Transport Mode Detection (TDM) algorithms in principle are developed for fine-grained data which is either high frequent accurate GPS data with/or further optional data such as accelerometer from mobile phones. The main drawback of using high frequent GPS data is the battery issue which makes it very expensive experiment to be employed for large scale data. Besides, GPS can not cover underground trajectories and some additional resource is required for such multi-modal trajectories. In this work we investigate the TDM algorithms using a combination of fine-grained (GPS) and coarse-grained (GSM) data with lower frequency compared to existing studies. We first provide a comprehensive overview of transport mode detection for such data by exploring both segment based and sequence-based machine learning approaches and then we use the collected heterogeneous mobility dataset to compare different mode detection algorithms. With the obtained results, we show that TDM algorithms are still effective approach for noisy and sparse heterogeneous data. The obtained decent performance provides the opportunity of extracting precious data from a large population of users in an inexpensive approach.