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Table 6 Methodlogy and features, for purpose imputation

From: Transport behavior-mining from smartphones: a review

References Method Main features AGPS INS GIS
Bohte and Maat [21] Rule-based Distance \(GPS \rightarrow \,{\text {Points-of-interest}}\), Distance \(GPS \rightarrow LandUse\) GPS No Yes
Feng and Timmermans [40] Random forest Activity duration, activity start time, travel time to activity, distance \(GPS \rightarrow \,{\text {Points-of-interest}}\) GPS No Yes
Kim et al. [60] Bagging decision tree, random forest Activity probability, distance-based empirical probability, activity transition probability, activity duration Yes Accelerometer Yes
Montini et al. [76] Clustering, random forest start time, end time, GPS points density, age, education, income, mobility ownership, activity duration, walk percentage Yes Accelerometer Yes
Xiao et al. [120] Multi layer perceptron, particle swarm optimisation, multinomial logit, support vector machines, Bayesian network Age, gender, education, working hours, income, time of week, activity duration, time of day, transportation mode, distance \(GPS \rightarrow \,{\text {Points-of-interest}}\), distance \(GPS \rightarrow LandUse\) Yes No Yes
Yazdizadeh et al. [122] Random forest Features returned by Open Trip Plannera itinerary: GPS tracks average speed, time interval between the first and last GPS track of a trip, average distance between consecutive GPS point, attributes from, itinerary length, total transit time of each returned, total walking time of each itinerary, total waiting time of each itinerary, total travel time, number of transfers, walking distance, itinerary average speed attributes from GPS tracks, difference between GPS tracks length and itinerary length, overlapping percentage of itinerary and GPS tracks Yes No Yes
  1. aOpen Trip Planner (OTP) retrieved from web 01/01/2019,