In today’s age, trip-making behaviour is no more a mere linearity. The socio-economic environment around us has a quintessential bearing on the travel characteristics and hence the land-use pattern. The latter half of the century’s pressing problems on rapid urbanization to facilitate futuristic development is a situation at hand which requires redressal on both issues with fair just. The growth of infrastructural needs of a country is dependent on various level of demographics. One such imperative tool is the travel demand modelling, which essentially aims to synchronize and produce results of a far more balanced transport system approach. The present study focuses on understanding and analysing the trip-making behaviour of a working-class student community based in Bengaluru. The study preponderantly focuses on the disaggregated model as their trip making behaviour digresses from the normal sect and are generally found to be non-home based trips. To capture the trend of trip generations and productions of the study area, pre-defined questionnaires were used through an online platform as an effective communication mode and to gather relevant information. The data thus collected were pragmatically analysed by assigning the trip-ends (both origin and destination) to the eight identified Bruhat Bengaluru Mahanagara Palike zones, in short, BBMP zones of Bengaluru city. Furthermore, in this paper, the said data has been studied at three levels namely—cross-classification technique, trip distribution stage and multi-linear regression analysis to understand the causation effects of independent variables (household size, education, vehicle and car ownership) on dependent variable (trip). The cross-classification technique with two different approaches used in this study attempts to effectively quantify the changes in independent variables to that of the dependent variable. The trip distribution modelling provides a better picture of probable trip apportioning of three major zones, that is, West, South and East where maximum number of trips are generated from and attracted to, highlighting the need of optimum projections for horizons. Also, the results of multi-linear regression have yielded a strong correlation of 97.9% implying the significant role of chosen independent variables on dependent variables for the study. Thus, chosen variables of the present study also attempts showcase a rather fairly more realistic scenario that persists as matter of concern for Bengaluru’s urban transport planning.