Development and Evaluation of Recurrent Neural Network based Models for Hourly Traffic Volume and AADT Prediction

The prediction of high-resolution hourly traffic volumes of a given roadway is essential for future planning in a transportation system. Traditionally, Automatic Traffic Recorders (ATR) are used to collect this hourly volume data, which is often used to predict future traffic volumes accurately. However, these large data sets generated from these various ATRs are time series data characterized by long-term temporal dependencies and missing data sets. Regarding the temporal dependencies, all roadways are characterized by seasonal variations that can be weekly, monthly or yearly, depending on the cause of the variation. Regarding the missing data in a time-series sequence, traditional time series forecasting models perform poorly under their influence. Therefore, a robust, Recurrent Neural Network (RNN) based, multi-step ahead time-series forecasting model is developed in this study. The simple RNN, the Gated Recurrent Unit (GRU) and the Long Short-Term Memory (LSTM) units are used to develop the model and evaluate its performance. Two novel approaches are used to address the missing value issue: masking and imputation, in conjunction with the RNN unit. Six different imputation algorithms are then used to identify the best model. Our analyses indicate that the LSTM model performs better than simple RNN and GRU models, and imputation performs better than masking. The performance of imputation methods is also dependent on the percentage of missing data in the input dataset. Overall, the LSTM-Median model is deemed the best model in all scenarios for AADT prediction, with an average accuracy of 98.5%.

Development of Statewide AADT Estimation Model from Short-Term Counts: A Comparative Study for South Carolina

Annual Average Daily Traffic (AADT) is an important parameter used in traffic engineering analysis. Departments of Transportation (DOTs) continually collect traffic count using both permanent count stations (i.e., Automatic Traffic Recorders or ATRs) and temporary short-term count stations. In South Carolina, 87% of the ATRs are located on interstates and arterial highways. For most secondary highways (i.e., collectors and local roads), AADT is estimated based on short-term counts. This paper develops AADT estimation models for different roadway functional classes with two machine learning techniques: Artificial Neural Network (ANN) and Support Vector Regression (SVR). The models aim to predict AADT from short-term counts. The results are first compared against each other to identify the best model. Then, the results of the best model are compared against a regression method and factor-based method. The comparison reveals the superiority of SVR for AADT estimation for different roadway functional classes over all other methods. Among all developed models for different functional roadway classes, the SVR-based model shows a minimum root mean square error (RMSE) of 0.22 and a mean absolute percentage error (MAPE) of 11.3% for the interstate/expressway functional class. This model also shows a higher R-squared value compared to the traditional factor-based model and regression model. SVR models are validated for each roadway functional class using the 2016 ATR data and selected short-term count data collected by the South Carolina Department of Transportation (SCDOT). The validation results show that the SVR-based AADT estimation models can be used by the SCDOT as a reliable option to predict AADT from the short-term counts.