Applying Machine Learning and Statistical Approaches for Travel Time Estimation in Partial Network Coverage

The objective of this study is to estimate the real time travel times on urban networks that are partially covered by moving sensors. The study proposes two machine learning approaches; the random forest (RF) model and the multi-layer feed forward neural network (MFFN) to estimate travel times on urban networks which are partially covered by moving sensors. A MFFN network with three hidden layers was developed and trained using the back-propagation learning algorithm, and the neural weights were optimized using the Levenberg–Marquardt optimization technique. A case study of an urban network with 100 links is considered in this study. The performance of the proposed models was compared to a statistical model, which uses the empirical Bayes (EB) method and the spatial correlation between travel times. The models’ performances were evaluated using data generated from VISSIM microsimulation model. Results show that the machine learning algorithms, e.g., RF and ANN, achieve average improvements of about 4.1% and 2.9% compared with the statistical approach. The RF, MFFN, and the statistical approach models correctly predict real time travel times with estimation accuracies reaching 90.7%, 89.5%, and 86.6% respectively. Moreover, results show that at low moving sensor penetration rate, the RF and MFFN achieve higher estimation accuracy compared with the statistical approach. At probe penetration rate of 1%, the RF, MFFN, and the statistical approach models correctly predict real time travel times with estimation accuracy of 85.6%, 84.4%, and 80.9% respectively. Furthermore, the study investigated the impact of the probe penetration rate on real time neighbor links coverage. Results show that at probe penetration rates of 1%, 3%, and 5%, the models cover the estimation of real time travel times on 73.8%, 94.8%, and 97.2% of the estimation intervals.