Abstract-SMS messaging is a popular media of communication. Because of its popularity and privacy, it could be used for many illegal purposes. Additionally, since they are part of the day to day life, SMSes can be used as evidence for many legal disputes. Since a cellular phone might be accessible to people close to the owner, it is important to establish the fact that the sender of the message is indeed the owner of the phone. For this purpose, the straight forward solutions seem to be the use of popular stylometric methods. However, in comparison with the data used for stylometry in the literature, SMSes have unusual characteristics making it hard or impossible to apply these methods in a conventional way. Our target is to come up with a method of authorship detection of SMS messages that could still give a usable accuracy. We argue that, considering the methods of author attribution, the best method that could be applied to SMS messages is an n-gram method. To prove our point, we checked two different methods of distribution comparison with varying number of training and testing data. We specifically try to compare how well our algorithms work under less amount of testing data and large number of candidate authors (which we believe to be the real world scenario) against controlled tests with less number of authors and selected SMSes with large number of words. To counter the lack of information in an SMS message, we propose the method of stacking together few SMSes.

Commuters tend to shift from public to private transport modes due to various reasons. This results in an increased traffic volume in the urban road network. The ultimate consequence is traffic congestion which creates massive economic losses and adverse environmental pollution. To provide a feasible solution for the above problems, this study is set to examine the factors affecting transport mode choice. Factor analysis was used to identify the factors influencing the mode choice variability of the commuters. Parameter estimation is done using the Multinomial Logit (MNL) model based on the utility maximization theory. The adjusted likelihood ratio index is used to show the model fits. Also, t-statistics and the respective sign of parameters are used to show the validity of estimated parameters. The outcome of the factor analysis shows that age and occupation significantly affect the public transport mode choice under personal characteristics. For private mode choice, the contributing factors are gender, age, occupation and monthly income. Trip distance, invehicle travel time, and travel cost are significantly influencing both public and private transport mode choices. Results of the estimated parameters and elasticity analysis suggest that more commuters can be attracted to public transport by reducing the total travel time of buses.