DocumentCode :
2946005
Title :
Traffic Accident Macro Forecast Based on ARIMAX Model
Author :
Li, Chunyan ; Chen, Jun
Author_Institution :
Coll. of Transp., Southeast Univ., Nanjing, China
Volume :
3
fYear :
2009
fDate :
11-12 April 2009
Firstpage :
633
Lastpage :
636
Abstract :
To overcome the deficiency of traditional traffic accident estimation models, this paper introduced a new way. It gave two categories on traffic accident affected factors and selected the main ones, using stepwise regression model. ARIMAX model, a dynamic regression one, was used to forecast traffic accident volumes. The former job ensures the precision of estimation, while the latter one owns both regression and ETS modelspsila merits. The example of traffic accident data from 1983 to 2005 in China was taken to validate the feasibility of the model. It established the relationship between dead people due to traffic accident and total population, mileage of road rank, the number of passenger transport, the population of drivers, the average GDP and automobile numbers, and gave an estimation adopting ARIMAX model. The result shows that the error is small and it has a much better foreground on traffic accident macro forecast.
Keywords :
automobiles; autoregressive moving average processes; economic indicators; forecasting theory; regression analysis; road accidents; road traffic; ARIMAX model; automobile number; average GDP; passenger transport; road rank mileage; stepwise regression model; traffic accident macro forecast; Automobiles; Economic indicators; Educational institutions; Predictive models; Road accidents; Road transportation; Road vehicles; Safety; Traffic control; Vehicle driving; ARIMAX model; Macroscopically Forecast; select factors; stepwise regression;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Measuring Technology and Mechatronics Automation, 2009. ICMTMA '09. International Conference on
Conference_Location :
Zhangjiajie, Hunan
Print_ISBN :
978-0-7695-3583-8
Type :
conf
DOI :
10.1109/ICMTMA.2009.250
Filename :
5203284
Link To Document :
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