A Multifeatures Spatial-Temporal-Based Neural Network Model for Truck Flow Prediction

Abstract

The majority of studies on road traffic flow prediction have focused on the flow of passenger cars or the flow of traffic as a whole, which ignore the significant impact of trucks with different sizes and operational characteristics on traffic flow efficiency. Therefore, in this paper, we focus on truck traffic flow and propose a Multifeatures Spatial-Temporal-Based Neural Network model (M-BiCNNGRU) to improve its prediction. The proposed model not only comprises conventional temporal characteristics and spatial relationships but also includes a range of multifeatures. These multifeatures include policy limit, optimal time delay, road resistance, and traffic congestion state. The impacts of upstream and downstream road sections are considered on the spatial relationship by using a Convolutional Neural Network (CNN). A Bidirectional Gated Recurrent Unit (Bi-GRU) is employed to account for the temporal characteristics. To evaluate the proposed model, traffic flow data were collected from a major expressway in Beijing and the results were compared with those derived from existing models. The results show that the prediction accuracy of the BiCNNGRU model, with spatial-temporal characteristics, and the M-BiGRU model, with multifeatures and temporal, is, respectively, 4.13% and 2.15% greater than that of the Bi-GRU model, with temporal characteristics. The prediction accuracy of the proposed M-BiCNNGRU model is 92.86%, which is 7.12% greater than that of the Bi-GRU model and 13.83% greater than that of the Support Vector Regression (SVR) model. In general, therefore, the proposed M-BiCNNGRU model, which combines multifeatures, temporal characteristics, and spatial relationships, can significantly improve accuracy in predicting truck traffic flow.

Soft computing in business: exploring current research and outlining future research directions

“…Topic 14- “Logistics System Optimization” deals with the research exploiting soft computing for traffic optimization (Zhang et al ., 2021), efficient routing of vehicles (Haixiang et al ., 2021; Wang et al. , 2021b; Xu and Lyu, 2021), traffic flow prediction (Wang et al ., 2021), path optimization in urban transportation networks (Liu et al ., 2021a, b) and sustainable reverse logistics network design (Hashemi, 2021), urban transportation network management (Chen et al ., 2022e).…”

Traffic flow prediction using bi-directional gated recurrent unit method

“…Bi-GRU model is a variant of RNN, which have capacities to memory long-term dependencies (e.g. 1 day traffic flow information at 1 hour interval) of time series data (Wang, Shao, et al, 2021 ). Short-term traffic flow prediction belongs to time series prediction problem, which indicates that Bi-GRU can be applied to short-term traffic flow prediction.…”