A multinomial logit (MNL) model of home-to-work (HW) travel mode choice in the morning peak period is the proposed model for the particular area of study to analyze the explanatory variables that influence the choice of travel mode. This method is essential because the choice of mode is not only influenced by spatial constraints but also by socio-demographic constraints. To evaluate these variables, a multinomial logit model is used and five mode choice utility functions are developed to determine commutes during morning rush hours. To conclude whether our proposed model can be implemented properly or not, regional travel survey data is fed into the model to produce real results. The model results can predict with 50.7% certainty morning rush hour trips for this particular study area. By determining an accurate pattern of commute mode choice during morning rush hour, policymakers can use the results of this model to aid in decision making for policies regarding transportation, sustainability, and future planning for this particular area study. A dataset was provided by a regional travel survey that was used for modeling purposes and included information regarding explanatory variables of home-to-work (HW) travel mode in the morning peak period. As shown below in Figure 1, a decision tree is presented that outlines morning HW trips in the peak periods that were modeled. A multinomial logit model was deemed to be the preferable model for this task because this particular model is capable of analyzing choices with multiple variables. In comparison, one model that is not a good fit is a binary logit model which would be used for models that have fewer than two variable choices. Data are...... middle of paper ......or, arterial/collector/and local streets; optimal layout of transits; and streetscape design. To obtain favorable and accurate results, an additional number of iterations of the dataset should be processed if time permits. Likewise, if time permits, it is best to start with all the variables of each utility function and then work backwards through the complete process of backward elimination. Furthermore, to obtain refined results, it may be advantageous to use advanced modeling software such as PTV Visum Software.Works CitedBierlaire, M. (2003). BIOGEME: A free package for estimating discrete choice models, Proceedings of the 3rd Swiss Transport Research Conference. Ascona, Switzerland.Idris, OA (2014). Transport planning and design. [Lesson notes]. Retrieved from https://connect.ubc.ca/