Activity Imputation of Shared e-Bikes Travels in Urban Areas

AbstractIn 2017, about 900 thousands motorbikes were registered in Europe. These types of vehicles are often selected as the only alternative when the congestion in urban areas is high, thus consistently contributing to environmental emissions. This work proposes a data-driven approach to analyse trip purposes of shared electric bikes users in urban areas. Knowing how e-bikes are used in terms of trip duration and purpose is important to integrate them in the current transportation system. The data set consists of GPS traces collected during one year and three months representing 6,705 trips performed by 91 users of the e-bike sharing service located in three South European cities (Malaga, Rome and Bari). The proposed methodology consists of computing a set of features related to the temporal (time of the day, day of the week), meteorological (e.g. weather, season) and topological (the percentage of km traveled on roads with cycleways, speed on different types of roads, proximity of arrival to the nearest Point of Interest) characteristics of the trip. Based on the identified features, logistic regression and random forest classifiers are trained to predict the purpose of the trip. The random forest performs better with an average accuracy, over the 10 random splits of the train and test set, of 82%. The overall accuracy decreases to 67% when training and test sets are split at the level of users and not at the level of trips. Finally, the travel activities are predicted for the entire data set and the features are analysed to provide a description of the behaviour of shared e-bike users.KeywordsTrip imputationTravel activity behavioure-bikesActivity detectionGPS tracesMachine learningRandom forestMultinomial logistic regressionSafety