Skip to main content

Table 3 Methodlogy and features, for mode detection

From: Transport behavior-mining from smartphones: a review

References Method Main features AGPS INS GIS
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