An attention-based recurrent learning model for short-term travel time prediction

Abstract

With the advent of Big Data technology and the Internet of Things, Intelligent Transportation Systems (ITS) have become inevitable for future transportation networks. Travel time prediction (TTP) is an essential part of ITS and plays a pivotal role in congestion avoidance and route planning. The novel data sources such as smartphones and in-vehicle navigation applications allow traffic conditions in smart cities to be analyzed and forecast more reliably than ever. Such a massive amount of geospatial data provides a rich source of information for TTP. Gated Recurrent Unit (GRU) has been successfully applied to traffic prediction problems due to its ability to handle long-term traffic sequences. However, the existing GRU does not consider the relationship between various historical travel time positions in the sequences for traffic prediction. We propose an attention-based GRU model for short-term travel time prediction to cope with this problem enabling GRU to learn the relevant context in historical travel time sequences and update the weights of hidden states accordingly. We evaluated the proposed model using FCD data from Beijing. To demonstrate the generalization of our proposed model, we performed a robustness analysis by adding noise obeying Gaussian distribution. The experimental results on test data indicated that our proposed model performed better than the existing deep learning time-series models in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2).

Hourly Long-Term Traffic Volume Prediction with Meteorological Information Using Graph Convolutional Networks

“…It is widely used in time-series data prediction or spatial dependence [17]. The GAN uses an attention mechanism in the GCN [7]. The attention mechanism focuses on the most related features from the input data.…”

“…Recent progress in neural network methodologies has opened new avenues for traffic volume prediction. These contemporary techniques, especially those driven by neural networks, have shown promising results in predicting traffic speeds across road networks [7]. Neural network models can incorporate diverse data types, including meteorological conditions, such as precipitation, temperature, and humidity, to improve predictions.…”

“…Integrating weather data into traffic volume prediction models could significantly enhance their precision and reliability. Recent works have used the multi-layer perception network (MLP) [8], long short-term memory (LSTM) [9], graph convolution network (GCN) [10,11], and graph attention network (GAN) [7] to predict traffic volumes. Despite the important role of and progress in neural network applications, the intensive study of long-term traffic volume prediction incorporating meteorological data remains limited.…”

Edge-based graph neural network for ranking critical road segments in a network

“…Recent advancements in computing and the availability of large data sets have resulted in powerful machine learning and deep learning methods. Such learning-based methods have been used in transportation networks for travel time prediction [ 50 ], traffic forecasting [ 51 ], and missing traffic data imputation [ 52 ]. Mendonça et al [ 53 ] proposed a simple neural network with graph embedding to estimate the approximate NBC.…”