Advancement Through Research
There has been a wealth of literatures for the method of estimating an accurate origin-destination (O-D) trip matrix information from observed link counts since O-D trip matrix is one of the key information for a variety of transportation planning and analysis studies. A new type of applications, Machine learning (ML), recently opens a new door to transportation study including O-D trip matrix (ODME) domain so that many agencies and researchers are trying to incorporate both traditional ODME and emerging techniques including ML and Deep Learning (DL). Unfortunately, several difficulties such as limited computing power and data resources make such changes remain as unsolved. In this study, we offer a novel framework, an open-data-driven hybrid origin-destination trip matrix estimation model integrating with machine learning techniques (Open-DOMEiM). Several public resources including annual average daily traffic (AADT), public GIS map, American community survey (ACS), and average travel time data in normal and congestion conditions obtained from Google Map API, were used. Also, Several ML techniques including t-distributed stochastic neighbor embedding (t-SNE), Backpropagation (BP), Random forests (RF), and Stacked Autoencoder (SAE) algorithms which is one of the DL algorithms, were applied for this study. The t-SNE model was used as dimensional reduction algorithm to reduce computational burdens for other ML algorithms. An urban area of St. Louis, Missouri, USA was selected for the model comparisons along with one mathematical ODME model, Cho (2008). The results show that RF in conjunction with t-SNE and SAE outperformed other models for three scenarios in this study.
O-D Estimation, Google Map API, Machine Learning, Deep Learning, Open Source
