Table 3 Methodlogy and features, for mode detection

Assemi et al. [11]

Nested logit model, muiltinomial logistic regression, multiple discriminant analysis

Skewness of speed distribution, share of travel time with speed (m/s) \(\in [2, 8)\), share of travel time with speed (m/s) \(\in [8, 15)\), maximum speed, 95% percentile acceleration, maximum acceleration, acceleration variance, direct distance \(origin \rightarrow destination\), travelled distance \(origin \rightarrow destination\)

Yes

No

No

Bohte and Maat [21]

Rule-based

Distance \(GPS \rightarrow \,{\text{Points-of-interest}}\), Distance \(GPS \rightarrow LandUse\)

Yes

No

Yes

Byon and Liang [22]

Neural network

Speed, acceleration, magnetic field, satellites number

GPS

Accelerometer magnetometer

No

Dabiri and Heaslip [31]

Convolutional neural network, random forest, key nearest neighbor, support vector machines, multi layer perceptron

Speed, acceleration, jerk, bearing rate

Yes

No

No

Dabiri et al. [32]

SEmi-Supervised Convolutional Autoencoder

GPS points: relative distance, speed

Yes

No

No

Jahangiri and Rakha [55]

Random forest, bagging model, support vector machines, key nearest neighbor, Max-dependency Min-redundancy

Acceleration spectral entropy, acceleration range, Max angular velocity, average absolute acceleration, average angular velocity

Yes

Accelerometer, gyro-scope, rotation vector

No

Jiang et al. [57]

Recurrent neural network, Hampel filter

Speed, average speed, standard deviation speed

Yes

No

No

Mäenpää et al. [72]

Bayesian classier, neural network, random forest, auto encoder

Maximum acceleration, maximum speed, minimum acceleration, minimum speed, average acceleration, average speed, acceleration variance, speed variance, speed skewness, speed kurtosis, acceleration skewness, acceleration kurtosis

Yes

No

No

Martin et al. [75]

Random forest, key nearest neighbor, principal component analysis, recursive feature elimination

Average change in acceleration (\(\Delta T = 120\) s), 80% percentile speed (\(\Delta T = 120\) s), variance change in acceleration (\(\Delta T = 120\) s), maximum speed (\(\Delta T = 120\) s), average speed (\(\Delta T = 120\) s), average change in speed (\(\Delta T = 120\) s)

Yes

Accelerometer

No

Rasmussen et al. [90]

Fuzzy logic

95% percentile acceleration, 95% percentile speed, median speed, network segment

GPS

No

Yes

Semanjski et al. [96]

Support vector machines

Distance from (DF) motorway, DF railway, DF bicycle lane, DF bus stop, DF railways station, DF car parking, DF bicycle parking, DF bus line

Yes

No

Yes

Thomas et al. [104]

Bayesian classier

Personal trip history, speed, altitude, longitude, latitude, public transport time-table

Yes

Accelerometer

Yes

Xiao et al. [119]

Bayesian network

Average speed, 95% percentile speed, average absolute acceleration, travel distance, average heading change, Low-speed-rate (as the ratio of points with speed < threshold)

Yes

No

No

Yazdizadeh et al. [123]

Counvolutional neural network augmented with ensemble method, with random forest as meta learner

GPS points: relative distance, speed

Yes

No

No

Yazdizadeh et al. [122]

Random forest

Measures between origin-destination: cumulative and direct distance (m), travel time (Min.), average and 85th percentile speed (km/h), maximum, minimum difference between Min. and Max. acceleration (\({\mathrm{km/h}}^2\)), minimum and maximum slope; Max time interval (min) and Max distance (m) between each consecutive pair of GPS point; time of day and time of week; age, gender, occupation; average value of residential buildings around each individual’s home (in 250 m radius); direct distance between the origin and nearest public transit stop; direct distance between the destination and nearest public transit stop; average value of residential buildings around each individual’s home (in 250 m radius)

Yes

No

Yes

Yazdizadeh et al. [124]

Semi-supervised Generative Adversarial Networks

GPS points: relative distance, speed

Yes

No

No

Zhou et al. [132]

Random forest with 3 layers

Speed, \(Acceleration-Gravity\), fast fourier transform (frequency domain), energy of the signals, sum of spectral coefficients

Yes

Accelerometer

No

Zhou et al. [131]

Random forest

85% percentile speed, average speed, median speed, medium velocity rate, high velocity rate, low velocity rate, travel distance

Yes

No

Yes

Zhu et al. [134]

Auto encoder, deep neural network

Average speed, travel distance, average acceleration, head direction change, bus stop closeness, subway line closeness

Yes

No

Yes