Table 6 Methodlogy and features, for purpose imputation

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 Planner^a 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