Gonzalez et al. (2010) performed a travel survey study using GPS-enabled phones. They
developed a mobile phone application, called TRAC-IT, to collect real-time GPS data and
passively determine trip mode through the collected data of mobile phones using neural
network approach. In 2008, TRAC-IT was developed in Java Micro Edition, and able to
passively determine record the position of respondents. In data collection phase, 14
participants were provided with a mobile phone, beside their own mobile phone, on which
the TRAC-IT Java ME software was installed and set to record the GPS position of the
mobile phone user every 4 seconds. While participants were travelling using different modes
of transport, the application logged their GPS data and sent the recorded position data to the
server of study. In next step, participant added manual notes regarding their chosen trip
modes, which was used to check the accuracy of mode detection algorithm. Some
snapshots of different section of TRAC-IT is presented in Figure 1 (Gonzalez et al.
2010).This study had some limitations as well. For instance, the immediate data transfer
which was used in this study not only raise ethical concerns, but also forced extra costs on
respondents and used the battery of mobile considerably.
Jariyasunant et al. (2011) developed a smartphone application, called “Quantifiable
Traveller”, which was mainly designed for monitoring users‟ movement. This application is
designed to provide users with personalized feedback and statistics on their travel habits,
burned calories, greenhouse gas emissions, travel time and travel cost. Passive data
collection approach was employed in this application to minimize the burden of users. In
order to minimise the battery consumption, a combination of WIFI (80%) and GPS (20%)
was utilised for position and travel route recognition. “Quantifiable Traveller” records the
location of respondents in 2 minutes intervals; however, considering the fact that in most