Uncertainty-Aware Traffic Prediction using Attention-based Deep Hybrid Network with Bayesian Inference

Abstract

Traffic congestion has an adverse impact on the economy and quality of life and thus accurate traffic flow forecasting is critical for reducing congestion and enhancing transportation management. Recently, hybrid deep-learning approaches show promising contributions in prediction by handling various dynamic traffic features. Existing methods, however, frequently neglect the uncertainty associated with traffic estimates, resulting in inefficient decision-making and planning. To overcome these issues, this research presents an attention-based deep hybrid network with Bayesian inference. The suggested approach assesses the uncertainty associated with traffic projections and gives probabilistic estimates by applying Bayesian inference. The attention mechanism improves the ability of the model to detect unexpected situations that disrupt traffic flow. The proposed method is tested using real-world traffic data from Dhaka city, and the findings show that it outperforms than other cutting-edge approaches when used with real-world traffic statistics.

Long-Term Traffic Prediction Based on Stacked GCN Model

“…Table 1 demonstrates that an increase in the number of segments leads to reduced error. Twentyfour segments give less error than others (2,3,4,6,8,12). In twenty-four segments, each segment consists of one-hour timestamps.…”

“…This filter, applied to the nodes within the graph, gathers spatial characteristics from the first-order neighborhood of each node. The GCN model is constructed by stacking multiple convolutional layers, allowing it to capture increasingly complex spatial relationships among the nodes, as in (2).…”

“…In most megacities, traffic congestion is a significant issue that hinders residents' daily lives and the nation's economic progress [2]. The significant causes of traffic congestion include rising population, urbanization, poor traffic management, and inadequate transportation infrastructure [3].…”

“…Recently, this field has garnered increasing interest due to the rapid advancements and adoption of Intelligent Transportation Systems (ITS). Traffic flow prediction plays a fundamental role within the framework of ITS, serving as a pivotal component, plays a critical role in traffic management and planning, and aims to provide better transport management by avoiding congestion.In most megacities, traffic congestion is a significant issue that hinders residents' daily lives and the nation's economic progress [2]. The significant causes of traffic congestion include rising population, urbanization, poor traffic management, and inadequate transportation infrastructure [3].…”