Abstract
Prediction of the probable future pick-ups is one of the most beneficial and challenging tasks for taxi drivers. Efficient prediction of the same requires proper study of the past history. In this paper we have considered the past history of the New York Yellow Taxi Data Set to predict the number of pick-ups. Prediction of the passenger demand for cab driver is made based on the criteria- area of region, travel time, distance between each region and trip fare etc. By taking all these criteria into consideration the passengers demand is predicted which is expected to help build strong advanced traffic management system (ATMS) and intelligent traffic system (ITS) and also solve other challenges related to traffic. In all six modeling techniques have been taken into consideration. The modeling techniques used being Simple Moving Average, Weighted Moving Average, Exponential Moving Average, Linear Regression, Random Forest and XGBoost Regressor. Appropriate weights are assigned to the predictions from these models, depending on the accuracy of their prediction. A combined decision of the prediction is thereafter given.
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Rajak, S., Baruah, U. (2020). An Ensemble Model for Predicting Passenger Demand Using Taxi Data Set. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1241. Springer, Singapore. https://doi.org/10.1007/978-981-15-6318-8_28
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DOI: https://doi.org/10.1007/978-981-15-6318-8_28
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