Trip Purpose Prediction Based on Hidden Markov Model with GPS and Land Use Data

Abstract

Trip purpose is vital to infer travel behavior and predict travel demand for transportation planning. Therefore, trip purpose prediction has been becoming an important field of research that can improve people's travel efficiency through travel information, such as travel mode, time, location and so on. However, there are a few challenges linked with collecting data via the surveys and the spatial complexity of human travel. To solve the above problems effectively, the study adopts GPS data and land use data and proposes a machine learning method and prediction model as forecasting process. The prediction model is used to automatically predict trip purpose, while the machine learning method is used to constantly updating the prediction model, based on surveys from participants. Compared with traditional models, the method can significantly improve destination prediction accuracy by dynamically updating. In addition, the estimation model is developed employing the Markov model, the structure of model can use for a short training period. Meanwhile, the research can apply to crowded place analysis or in trip distribution prediction, which shows a broad application in transportation planning and management.