In recent years, with the wide application of global positioning system (GPS), more and more electric bicycles are equipped with GPS sensors. A massive amount of trajectory data recorded by those sensors are of great value in many fields, such as users"" travel patterns analysis, decision support for urban planners and so on. However, the low-cost GPS sensors widely used on electric bicycles cannot provide high-precision positioning. Besides some characteristics of the electric bicycles’ track data make the matching process more complex and challenging: 1) there are a large number of stay points on electric bicycles’ trajectories; 2) the original track data generated by the electric bicycle have higher sampling frequency and shorter distance between adjacent track points; 3) some roads only open for electric bicycles, and the accuracy of matching is sensitive to the quality of the road network. To solve those problems mentioned above, we propose an adaptive and accurate road network map matching algorithm named KFTS-AMM, which consists of two main components: 1) the segmented Kalman Filtering based Trajectory Simplification (KFTS) algorithm and 2) segmented Hidden Markov Model based Adaptive Map Matching (AMM) algorithm. Based on the characteristics that Kalman Filtering algorithm can be used for optimal state estimation, the trajectory simplification algorithm KFTS can make the trajectory curve smoother and reduce the impact of abnormal points on the accuracy of map matching by fixing the trajectory points automatically in the process of trajectory simplification. Besides, we use the matching algorithm AMM to reduce the impact of invalid trajectory segments on the map matching accuracy. Moreover, we add stay points identification and merging step into the processing of track data, and the accuracy is further improved. The results of extensive experiments conducted on the real-world track dataset of electric bicycles in Zhengzhou city show that the proposed approach KFTS-AMM outperforms baselines in terms of accuracy and can speed up the matching process by using the simplified track data significantly.