Reconciling spatiotemporal conjunction with digital twin for sequential travel time prediction and intelligent routing

A traffic digital twin explicitly communicates the primary domain of the digital twin for traffic management and analysis in understanding and simulating traffic patterns, optimizing traffic flow, and addressing related challenges. It characterizes a virtual, computerized representation of the Intelligent Transportation System (ITS), where a digital twin mimics the real-world ITS by integrating real-time data, analysis and simulation to replicate and simulate the behavior, performance and dynamics of the transport system. This study proposes a novel deep learning algorithm, called the Bidirectional Anisometric Gated Recursive Unit (BDAGRU), which is designed for a digital twin to dynamically process current traffic information to predict near-future travel times and support route selection. After formulating the computational procedure using the stochastic gradient descent algorithm, we successfully perform several near-future sequence predictions (ranging from 15 to 150 min) with extensive multimodal (numerical and textual) data, especially under congested traffic conditions. We then determine the most efficient vehicle route by minimizing travel time under uncertainty, using a digital twin enhanced by the BDAGRU-driven deep learning method. Empirical analysis of extensive traffic data shows that our proposed model achieves two notable results: (1) significant improvement in the accuracy of travel time prediction over different time intervals compared to several traditional deep learning methods, and (2) competent determination of the best route with the shortest travel time, especially in scenarios with future uncertainties.