A Multi-Scale Attributes Attention Model for Transport Mode Identification

Transport mode identification (TMI), which infers the travel modes of user trajectories, is essential to facilitate an understanding of urban mobility patterns and passengers’ choice behaviors with the goal of improving urban transportation systems. To achieve higher accuracy, existing TMI methods usually rely on mobility features obtained from densely sampled GPS trajectory points (e.g. 1 second per GPS point) or data measurements of additional inertial measurement unit (IMU) sensors (e.g. accelerometer, gyroscope, rotation vector). However, these lead to high energy consumption of the users’ mobile devices. In this paper, we propose a novel deep learning framework, Multi-Scale Attributes Attention (MSAA) model, to extract discriminating trajectory features from GPS data only, without the need to increase its sampling rate. The proposed model first partitions the trajectories into different scales and extract the latent representation of local attributes at each scale. The MSAA model relies on Convolutional Neural Network (CNN) to capture the spatial correlation of different trajectory segments, and utilizes attention mechanism to select the most suitable local attributes on the different trajectory scales that can effectively characterize the various transport modes. Since the learned latent local attributes are significantly different from the global features (e.g. average/min/max travel speeds which are measurable quantities), an ensemble model based on Neural Decision Forest (NDF) is employed to fuse the heterogeneous features consisting of both measurable quantities and non-measurable elements for determining the transport mode. Experiments on real-world datasets demonstrate the competitive performance of the proposed approach compared to several state-of-the-art baselines, with average improvements in accuracy ranging from 0.76% to 6.4%. In addition, the proposed multi-scale local attributes well complement the global features. Our results show that by incorporating the local attributes, the detection performance improved by 2.3% on average compared to using only global features.