This paper analyzes the impact of Accessibility based on GPS data for both motorized and non-motorized modes on distance traveled by private and public transport users in developing countries. The data was collected from a mobile phone app called Inertia. A neural network model was developed for the dependent variable distance. The independent variables are the Level of Service (time, speed) measured via GPS data, public transport (e.g., distance to the nearest station, number of stops/stations within the catchment area) called PT Availability, and accessibility (off-peak and peak neighborhood and station accessibility). The results show that the type of station and neighborhood peak access promotes better modeling of the distance.