Deep Learning Framework for Freeway Speed Prediction in Adverse Weather

Abstract

The introduction of deep learning (DL) models and data analysis may significantly elevate the performance of traffic speed prediction. Adverse weather causes mobility and safety concerns because of varying traffic speeds with poor visibility and road conditions. Most previous modeling approaches have not considered the heterogeneity of temporal and spatial data, such as traffic and weather conditions. This paper presents a framework, consisting of two DL models, to predict traffic speed under normal conditions and during adverse weather, considering prevailing traffic speed, wind speed, traffic volume, road capacity, wind bearing, precipitation intensity, and visibility. To ensure the accuracy of speed prediction, different DL models were assessed. The results indicated that the proposed one-dimensional convolutional neural network model outperformed others in relation to the least root mean square error and the least mean absolute error. Considering real-time weather data feeds on a 15-min basis, a tool was also developed for displaying predicted traffic speeds on New Jersey freeways. Application of the proposed framework models for predicting spatio-temporal hot-spot congestion caused by adverse weather is discussed.

Predicting Bus Travel Time with Hybrid Incomplete Data – A Deep Learning Approach

“…Many deep learning methods, such as LSTM, Gated Recurrent Unit (GRU), Bi-directional Long Short Term Memory (Bi-LSTM), Bi-Gated Recurrent Unit (Bi-GRU) and Graph Convolution Network (GCN) are similar to the RNN structure, which has been widely applied to predict time sequence information. LSTM is an advanced RNN structure, which is robust for predicting bus travel time [19,20]. Liu et al [32] developed an LSTM model to predict bus travel time using GPS data and found that LSTM outperformed RNN.…”

“…Recurrent Neural Network (RNN) considers a sequence of data inputs and has been widely used in time sequence analysis. The LSTM is an advanced form of RNN, which is robust for predicting bus travel time [19,20]. However, LSTM is characterised by a set of hyperparameters, which shall be effectively determined by a sound algorithm to yield the best performance.…”

A Link Real-time Travel Time Estimation on Speed-time Field Traversing Incorporating LSTM Neural Network

“…The average speed [ ( )] TT t x x  and the travel time from the origin node to destination ( ; , ) TT t O D can be calculated by equation (14) and equation (15).…”

“…The travel time of each link was calculated according to equation (14), and the travel time from the origin node to the destination was calculated according to equation (15).…”

“…The LSTM neural networks model is widely used for traffic flow and speed prediction since it performs better than other neural network models in large-scale time series prediction [14] . Shabarek [15] and Ma [16] have achieved high-accuracy speed prediction by constructing LSTM neural networks model. In this work, an improved travel time estimation method based on LSTM model and node speed data was proposed.…”