Table 1. Summary of travel time...

Table 1.

Summary of travel time prediction using machine learning approaches

Author (year)	Location	Roadway category	Data source	Method category	Data type	Prediction method
Wunderlich et al. (2000)	N/A	N/A	Simulated data from	Navie model	Travel time	Exponential filtering
Dion et al. (2004)	Virginia, USA	N/A	Simulated data from integration	Traffic theory-based model	Travel time	Delay models
Van Lint et al. (2002)	N/A	Freeway	Freeway operations simulation (FOSIM)	Non-parametric	Travel time, travel speed	State-space neural network
Wu et al. (2005)	Taiwan	Highway	Loop detector	Non-parametric	Travel speed	SVR
Schmitt and Jula (2007)	California, USA	Urban road	Loop detector	Navie model	Travel time	Switch model
Zou et al. (2008)	Maryland, USA	Highway	Roadside detector	Hybrid non-parametric	Travel time	Combined clustering neural networks
Li et al. (2009)	Atlanta, USA	N/A	Simulated data from VISSIM	Hybrid non-parametric	Travel time, travel speed	Combined boosting and neural network
Papageorgiou et al. (2010)	N/A	N/A	Simulated data from MATANET	Traffic theory-based model	Travel time	Macroscopic simulation
Hamner (2010)	N/A	N/A	GPS	Non-parametric	Travel speed	RF
Myung et al. (2011)	Korea	N/A	ATC system	Non-parametric	Travel time	KNN
Wisitpongphan (2012)	Bangkok, Thailand	Highway	GPS	Non-parametric	Travel time, GPS	BP neural network
Zheng et al. (2013)	Delft, The Netherlands	Urban road	GPS data	Non-parametric	Vehicle position, travel speed	State-space neural network
Yildirimoglu and Geroliminis’s (2013 )	California, USA	Freeway	Loop detector	Hybrid non-parametric	Travel time	Combined Gaussian mixture, PCA and clustering
Zhang and Haghani (2015)	Maryland, USA	Interstate highway	INRIX	Non-parametric	Travel time	Gradient boosting
Joao et al. (2015)	Porto, Portugal	Urban road	STCP system	Hybrid non-parametric	Travel time	Combined RF, projection pursuit regression and SVM
Duan et al. (2016)	England	Highway	Cameras, GPS and loop detectors	Non-parametric	Travel time	LSTM neural network
Li and Bai (2016)	Ningbo, China	N/A	N/A	Non-parametric	Truck trajectory, travel time, travel speed	Gradient boosting
Liu et al. (2017)	California, USA	Interstate highway	PeMS	Non-parametric	Travel time	LSTM neural network
Fan et al. (2018)	Taiwan	Highway	Electric toll	Non-parametric	Travel time, vehicle information	RF method
Yu et al. (2017)	Shenyang, China	bus route	AVL system	Non-parametric	Bus travel time	RF and KNN
Wang et al. (2018)	Beijing, China	Urban road	Floating ar data	Non-parametric	Taxi travel time, vehicle trajectory data	LSTM neural network
Wei et al. (2018)	China	Urban road	Vehicle passage records	Non-parametric	Travel time	LSTM neural network
Wang et al. (2018)	Beijing and Chengdu, China	Urban road	GPS	Non-parametric	Vehicle trajectory data	LSTM neural network
Gupta et al. (2018)	Porto, Portugal	Urban road	GPS	Non-parametric	Taxi travel speed	RF and gradient boosting
Moonam et al. (2019)	Madison, Wisconsin, USA	Freeway	Bluetooth detector	Non-parametric	Travel speed	KNN, KF
Kumar et al. (2019)	Chennai, India	Urban road	GPS	Non-parametric	Travel time	KNN
Cristóbal et al. (2019)	Gran Canaria, Spain	Urban road	Public transport network	Non-parametric	Travel time	K-medoid clustering technique
Kwak and Geroliminis (2020)	California, USA	Freeway	PeMS	Parametric	Travel time	Dynamic linear model
Fu et al. (2020)	Beijing, Suzhou, Shenyang, China	Urban road	Ride-hailing platform	Non-parametric	Travel time	Graph attention network
Chiabaut and Faitout (2021)	Lyon, French	Highway	Loop detector	Non-parametric	Travel time	PCA and Clustering
Wu et al. (2021)	Houston, USA	Urban	AWAM	Parametric	Travel time	Autoregressive with exogenous inputs (NARX) model and feed-forward neural network

Author (year)	Location	Roadway category	Data source	Method category	Data type	Prediction method
Wunderlich et al. (2000)	N/A	N/A	Simulated data from	Navie model	Travel time	Exponential filtering
Dion et al. (2004)	Virginia, USA	N/A	Simulated data from integration	Traffic theory-based model	Travel time	Delay models
Van Lint et al. (2002)	N/A	Freeway	Freeway operations simulation (FOSIM)	Non-parametric	Travel time, travel speed	State-space neural network
Wu et al. (2005)	Taiwan	Highway	Loop detector	Non-parametric	Travel speed	SVR
Schmitt and Jula (2007)	California, USA	Urban road	Loop detector	Navie model	Travel time	Switch model
Zou et al. (2008)	Maryland, USA	Highway	Roadside detector	Hybrid non-parametric	Travel time	Combined clustering neural networks
Li et al. (2009)	Atlanta, USA	N/A	Simulated data from VISSIM	Hybrid non-parametric	Travel time, travel speed	Combined boosting and neural network
Papageorgiou et al. (2010)	N/A	N/A	Simulated data from MATANET	Traffic theory-based model	Travel time	Macroscopic simulation
Hamner (2010)	N/A	N/A	GPS	Non-parametric	Travel speed	RF
Myung et al. (2011)	Korea	N/A	ATC system	Non-parametric	Travel time	KNN
Wisitpongphan (2012)	Bangkok, Thailand	Highway	GPS	Non-parametric	Travel time, GPS	BP neural network
Zheng et al. (2013)	Delft, The Netherlands	Urban road	GPS data	Non-parametric	Vehicle position, travel speed	State-space neural network
Yildirimoglu and Geroliminis’s (2013 )	California, USA	Freeway	Loop detector	Hybrid non-parametric	Travel time	Combined Gaussian mixture, PCA and clustering
Zhang and Haghani (2015)	Maryland, USA	Interstate highway	INRIX	Non-parametric	Travel time	Gradient boosting
Joao et al. (2015)	Porto, Portugal	Urban road	STCP system	Hybrid non-parametric	Travel time	Combined RF, projection pursuit regression and SVM
Duan et al. (2016)	England	Highway	Cameras, GPS and loop detectors	Non-parametric	Travel time	LSTM neural network
Li and Bai (2016)	Ningbo, China	N/A	N/A	Non-parametric	Truck trajectory, travel time, travel speed	Gradient boosting
Liu et al. (2017)	California, USA	Interstate highway	PeMS	Non-parametric	Travel time	LSTM neural network
Fan et al. (2018)	Taiwan	Highway	Electric toll	Non-parametric	Travel time, vehicle information	RF method
Yu et al. (2017)	Shenyang, China	bus route	AVL system	Non-parametric	Bus travel time	RF and KNN
Wang et al. (2018)	Beijing, China	Urban road	Floating ar data	Non-parametric	Taxi travel time, vehicle trajectory data	LSTM neural network
Wei et al. (2018)	China	Urban road	Vehicle passage records	Non-parametric	Travel time	LSTM neural network
Wang et al. (2018)	Beijing and Chengdu, China	Urban road	GPS	Non-parametric	Vehicle trajectory data	LSTM neural network
Gupta et al. (2018)	Porto, Portugal	Urban road	GPS	Non-parametric	Taxi travel speed	RF and gradient boosting
Moonam et al. (2019)	Madison, Wisconsin, USA	Freeway	Bluetooth detector	Non-parametric	Travel speed	KNN, KF
Kumar et al. (2019)	Chennai, India	Urban road	GPS	Non-parametric	Travel time	KNN
Cristóbal et al. (2019)	Gran Canaria, Spain	Urban road	Public transport network	Non-parametric	Travel time	K-medoid clustering technique
Kwak and Geroliminis (2020)	California, USA	Freeway	PeMS	Parametric	Travel time	Dynamic linear model
Fu et al. (2020)	Beijing, Suzhou, Shenyang, China	Urban road	Ride-hailing platform	Non-parametric	Travel time	Graph attention network
Chiabaut and Faitout (2021)	Lyon, French	Highway	Loop detector	Non-parametric	Travel time	PCA and Clustering
Wu et al. (2021)	Houston, USA	Urban	AWAM	Parametric	Travel time	Autoregressive with exogenous inputs (NARX) model and feed-forward neural network

Notes:

SVR = support vector regression; VISSIM = Verkehr In Städten - SIMulationsmodell (German for “Traffic in cities - simulation model”); PeMS = performance measurement system; GPS = global positioning system; ATC = air traffic control; KNN = k-nearest neighbours; AVL = automatic vehicle location; KF = K filter; AWAM = anonymous wireless address matching; NARX = nonlinear autoregressive exogenous model

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