The potential bicycle travel demand indicates the travel demand that could potentially be served by bicycles. Assessing the potential bicycle travel demand can help to optimize the allocation of the related infrastructure (e.g., bike parking areas and bike lanes) in cities. Mobile phone location data have the advantage of providing low-cost and large-scale sample sizes that contain rich human mobility information. The data can be used to estimate the potential bicycle travel demand. Based on the spatiotemporal characteristics of daily bicycle travel, we proposed a method for assessing the potential bicycle travel demand from large-scale mobile phone location data. Specifically, each individual instance of travel was taken as a sample for the analysis. First, we used the Stops and Moves of a Trajectory (SMoT) model to extract the movement trajectory segments of the users. Second, we identified a "tour" pattern for the trajectory segments, where the start location and the end location were the same. Then, the location that was at the furthest point from the start location was used to divide the movement trajectory segment into two segments. Finally, the movement trajectory segments that were characterized by short distances and those in which the "last mile" of the travels was served by the public transport system were extracted for further assessment of the potential bicycle travel demand. In this study, Shanghai was chosen as the example city. Through our proposed method, we assessed and analyzed the spatiotemporal characteristics of daily bicycle travel in Shanghai to determine the potential bicycle travel demand. From a spatial perspective, we found the following: (1) the potential bicycle travel demand in Shanghai was mainly concentrated in the downtown areas and commercial centers in the suburb areas; (2) the potential bicycle travel demand in the downtown areas and commercial central urban areas was stable, while the potential bicycle travel demand in the suburban areas tended to be variable; and (3) most of the “last mile” demands were located in the suburb areas, which showed that the characteristics of the “last mile” demands at different public transport stations varied. From a temporal perspective, several patterns could be observed during the morning and evening rush hours: (1) the potential bicycle travel demand in the central urban area continued to remain relatively high; (2) the potential bicycle travel demand in the suburbs in the Songjiang and Qingpu districts had relatively large differences; (3) the potential bicycle travel demand was concentrated in the direction of the central urban area from the noncentral urban areas in the morning, while the potential bicycle travel demand spread from the central urban areas to the noncentral urban areas in the evening; and (4) the potential bicycle travel demand of Shanghai showed a double-peak characteristic (at 11:00—12:00 and 16:00—17:00). The “last mile” type demand also had two peaks (at 7:00—9:00 and 17:00—18:00).