Estimating Hourly Traffic Volumes using Artificial Neural Network with Additional Inputs from Automatic Traffic Recorders

Abstract

Traffic volumes are an essential input to many highway planning and design models; however, collecting this data for all road network segments is neither practical nor cost-effective. Accordingly, transportation agencies must find ways to leverage limited ground truth volume data to obtain reasonable estimates at scale on the statewide network. This paper aims to investigate the impact of selecting a subset of available automatic traffic recorders (ATRs) (i.e., the ground truth volume data source) and incorporating their data as explanatory variables into a previously developed machine learning regression model for estimating hourly traffic volumes. The study introduces a handful of strategies for selecting this subset of ATRs and walks through the process of choosing them and training models using their data as additional inputs using the New Hampshire road network as a case study. The results reveal that the overall performance of the artificial neural network (ANN) machine learning model improves with the additional inputs of selected ATRs. However, this improvement is more significant if the ATRs are selected based on their spatial distribution over the traffic message channel (TMC) network. For instance, selecting eight ATR stations according to the TMC coverage-based strategy and training the ANN with their inputs leads to average relative reductions of 35.39% and 13.44% in the mean absolute percentage error (MAPE) and error to maximum flow ratio (EMFR), respectively. The results achieved by this study can be further expanded to create a practical strategy for optimizing the number and location of ATRs through transportation networks in a state.