Estimation of daily bicycle traffic using machine and deep learning techniques

Machine learning (ML) architecture has successfully characterized complex motorized volumes and travel patterns; however, non-motorized traffic has given less attention to ML techniques and relied on simple econometric models due to a lack of data for complex modeling. Recent advancements in smartphone-based location data that collect and process large amounts of daily bicycle activities makes the use of machine learning techniques for bicycle volume estimations possible and promising. This study develops eight modeling techniques ranging from advanced techniques, such as Convolution Neural Network (CNN), Deep Neural Network (DNN), Shallow Neural Network (SNN), Random Forest (RF), XGBoost, to conventional and simpler approaches, such as Decision Tree (DT), Negative Binomial (NB), and Multiple Linear Regression, to estimate Daily Bicycle Traffic (DBT). This study uses 6746 daily bicycle volumes collected from 178 permanent and short-term count locations from 2017 to 2019 in Portland, Oregon. A total of 45 independent variables capturing anonymous bicycle user activities (Strava count, bike share), built environments, motorized traffic, and sociodemographic characteristics create comprehensive variable sets for predictive modeling. Two variable dimension reduction techniques using principal component analysis and random forest variable importance analysis ensure that the models are not over-generalized or over-fitted with a large variable set. The comparative analysis between models shows that the SNN and DNN machine learning techniques produce higher accuracies in estimating daily bicycle volumes. The results show that the DNN models predict the DBT with a maximum mean absolute percentage error (MAPE) of 22% while the conventional model (linear regression) shows an APE of 45%.