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.
Highlights
As the development of society, the forecasting is focused increasingly
Beginning from the external features of traffic system, this study look road traffic accident as a time series to found input-output model based on the characteristic of multi-level recursive forecasting method
We conclude that the forecasting accuracy in short period is higher than in long period by comparing with results of long and short period
Summary
As the development of society, the forecasting is focused increasingly. So it is caused for many researchers to discuss the forecasting methods. As the change of many factors such as parameter, forecasting period, the change of result is caused. The cause is that parameters of many forecasting models are no-time varying but forecasting object is varied continuously. The used methods include qualltative and quantitative methods It are better for the quantitative methods to describe the future tendency of road traffic accident ,so is used widely, such as fuzzy mathematics, statistics and gray theory, Xiang-Yong et al (2003), El-Basyouny and Sayed (2009), Ma et al. Beginning from the external features of traffic system, this study look road traffic accident as a time series to found input-output model based on the characteristic of multi-level recursive forecasting method.
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