A Comparative Evaluation of Probabilistic and Deep Learning Approaches for Vehicular Trajectory Prediction

Abstract

This work compares two innovative methodologies to predict the future locations of moving vehicles when their current and previous locations are known. The two methodologies are based on: (a) a Bayesian network model used to infer the statistics of prior vehicles' trajectory data that is further adopted in the estimation process; (b) a deep learning approach based on recurrent neural networks (RNNs), more specifically Long Short-term Memory (LSTM) networks. Both methodologies are evaluated with GPS traces of multiple taxis servicing Porto city in Portugal. After a characterization of the dataset, we present experimental results obtained with both prediction methodologies. The results indicate that the prediction accuracy is improved in both methods as more information about prior vehicle mobility is available. The Bayesian network-based method is advantageous because the statistical inference can be updated in real-time as more trajectory data is known. On the contrary, the RNN-based method requires a time-consuming learning task every time new data is added to the inference dataset. However, the RNN achieves a higher prediction accuracy performance (3% to 5% higher) than the Bayesian method. Additionally, we show that the computational cost to predict the next position a vehicle will move to can be substantially reduced when the Bayesian network is adopted, a scenario where the RNN method requires more computational time. But when the quantity of prior data used in the prediction increases, the computational time required by the RNN-based method can be two orders of magnitude lower than the Bayesian network-based approach, showing that the RNN method is advantageous in both accuracy and computational time. Finally, we highlight that both methods achieve a next position successful prediction rate higher than 90%, confirming the applicability and validity of the proposed methods.

Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction

“…Adege et al [20] put forward a hybrid approach of principal component analysis and GRU for mobility predictions in a wireless urban area. Irio et al [21] compared RNNbased future location prediction with statistical inferencebased methods, demonstrating that the RNN approach is competitive in both accuracy and computational time. More recently, Ip et al [22] adopted LSTM to predict the future locations of vehicles with several different prior locations information.…”

Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction

“…al. [21] compared RNN-based future location prediction with statistical inference-based methods, demonstrating that the RNN approach is competitive in both accuracy and computational time. More recently, Ip et al [22] adopted LSTM to predict the future locations of vehicles with several different prior locations information.…”