Machine learning methods for surge rate prediction: a case study of Yassir
DOI:
https://doi.org/10.64740/ittum.1.1.4Keywords:
Machine Learning, Surge Rate Prediction, Surge Price, Classification, Regression, Random Forest, Light GBM, XGBoostAbstract
Transportation Network Companies (TNCs) face two extreme situations, namely, high demand and low demand. In high demand, TNCs use surge multiplier or surge rate to balance the high demand of riders with available drivers. Willingness of drivers, willingness of riders to pay more and appropriate surge rate play a crucial role in maximizing profits of TNCs. Otherwise, a considerable number of trips can be discarded either by drivers or riders. This paper explains an application of a combined classification and regression model for surge rate prediction. In this paper, twenty-six different machine learning (ML) algorithms are considered for classification and twenty-nine ML algorithms are considered for regression. A total of 55 ML algorithms is considered for surge rate prediction. This paper shows that estimated distance, trip price, acceptance date and time of the trip, finishing time of the trip, starting time of the trip, search radius, base price, wind velocity, humidity, wind pressure, temperature etc. determine whether surge rate or surge multiplier will be applied or not. The price per minute applied for the current trip or minute price, base price, cost of the trip after inflation or deflation (i.e. trip price), the applied radius search for the trip or search radius, humidity, acceptance date of the trip with date and time, barometric pressure, wind velocity, minimum price of the trip, the price per km etc., on the other hands, influenced surge rate A case study has been discussed to implement the proposed algorithm
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