Estimating Path Travel Costs in Large-Scale Networks Using Machine-Learning Techniques

Abstract

The integration of activity-based models (ABMs) with simulation-based dynamic traffic assignment (DTA) is proposed as a prominent alternative to the traditional four-step approach to modeling transportation networks. In DTA, the least-generalized cost path-finding algorithm is executed to route travelers to their optimal paths, while ABM requires travel-cost skims of the activities, regardless of actual paths, to assess various options available to users such as destination and mode choices. These skims can be calculated either by rerunning the shortest-path-finding algorithm or storing the dynamic travel-cost skims generated at the DTA level. Having a large-scale network with multiple user classes, these methods require high memory storage and run time. Therefore, this study presents a heuristic algorithm utilizing machine-learning methods to predict origin–destination (OD) travel distances, times, and costs using historical vehicle trajectory data generated at the DTA level. This study uses an ellipse boundary around the ODs to truncate the network and extract the relevant vehicle sub-trajectories. Extracted sub-trajectories are clustered into with-toll and non-toll groups based on trip costs to estimate travel distance, time, and monetary cost accordingly. This approach can also be used in navigation systems to have a better estimate of arrival times. Numerical results for the greater Chicago network show promising performance in travel-cost measures estimation with improved solution times. To test the scalability of the proposed method for different OD pairs, the prediction of the best ellipse size is carried out using random forest and neural network methods.