Citation

Lee Y, Wei CH, Chao KC. Int. J. Intell. Transp. Syst. Res. 2018; 16(1): 26-38.

Copyright

(Copyright © 2018, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s13177-016-0135-x

PMID

unavailable

Abstract

Accurate estimation of vehicle accident queue lengths can assist traffic managers in providing improved plans for traffic control. Few studies that have analyzed the influence of highway incidents on traffic flow have discussed the length of vehicle queues during incidents. Therefore, the present study applied artificial neural network and regression methods to construct models for estimating the length of vehicle accident queues. The factors influencing the occurrence of accidents are numerous and complex. To estimate vehicle accident queue lengths, the models incorporated various data from accidents, including accident characteristics, traffic data, illumination, weather conditions, and road geometry characteristics. All raw data were collected from two public agencies and were integrated and cross-checked. Before model development, a correlation analysis was performed to reduce the scale of interrelated features or variables. In the model evaluation, the mean absolute percentage error of the most suitable model was below 50%, indicating that the proposed model and procedure provide a reasonable estimate of vehicle accident queue lengths.

Language: en