linayaseen/Seoul-Bike-Trip-Duration-Prediction

Trip duration is the most fundamental measure in all modes of transportation. Hence, it is crucial to predict the trip-time precisely for the advancement of Intelligent Transport Systems (ITS) and traveller information systems. In order to predict the trip duration, data mining techniques are employed in this paper to predict the trip duration of rental bikes in Seoul Bike sharing system. The prediction is carried out with the combination of Seoul Bike data and weather data. The Data used include trip duration, trip distance, pickup-drop-off latitude and longitude, temperature, precipitation, wind speed, humidity, solar radiation, snowfall, ground temperature and 1-hour average dust concentration. Four performance metrics Root mean squared error, Coefficient of Variance, Mean Absolute Error and Median Absolute Error can be used to determine the efficiency of the models

GPL-3.0