Distributed Acoustic Sensing (DAS) offers an innovative solution for intelligent transportation systems, leveraging its benefits of all-weather monitoring, extensive coverage, and low maintenance costs. However, most existing studies use dark fiber as sensing arrays, which results in critical issues such as insufficient resolution, poor time-domain continuity, and a low signal-to-noise ratio in the vehicle signals collected by DAS systems, thereby limiting the practical application of this technology in traffic monitoring. To address these technical bottlenecks, this study proposes a novel, rapidly deployable intelligent traffic monitoring system based on DAS. Firstly, the system utilizes the self-developed high spatial resolution interrogator, and by optimizing fiber cable selection and introducing the Variational Mode Decomposition (VMD) method, the adverse effects of the fiber stress relaxation on signal demodulation are effectively suppressed from both the fiber structure and digital signal processing perspectives. Secondly, based on this system, high-fidelity vehicle signals were successfully collected, achieving the first direct and accurate measurement of wheel count and wheelbase under the condition of optical fiber deployment parallel to the road. Lastly, the system also successfully captured characteristic signals of pedestrians in different motion states, with rich detailed information. This study reveals the significant potential of DAS technology in heterogeneous traffic features recognition and provides high-quality data support for the development of high-precision DAS traffic monitoring technology.