# Convolutional Neural Network-Based Travel Mode Recognition Based on Multiple Smartphone Sensors
## 2 Related Works  
Numbers of studies have proposed methods to infer transportation modes based on multiple data sources. One popular data source is GPS data [19; 20; 21; 22; 23; 24; 25], which are real-time records of people's activities of travel [19; 20; 21], thus providing a crucial data source for travel mode detection. The GPS data-based methods are popular to classify multiple travel modes based on different moving features. [PERSON] et al. [22] proposed a framework to infer transportation mode from users' GPS trajectories. In their work, each trajectory was divided into segments by a change-point-based segmentation algorithm and features of each segment were extracted. Then, a traditional classification algorithm was deployed to train the transportation mode detection model. [PERSON] [23] intended to select the most significant features from three sets of potential features extracted from GPS trajectories. [PERSON] et al. [24] proposed a GPS-based transportation mode detection method using a moving window SVM classification. They implemented the proposed method by using a framework based on SVM classification. [PERSON] et al. [25] detected different travel modes based on GPS track data and Bayesian networks. These studies mainly rely on a single GPS data source, and it is difficult to effectively classify travel modes for areas where the signal is blocked. Especially, when people do not use GPS sensors (such as subway travel, walking), the above methods are difficult to accurately classify travel modes. Instead of single GPS-based methods, some researchers improved the travel mode classification performance by using multiple sources combination. [PERSON] et al. [26] proposed a GPS/GIS method for travel detection in New York City. [PERSON] et al. [27] developed and evaluated a Random Forest classifier combined with a rule-based method to detect travel modes. Besides, socioeconomic attributes were used to further improve the precision. [PERSON] and [PERSON] [28] utilized a Bayesian Belief Network to combine GPS and accelerometer data for travel mode detection. At the methodological level, deep learning-based methods currently have been proposed to integrate the hand-crafted features with high-level features [29; 30; 31] for transportation mode estimation. [PERSON] et al. [31] proposed a multi-feature network for sound classification. [PERSON] et al. [32] utilized a fully connected deep neural network (DNN) to automatically extract high-level features. Similarity, [PERSON] et al. [33] obtained the deep features by transforming point-level features to deep features with the aid of sparse autoencoder. [PERSON] and [PERSON] [1] proposed a transportation mode detection method based on GPS trajectories using a convolutional neural network that structures a raw GPS trajectory into a format. The key of these methods is to convert a raw GPS trajectory into a 2D image structure as the input of the DNN model. However, GPS-based methods show poor performance in a complex urban environment where a significant multipath effect exists, and the problems mentioned above are still difficult to solve [26]. With the development of mobile computation, various sensors carried in smart phones are becoming novel data sources for crowd behavior identifying and environment perception [34; 35; 36; 37]. Thus, smartphone-based methods have gradually emerged as a popular way to categorize different travel modes [11]. [PERSON] et al. [12] presented a convenient classification system that utilized a smartphone with a built-in GPS receiver and an accelerometer. The proposed system consisted of a decision tree followed by a first-order discrete Hidden Markov Model. [PERSON] et al. [2] proposed an accelerometer-based transportation mode detection using smartphones. Their paper described a novel gravity estimation technique for accelerometer measurements to distinguish information pertaining to movement behavior from other factors that affect the accelerometer signals.