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The importance of railway safety cannot be overemphasized; hence it requires reliable traffic monitoring systems. Widespread trackside telecommunication fiber-optic cables can be suitably deployed in the form of dense vibration sensors using Distributed Acoustic Sensing technology (DAS). Train-induced ground motion signals are recorded as continuous “footprints” in the DAS recordings. As the DAS system records huge datasets, it is thus imperative to develop optimized/stable algorithms which can be used for accurate tracking of train position, speed, and the number of trains traversing the position of the DAS system. In this study, we transform a 6-days continuous DAS data sensed by a 2-km cable into time-velocity domain using beamforming on phase-squeezed signals and automatically extract the position and velocity information from the time-beampower curve. The results are manually checked and the types of the trains are identified by counting the peaks of the signals. By reducing the array aperture and moving subarrays, the train speed-curve/motion track is obtained with acceptable computational performance. Therefore, the efficiency and robustness of our approach, to continuously collect data, can play a supplementary role with conventional periodic and time-discrete monitoring systems, for instance, magnetic beacons, in railway traffic monitoring. In addition, our method can also be used to automatically slice time windows containing train-induced signals for seismic interferometry.