An Adaptive Route Planning Method of Connected Vehicles for Improving the Transport Efficiency

In recent years, the route-planning problem has gained increased interest due to the development of intelligent transportation systems (ITSs) and increasing traffic congestion especially in urban areas. An independent route-planning strategy for each in-vehicle terminal improves its individual travel efficiency. However, individual optimal routes pursue the maximization of individual benefit and may contradict the global benefit, thereby reducing the overall transport efficiency of the road network. To improve traffic efficiency while considering the travel time of individual vehicles, we propose a new dynamic route-planning method by innovatively introducing a bidding mechanism in the connected vehicle scenario for the first time. First, a novel bidding-based dynamic route planning is proposed to formulate vehicle routing schemes for vehicles affected by congestion via the bidding process. Correspondingly, a bidding price incorporating individual and global travel times was designed to balance the travel benefits of both objectives. Then, in the bidding process, a new local search algorithm was designed to select the winning routing scheme set with the minimum bidding price. Finally, the proposed method was tested and validated through case studies of simulated and actual driving scenarios to demonstrate that the bidding mechanism would be conducive to improving the transport efficiency of road networks in large-scale traffic flow scenarios. This study positively contributes to the research and development of traffic management in ITSs.

TSN Switch Queue Length Prediction Based on an Improved LSTM Network

With the high-speed development of network technology, time-sensitive networks (TSNs) are experiencing a phase of significant traffic growth. At the same time, they have to ensure that highly critical time-sensitive information can be transmitted in a timely and accurate manner. In the future, TSNs will have to further improve network throughput to meet the increasing traffic demand based on the guaranteed transmission delay. Therefore, an efficient route scheduling scheme is necessary to achieve network load balance and improve network throughput. A time-sensitive software-defined network (TSSDN) can address the highly distributed industrial Internet network infrastructure, which cannot be accomplished by traditional industrial communication technologies, and it can achieve distributed intelligent dynamic route scheduling of the network through global network monitoring. The prerequisite for intelligent dynamic scheduling is that the queue length of future switches can be accurately predicted so that dynamic route planning for flow can be performed based on the prediction results. To address the queue length prediction problem, we propose a TSN switch queue length prediction model based on the TSSDN architecture. The prediction process has three steps: network topology dimension reduction, feature selection, and training prediction. The principal component analysis (PCA) algorithm is used to reduce the dimensionality of the network topology to eliminate unnecessary redundancy and overlap of relevant information. Feature selection requires comprehensive consideration of the influencing factors that affect the switch queue length, such as time and network topology. The training prediction is performed with the help of our enhanced long short-term memory (LSTM) network. The input-output structure of the network is changed based on the extracted features to improve the prediction accuracy, thus predicting the network congestion caused by bursty traffic. Finally, the results of the simulation demonstrate that our proposed TSN switch queue length prediction model based on the improved LSTM network algorithm doubles the prediction accuracy compared to the original model because it considers more influencing factors as features in the neural network for training and learning.