Bridges form a salient part of critical infrastructure networks that are faced with the adverse effects of aging. At the same time, the growing need for mobility has created new demands for higher traveling speeds and increased imposed loads, which introduce additional requirements on such aged structures. This motivates monitoring of the condition of bridges utilizing Structural Health Monitoring (SHM) schemes, which aim at identifying changes in the characteristics of the response of the respective structures. Such changes may signify the presence of damage or deterioration; thus, SHM is imperative for deciding on the remaining life of bridges and accordingly scheduling maintenance procedures. Focusing on railway bridges, SHM typically relies on stationary sensors mounted on the bridge system, with direct assessment of the collected data. Although reliable, such an approach hinders the comprehensive inspection of multiple bridges of a railway network, while the short life span of sensors poses limitations to the continuous supply of data from the structure. As an alternative, vibration-based mobile sensing that relies on traversing trains has the potential to provide data from multiple railway bridges based solely on a few sensor networks installed on the trains. At the same time, when running the network at frequent intervals (e.g., in the case of in-service trains), the sensor-equipped trains can also provide continuous data that give insight into the deterioration of bridges over time. To this end, this work proposes a model-based methodology to extract modal parameters of bridges based on acceleration data collected by traversing trains. The proposed approach relies on Kalman filtering for the estimation of the train’s state and input and a subspace identification method for the identification of the frequencies and modes of the bridge. Long-term monitoring of bridge frequencies and modes can contribute to the timely restoration in case of damage and, thus, ensure the safety and reliability of rail transportation.