TY - GEN
T1 - Vehicle Trajectory Prediction based on LSTM Recurrent Neural Networks
AU - Ip, Andre
AU - Irio, Luis
AU - Oliveira, Rodolfo
N1 - Funding Information:
This work was funded by Fundação para a Ciência e Tecnologia, under the projects InfoCent-IoT (PTDC/EEI-TEL/30433/2017), CoSHARE (PTDC/EEI-TEL/30709/2017), and Grant UIDB/50008/2020.
PY - 2021/4
Y1 - 2021/4
N2 - This work presents an effective tool to predict the future trajectories of vehicles when its current and previous locations are known. We propose a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) prediction scheme due to its adequacy to learn from sequential data. To fully learn the vehicles' mobility patterns, during the training process we use a dataset that contains real traces of 442 taxis running in the city of Porto, Portugal, during a full year. From experimental results, we observe that the prediction process is improved when more information about prior vehicle mobility is available. Moreover, the computation time is evaluated for a distinct number of prior locations considered in the prediction process. The results exhibit a prediction performance higher than 89%, showing the effectiveness of the proposed LSTM network.
AB - This work presents an effective tool to predict the future trajectories of vehicles when its current and previous locations are known. We propose a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) prediction scheme due to its adequacy to learn from sequential data. To fully learn the vehicles' mobility patterns, during the training process we use a dataset that contains real traces of 442 taxis running in the city of Porto, Portugal, during a full year. From experimental results, we observe that the prediction process is improved when more information about prior vehicle mobility is available. Moreover, the computation time is evaluated for a distinct number of prior locations considered in the prediction process. The results exhibit a prediction performance higher than 89%, showing the effectiveness of the proposed LSTM network.
KW - Deep Learning
KW - Long Short-Term Memory (LSTM) Network
KW - Recurrent Neural Networks (RNNs)
KW - Trajectory Prediction
KW - Transportation Data Analytics
UR - http://www.scopus.com/inward/record.url?scp=85112403640&partnerID=8YFLogxK
U2 - 10.1109/VTC2021-Spring51267.2021.9449038
DO - 10.1109/VTC2021-Spring51267.2021.9449038
M3 - Conference contribution
AN - SCOPUS:85112403640
T3 - IEEE Vehicular Technology Conference
BT - 2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 93rd IEEE Vehicular Technology Conference, VTC 2021-Spring
Y2 - 25 April 2021 through 28 April 2021
ER -
