# Application of Online Transportation Mode Recognition in Games
## 2 Related Work
### Transportation Mode Recognition  
In recent years, transportation mode recognition has been used for: the identification of peoples' physical activities [23]; user dynamic control of their optimal route [24]; to support intelligent transportation systems [25]. However, limited research has been conducted on transportation mode recognition to promote sustainability awareness. There are various approaches for transportation mode recognition or classification. Our review focuses on smartphone-based approaches, which is relevant for our research. Research conducted to distinguish motorised transportation as one class from all other modes of transportation has been mostly successful [26; 27]. However, it is challenging to distinguish the different types of motorised transports, and typically, this is addressed by using specific sensors targeting a specific type of transportation mode [28]. Activity recognition is a separate branch of targeted human-centred activities machine learning and, to date, has achieved more than 90% classification accuracy for common classes (sitting, lying down, walking, running) [29], or even higher rates for more classes if additional sensors are deployed [30].  
Accelerometer-only approaches have been largely successful in the classification of a constrained number of motorised vehicles. For example, 97% classification accuracy for three classes (car, train, and pedestrian) has been achieved using support vector machines [31], and 80% classification accuracy for six modes of transportation (walk, bus, train, metro, tram, and car) has been achieved using many features (i.e., 78 features) from the gathered data [32]. [PERSON] and [PERSON] (2016) propose a hybrid model (using random forest and discrete hidden Markov chain filters) that attains 94% accuracy [33]. They use both accelerometer and magnetometer data to identify eight classes (still, walk, run, bike, road, rail, plane, and other). [PERSON] and [PERSON] (2015) employ a range of supervised learning approaches to classify five transportation modes (car, bicycle, bus, walking, and running) [34]. Methods tested include K-nearest neighbour (KNN), support vector machines (SVMs), and tree-based models including random forest (RF). They use a total of 80 features extracted from four smartphone sensors (accelerometer, gyroscope, GPS, and rotation vector) to train their models and manage to achieve classification accuracies of 91.2% for KNN, 94.6% for SVMs, 87.3% for decision trees, and 95.1% for a bagging and RF model. [PERSON] and colleagues [35] propose the use of so-called'magnitude' values as well as a time-based history set to filter out noise for enhanced classifier results. They attain an initial 97.7% accuracy for three classes (walking, car, and train). In their subsequent publication [36], [PERSON] and colleagues further evaluate their approach using seven classes (standing, walking, driving, train, bike, city bus, and national bus), where they achieve a mean accuracy of 79% for accelerometer-only, 87% for accelerometer and Gyroscope, and 95% for using accelerometer, gyroscope, and geo-locational data all together.