and reliable models. The models were estimated based on a sample of 101 intersections, having as prediction variables annual average daily traffic (AADT), number of lanes, number of approaches, and type of central median. The model containing AADT and number of lanes showed satisfactory performance for the estimation of the total number of traffic accidents at the evaluated intersections.
Keywords: safety prediction models, safety performance functions, generalized linear models, road safety modeling.
Downloads
Downloads
Published
How to Cite
Issue
Section
License
Authors who submit papers for publication by TRANSPORTES agree to the following terms:
- Authors retain copyright and grant TRANSPORTES the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors may enter into separate, additional contractual arrangements for the non-exclusive distribution of this journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in TRANSPORTES.
- Authors are allowed and encouraged to post their work online (e.g., in institutional repositories or on their website) after publication of the article. Authors are encouraged to use links to TRANSPORTES (e.g., DOIs or direct links) when posting the article online, as TRANSPORTES is freely available to all readers.
- Authors have secured all necessary clearances and written permissions to published the work and grant copyright under the terms of this agreement. Furthermore, the authors assume full responsibility for any copyright infringements related to the article, exonerating ANPET and TRANSPORTES of any responsibility regarding copyright infringement.
- Authors assume full responsibility for the contents of the article submitted for review, including all necessary clearances for divulgation of data and results, exonerating ANPET and TRANSPORTES of any responsibility regarding to this aspect.
