A Prediction of Travel Time by Machine Learning Approaches with Mobile Probe Data in Bangkok
Keywords:
prediction, travel time, machine learning, mobile probe dataAbstract
Last mile delivery is one of main transportation processes that accounts for more than 75% of total supply chain cost; however, the efficiency of such a process is relatively low, due largely to uncertainty in travel times across the day. In order to improve this process, various machine-learning based approaches are herein investigated for the development of more accurate travel time prediction models, using mobile probe data, collected by ITic, as a case study. For ease of implementation, we have preselected the information of taxis that currently provide services within the area of Bangkok (about 4.8 million records) for the construction of travel time prediction models. K-fold cross validation is also adopted to help reduce overfitting issues. Our results indicate that XGBoost is the most effective algorithm that provides the least RMSE (166.3069), while spending only 14.51 seconds in the model construction phase. Nonetheless, LightGBM and CatBoost seem to have good potentials for further studies as they provide relatively low RMSEs and computational times, when compared to other machine-learning approaches.
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