Automated Machine Learning Pipeline for Traffic Count Prediction
نویسندگان
چکیده
Research indicates that the projection of traffic volumes is a valuable tool for management. However, few studies have examined application universal automated framework car volume prediction. Within this limited literature, using broad data sets and inclusive predictors been inadequate; such works not incorporated comprehensive set linear nonlinear algorithms utilizing robust cross-validation approach. The proposed model pipeline introduced in study automatically identifies most appropriate feature-selection method modeling approach to reduce mean absolute percentage error. We utilized hyperparameter optimization generate framework, distinct from techniques rely on single case study. resulting can be independently customized any respective project. Automating much process minimizes work expertise required count forecasting. To test applicability our models, we used Florida historical between 2001 2017. results confirmed models outperformed predicting passenger vehicles’ monthly specific By employing developed study, transportation planners could identify critical links US roads incur overcapacity issues.
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ژورنال
عنوان ژورنال: Modelling
سال: 2021
ISSN: ['2673-3951']
DOI: https://doi.org/10.3390/modelling2040026