GPS data-based mobility mode inference model using long-term recurrent convolutional networks

Abstract

Recently, identifying and estimating distributions of mobility modes have become essential tasks for establishing traffic management strategies. The primary purpose of this study is to develop a mobility mode inference model able to consider the sequential behaviors of choosing a mobility mode by using long-term recurrent convolutional networks (LRCNs). Only GPS profile data are used for the mobility mode classification. The modes are categorized into walk, bike, motorcycle, bus, driving, train, and Segway. The proposed LRCN architecture applies the sequencing mode transition concept, a novel concept in mobility mode inference research. We conducted a data preprocessing procedure to normalize the input data size, capture the motion behaviors from the GPS points, and refine the data. We then identified an optimal convolutional neural network (CNN) model by considering the number of layers, layer-order pattern, and number of filters. The CNN model was established by applying an ensemble CNN concept to a single optimal CNN model. Furthermore, we integrated the optimal CNN model and a long short-term memory (LSTM) network as an LRCN, so as to consider the sequential behaviors in choosing the mobility mode over time. Consequently, we established an optimal LRCN–bi model with the highest performance among the existing LRCN architectures. By comparing the confusion matrices of each of the two best models in the CNN and LRCN approaches, we confirmed that considering the sequential behaviors in choosing the mobility mode enhances the model’s performance in inferring the mobility mode. Furtherm ore, we confirmed that the LRCN approach outperforms approaches from previous studies.