BDTM: A High-Performance Intelligent Transportation Information Transfer Model Based on Dynamic Adaptive Optimization Strategy

In recent years, collaborative intelligent transportation systems (C-ITS) have been developing rapidly, and the efficient real-time processing of massive amounts of C-ITS information has become a new challenge. An information transmission model based on message queuing telemetry transport (MQTT) bridging Kafka is a feasible solution; however, it faces significant performance bottlenecks due to fluctuations in upstream traffic. To address this issue, we propose the BDTM, a high-performance information transmission model for intelligent transportation systems based on a dynamic adaptive optimization strategy. First, we use the MQTT protocol to bridge C-ITS messages to the Kafka message broker. This bridging mechanism not only ensures the reliability of data transmission but also enables the system to scale with growing demand, achieving fault tolerance and horizontal scalability. Second, we implement a dynamic adaptive resource adjustment mechanism for Kafka to minimize communication latency and improve system throughput. Specifically, we employ the autoregressive integrated moving average (ARIMA) model to monitor and predict real-time Kafka upstream traffic. We dynamically adjust Kafka's system parameter configuration based on the real-time prediction data of Kafka upstream traffic. In addition, to ensure the reasonableness of the selected parameters, we use the weighted latin hypercube sampling (LHS) algorithm to select a /set of configuration parameters that provides more comprehensive coverage of the high-dimensional parameter space. Experiments show that BDTM achieves a communication throughput increase of more than 20% compared with the original model, while reducing communication latency by more than 30%. Therefore, BDTM offers significant advantages in overall performance.