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     Research Journal of Applied Sciences, Engineering and Technology


The Comparison on for Traffic Accident Forecasting in Long and Short Period Based on Multi-layer Recursive Forecasting Method

Yu-Rende, Zhang Qiang, Zhang-Xiaohong and Huo-Lianxiu
School of Transportation and Vehicle Engineering, Shandong University of Technology, China
Research Journal of Applied Sciences, Engineering and Technology  2013  21:4052-4057
http://dx.doi.org/10.19026/rjaset.6.3509  |  © The Author(s) 2013
Received: January 26, 2013  |  Accepted: March 02, 2013  |  Published: November 20, 2013

Abstract

The multi-layer recursive forecasting method is used on road traffic accident forecasting in this study. We suggested the factors as density of population, GDP per capita, highway passenger transport, highway freight volume, highway mileage, density of road network, amount of vehicle, amount of cars per capita and environmental factors is selected by MATLAB in forecasting model. The model including autoregression item and the model including autoregression item and environmental factors is proposed. By changing the forecasting period, the forecasting results in years and months are acquired and analyzed. We conclude that the forecasting accuracy in short period is higher than in long period by comparing with results of long and short period.

Keywords:

Auto regression, environmental factors, forecasting, long period, multi-layer recursive, road traffic accident, short period,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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