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 and traveller information systems. To predict the trip duration, data mining techniques are employed in this study 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 and dropoff latitude and longitude, temperature, precipitation, wind speed, humidity, solar radiation, snowfall, ground temperature and 1‐hour average dust concentration. Feature engineering is done to extract additional features from the data. Four statistical models are used to predict the trip duration. (a) Linear regression, (b) Gradient boosting machines, (c) k nearest neighbour and (d) Random Forest (RF). Four performance metrics root mean squared error, coefficient of variance, mean absolute error and median absolute error is used to determine the efficiency of the models. In comparison with the other models, the best model RF can explain the variance of 93% in the testing set and 98% (R 2) in the training set. The outcome proves that RF is effective to be employed for the prediction of trip duration.
Seoul bike trip duration prediction using data mining techniques
IET Intelligent Transport Systems ; 14 , 11 ; 1465-1474
2020-11-01
10 pages
Article (Journal)
Electronic Resource
English
data mining , feature extraction , mean square error methods , regression analysis , traffic information systems , intelligent transportation systems , random forests , nearest neighbour methods , Seoul bike trip duration prediction , data mining techniques , trip distance , Seoul bike data , Seoul bike sharing system , intelligent transport systems , traveller information systems , trip‐time prediction , rental bikes , feature engineering , statistical models , linear regression , gradient boosting machines , k nearest neighbour , Random Forest , root mean squared error , coefficient of variance , mean absolute error , median absolute error
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