KSCE Journal of Civil Engineering, Volume (21), No (5), Year (2017-7) , Pages (1912-1918)

Title : ( Introducing Prior Knowledge for a Hybrid Accident Prediction Model )

Authors: Ali Mansourkhaki , Abolfazl Karimpour , Hadi Sadoghi Yazdi ,

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Abstract

This study aims to address two potential issues regarding accident prediction models. (a) What are the benefits of using the prior information on the accuracy of prediction model, and (b) how to include the available prior knowledge of accident occurrences data in the prediction process? In accident databases, the prior knowledge can be defined as the most probable points in which an accident has happened in the earlier years and is closely correlated to its upcoming years. Large databases such as traffic and accident databases, inevitably contain noisy data. Therefore, to have accurate results, using approaches to alleviate the impact of these anomalies is significant. In this research, a hybrid method based on a module of prediction and a prior knowledge block is proposed. The module used for prediction is Recursive Least Square filter and Maximum a Posterior (MAP) estimator is used as the prior knowledge block. Results indicate an increase in accuracy of prediction by using the proposed hybrid model.

Keywords

, Keywords: road safety, accident prediction, prior knowledge, noisy environment, maximum a posterior
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@article{paperid:1059046,
author = {Ali Mansourkhaki and Abolfazl Karimpour and Sadoghi Yazdi, Hadi},
title = {Introducing Prior Knowledge for a Hybrid Accident Prediction Model},
journal = {KSCE Journal of Civil Engineering},
year = {2017},
volume = {21},
number = {5},
month = {July},
issn = {1226-7988},
pages = {1912--1918},
numpages = {6},
keywords = {Keywords: road safety; accident prediction; prior knowledge; noisy environment; maximum a posterior},
}

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%0 Journal Article
%T Introducing Prior Knowledge for a Hybrid Accident Prediction Model
%A Ali Mansourkhaki
%A Abolfazl Karimpour
%A Sadoghi Yazdi, Hadi
%J KSCE Journal of Civil Engineering
%@ 1226-7988
%D 2017

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