# Application of Online Transportation Mode Recognition in Games
## 1 Introduction  
This research presents an approach to invoke human behaviour change via machine-learning-supported persuasive games. The primary goal of the game is to explore if it could affect decreased transportation-related carbon footprints through behavioural change. Currently, transportation is the largest source of carbon emissions in the USA [1] and UK [2]. According to OECD [3], it is the fastest growing major contributor to global climate change, accounting for 23% of carbon emissions. According to [PERSON] and [PERSON] [4], 22% of total emissions stem from 'personal travel', of which 68% comes from direct usage while 32% is from indirect influences. On the other hand, research conducted by the Tyndall Centre [5] indicates that personal land-based transport is the major source of carbon dioxide emissions in the UK transport sector and the study reveals that behavioural change is generally perceived as an intractable issue. However, the UK Department of Transport [6] views the promotion of behavioural change and sustainable modes of transport (e.g., public transport) as part of a low-carbon transport strategy for a greener future. Thus, an individual's daily choices of transport could have a significant impact on the total carbon emissions, and it is recommended that cleaner modes of transportation be adopted (i.e., ranging from public transit to biking and walking) [7]. To reiterate, the effectiveness of a carbon reduction strategy largely depends on user acceptability [8], which is closely linkedto users' personal preferences change. The 5 stages (or trans-theoretical) of change model have been discussed in [9]. However, in this research, our focus is on: Stage 3 Preparation--collect information about change and take steps toward the change; Stage 4 Action--take direct action towards achieving a goal; Stage 5 Maintenance--maintain new behaviour (see [9; 10] for details). Information is considered a key element to begin to prepare for change, and strong motivators are key elements for maintaining change. All these elements are prominent in games, making them a viable medium to persuade users to change their behaviour. [PERSON] and colleagues [11] have conducted a critical survey of gamification mechanics to effect behavioural change. These form the bases for the development of a prototype persuasive game for our research. Using techniques from augmented reality and transport detection via machine-learning algorithms, it provides a personal feedback loop to users. Daily physical actions will have repercussions within the game world, stimulating behaviour change. The prototype game fosters multiplayer interaction, which is often lacking in contemporary persuasive games. The game is implemented on Android devices with accelerometers and gyroscope sensors being used to gather data to identify the mode of transportation. The developed prototype persuasive game addresses the following research questions:  
1. RQ1: What aspects of persuasive games could impact on transportation choices?
2. RQ2: How could specific forms of transport be automatically detected without significantly reducing battery life?  
This paper is organised as follows: Section 2 Related Work; Section 3 Delimitations and Experiment Overview; Section 4 Methodology; Section 5 Results; Section 6 Discussion; Section 7 Conclusion and Future Work.