The demand for goods and services is rapidly increasing in cities, in part due to the rise in online shopping and more varied delivery options. Cities around the world are experiencing an influx of goods pickup and delivery activities. The movement of goods within urban areas can be very constraining with high levels of congestion and insufficient curb spaces. Pick-up and delivery activities, specifically those that are out of vehicle activities, encompass a significant portion of urban goods movement and inefficient operations can negatively impact the already highly congested areas and truck dwell times. This dissertation aims to provide insights and data-driven approaches to support freight plans in various cities around the globe with a focus on urban freight deliveries. To accomplish this goal, this dissertation first proposes to discover the current delivery process at the final 50 feet by creating value stream maps that summarize the flow of delivery activities and times, and time variations between activities. The map will be based on the data collected from five freight-attracting buildings in downtown Seattle. Secondly, this research explores contributing factors associated with dwell time for commercial vehicles by building regression models. Dwell time, in this study, is defined as the time that delivery workers spend performing out-of-vehicle activities while their vehicle is parked. Finally, this dissertation predicts the total time spent at the final 50 feet of delivery, including dwell times and parking-related times through discrete event simulations for various “what if” delivery scenarios. Multi-objective simulation-based optimization algorithms were further used to discover the optimal numbers of parking and building resources (e.g. number of on and off-street parking capacity, number of security guards or receptionists). This aims to better understand how increased deliveries in urban cities can impact the cost distributions between city planners, building managers, and delivery workers. This will also identify the areas for improvement in terms of infrastructure and resources to better prepare for the future delivery demands based on various scenarios.