A Comparison of the Performace of ANN and SVM for the Prediction of Traffic Accident Duration

Title:

A Comparison of the Performace of ANN and SVM for the Prediction of Traffic Accident Duration

Creator:

Yu, B., Wang, Y. T., Yao, J. B. , and Wang, J. Y.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:c5ac7528-51c3-496d-9ede-207438dd111d
uuid:c5ac7528-51c3-496d-9ede-207438dd111d
doi:10.14311/NNW.2016.26.015

Subject:

preddiction, freeway accidents duration, artificial neural networks, and Support Vector Machines

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The prediction of traffic accident duration is great significant for rapid disposal of traffic accidents, especially for fast rescue of traffic accidents and re- moving traffic safety hazards. In this paper, two methods, which are based on artificial neural network (ANN) and support vector machine (SVM), are adopted for the accident duration prediction. The proposed method is demonstrated by a case study using data on approximately 235 accidents that occurred on freeways located between Dalian and Shenyang, from 2012 to 2014. The mean absolute error (MAE), the root mean square error (RMSE) and the mean absolute percentage error (MAPE) are used to evaluate the performances of the two measures. The conclusions are as follows: Both ANN and SVM models had the ability to predict traffic accident duration within acceptable limits. The ANN model gets a better result for long duration incident cases. The comprehensive performance of the SVM model is better than the ANN model for the traffic accident duration prediction.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2016 Volume:26 | Number:3

Harvested from:

CDK

Metadata only:

false