PhD PROJECT SPECIFICATIONYear 2013-2016Student name: XXXContact telephone number: XXXProject Title as submitted: Smart Mobility Profiling and GamificationBackground and MotivationIn many countries, the proportion of urban population has increased in recent decades. For instance, Chinas total urban population is currently just over a half of its total population, rising from 26% in 1990. This mirrors a trend in most other developed countries. Urbanization1, regarded as a strategy for accelerating development by some city governors, has also resulted in many challenges to urban public services. One of the main urban public services is transportation, which is in part governed by strategic transport policies in terms of both sustainability (CO2, air pollution) and competitiveness (congestion) and by incentives (e.g., cheaper off-peak private transport travel). A key challenge to all major cities is how to configure travel demand management (TDM), often referred to as mobility management policies and incentives to reduce congestion, accidents and pollution. Smarter transportation profiling both at an individual level to inform the individual and at a (spatial-temporal) group/public level to inform authorities, can lead to beneficial shifts in mobility to help address these challenges. TDM includes any policy to encourage better ways to use transport resource, for instance by offering people incentives to reduce their car use. The use of Gamification is investigated to set incentives and improve the engagement of target-specific app users 2. Gamification is the use of game theory and game design techniques in non-game contexts, in this case in urban travel, in order to encourage people to adopt , or to influence how they use transportation. An example of an urban travel game consist of travelers being awarded different levels of points for use of different travel modes. PROJECT (RESEARCH) AIMSThe main objective of this project is to use both smart mobility profiling and gamification of urban travel to research and develop (R&D) better models of individual and aggregated urban mobility in order to better understand and aid shifts in mobility to meet strategic TDM goals. In order to achieve this overall R&D objective, three specific sub-objectives and methods are planned: First, to R&D two apps to acquire mobility data from both mobile sensors (e.g., phones) and fixed infrastructure (e.g., traffic sensors) in two cities Beijing and London; Second, to generate individual mobility profiles and to data mine aggregates of mobility data to model mobility patterns; Third, to correlate the mobility patterns of actual sensed data combined with gamification simulations of urban mobility to evaluate the effect of what-if scenarios of different transportation policies and incentives.NOVELTYThe novelty for this study is mainly reflected by the exploitation of both smart mobility profiling and gamification. Nowadays, almost all people have one or more mobile devices such as smart phones, which give us a facility for developing some mobile applications to collect mobility data from mobile devices. Making full use of individual mobile devices and existing fixed infrastructure is quite important in terms of the cost efficiency of deployment and maintenance. However, the proliferation of most mobile applications relies on the users engagement. Gamification can be applied to the apps to motivate more people to use and keep them. METHODOLOGYThe methodology for the study involves mobility data collection, mobility profiling, data mining and modeling, gamification. Considering its good cross-platform ability and portability, we will use Java language to develop two apps: One runs on Android (or other mobile platforms) smartphones to collect mobility data from mobile users, with some game motivation strategies to encourage users try to use it and keep it; Another runs on a PC or laptop to collect data from fixed infrastructure and to conduct some necessary data processing.After acquiring mobility data required, methods (e.g. mobility path construction, topology construction, pattern discovery) described in 3 will be used to generate individual mobility profiles. According to 3, there are several algorithms for the pattern mining such as GSP, SPADE, AprioriAll. It is recommended to contain time-context information when representing mobility profiles.Gamification methodology is used to set incentives and improve the engagement of target-specific app users in this research. Gamification generally use six strategies 4: scores, levels, challenges, leaderboards, achievements and rewards. For this study, these strategies can be used not only to collect mobility data, but also to encourage people to adopt, or to influence how they use transportation. EXPECTED OUTCOMESThe expected outcomes for this study as follows:Two apps, which support common communication techniques (e.g.,