In recent years, DEtection TRansformer (DETR) has achieved remarkable performance for traffic monitoring. However, due to the typically high computational complexity of transformers, DETR encounters performance bottlenecks in resource-constrained scenarios. To solve the above problems, Real-Time DEtection TRansformer (RT-DETR) was recently proposed. The efficient design of RT-DETR allows for real-time object detection without sacrificing accuracy. But the model still requires a large amount of computing resources, which may limit its deployment on resource constrained devices. To make the model more lightweight, we propose a Real-Time DEtection TRansformer with Bi-Level Routing Attention (RDETR-BRA). Specifically, we incorporated an efficient hybrid encoder based on bi-level routing attention, which efficiently processes multi-scale features by decoupling intra scale interactions and inter scale fusion. Experimental results show that the RDETR-BRA effectively improves the accuracy and precision of object detection in road scenarios. On the BDD100K and Udacity datasets, the average precision of RDETR-BRA is improved by 0.6 percentage points compared to RT-DETR and 18 percentage points compared to RetinaNet. Additionally, the parameter count is reduced from 42M (RT-DETR) to 34M.