TY - JOUR
T1 - An Adaptive Learning-Based Approach for Vehicle Mobility Prediction
AU - Irio, Luis
AU - Ip, Andre
AU - Oliveira, Rodolfo
AU - Luis, Miguel
N1 - info:eu-repo/grantAgreement/FCT/9471 - RIDTI/151901/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/157671/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/157970/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/157970/PT#
POCI-01-0145-FEDER-030433
LISBOA-01-0145-FEDER-0307095
PY - 2021
Y1 - 2021
N2 - This work presents an innovative methodology to predict the future trajectories of vehicles when its current and previous locations are known. We propose an algorithm to adapt the vehicles trajectories' data based on consecutive GPS locations and to construct a statistical inference module that can be used online for mobility prediction. The inference module is based on a hidden Markov model (HMM), where each trajectory is modeled as a subset of consecutive locations. The prediction stage uses the statistical information inferred so far and is based on the Viterbi algorithm, which identifies the subset of consecutive locations (hidden information) with the maximum likelihood when a prior subset of locations are known (observations). By analyzing the disadvantages of using the Viterbi algorithm (TDVIT) when the number of hidden states increases, we propose an enhanced algorithm (OPTVIT), which decreases the prediction computation time. Offline analysis of vehicle mobility is conducted through the evaluation of a dataset containing real traces of 442 taxis running in the city of Porto, Portugal, during a full year. Experimental results obtained with the dataset show that the prediction process is improved when more information about prior vehicle mobility is available. Moreover, the computation time of the prediction process is significantly improved when OPTVIT is adopted and approximately 90% of prediction performance can be achieved, showing the effectiveness of the proposed method for vehicle trajectory prediction.
AB - This work presents an innovative methodology to predict the future trajectories of vehicles when its current and previous locations are known. We propose an algorithm to adapt the vehicles trajectories' data based on consecutive GPS locations and to construct a statistical inference module that can be used online for mobility prediction. The inference module is based on a hidden Markov model (HMM), where each trajectory is modeled as a subset of consecutive locations. The prediction stage uses the statistical information inferred so far and is based on the Viterbi algorithm, which identifies the subset of consecutive locations (hidden information) with the maximum likelihood when a prior subset of locations are known (observations). By analyzing the disadvantages of using the Viterbi algorithm (TDVIT) when the number of hidden states increases, we propose an enhanced algorithm (OPTVIT), which decreases the prediction computation time. Offline analysis of vehicle mobility is conducted through the evaluation of a dataset containing real traces of 442 taxis running in the city of Porto, Portugal, during a full year. Experimental results obtained with the dataset show that the prediction process is improved when more information about prior vehicle mobility is available. Moreover, the computation time of the prediction process is significantly improved when OPTVIT is adopted and approximately 90% of prediction performance can be achieved, showing the effectiveness of the proposed method for vehicle trajectory prediction.
KW - estimation and modeling
KW - hidden Markov model
KW - machine learning
KW - Trajectory prediction
UR - http://www.scopus.com/inward/record.url?scp=85099728748&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3052071
DO - 10.1109/ACCESS.2021.3052071
M3 - Article
AN - SCOPUS:85099728748
VL - 9
SP - 13671
EP - 13682
JO - IEEE Access
JF - IEEE Access
M1 - 9326302
ER -