Citation

Alzidi A, Shannaq B. Webology 2022; 19(2): 7795-7811.

Copyright

(Copyright © 2022, University of Tehran, Iran, Publisher Info Sci Publisher)

DOI

unavailable

PMID

unavailable

Abstract

This work intends to build a model to predict the time hour/day/date of an accident on specific city and street location, which causes traffic congestion. The proposed model could alert the driver of an accident occurrence in specific location and time. Therefore, this Forecasting Information Model Scheme will minimize the traffic congestion as most of the drivers will change their path route. A large data set has been collected from the Traffic Database for the past 10 years.Data Mining Methodology has been customized to build the forecasting model. 4011 Time series instances have been used to train the forecasting model. The MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) have been used for Evaluation on training data. The MAE scored ~5.1, and RMSE scored ~6.8. The obtained results are promising and could be useful for improving road safety and traffic congestion strategy.

Language: en