###### Abstract
In addition, ITS still faces a budget challenge: traffic sensors are not widely installed in urban areas due to their cost, and are mostly found on highways and freeways, where most accidents happen due to the increased speed limits. Therefore, logistic companies still need a framework that will not disrupt ITS planned resources and can be used for effective traffic prediction regardless of the area of delivery. Currently, cities use traffic sensors mostly on highways or freeways due to lack of budget. As a result, research has been predicting traffic to prevent accidents on these types of roads or to have a better infrastructure for the traffic management system. However, shifts in e-commerce and logistics demand more from Intelligent Transportation Systems (ITS), which are a challenge to implement, and this calls for resources such as money, workforce, and time. This issue leads to inaccurate, sometimes non-existent, traffic prediction for urban and residential areas, where most packages are delivered to end customers (nano stores, houses, residential buildings, communities, etc.). There is a potential solution using social media, which hubs millions of geotagged data regarding check-ins, events, and movement from masses of people. While smart cities have developments in areas such as smart grids and autonomous vehicles [6; 7], ITS still lacks a framework where social media data, which has been applied to supply chain management and e-commerce [8], is implemented in a traffic prediction tailored for last-mile deliveries. The need for this framework is derived from companies such as Amazon, where optimal routes are calculated and given to drivers before starting the day. However, drivers often change them because the ones previously retrieved are not optimal given the circumstances at the time of delivery. Dynamic Vehicle Routing Problems (DVRP) are a must for the supply chain, given that traffic is inconsistent throughout the week or the day, and these changes may cause delivery delays. The same route used for one day might not be optimal for the next one, not even for the same customer list delivered during a different shift. Having a framework to optimize DVRPs without demanding more from ITS by using external factors now looks like the next step to optimize last-mile delivery. The following research questions were derived before conducting the systematic literature review and considering the challenges stated in the Problem Statement. These questions are the basis of this research. In addition, these questions are the main part of the eligibility criteria for the deletion and inclusion of papers in the study and further analysis. 1. Is it possible to characterize an urban road network with dynamic data, such as weather, traffic, and social media updates, to implement into a Dynamic Vehicle Routing Problem methodology to improve last-mile delivery? 2. Can a local social network's behavior contribute to traffic prediction? 3. Could this framework be an initial point to further implement real-time route optimization, if needed, for highly variable optimal routes? The primary contributions of this research can be summarized as follows: (1) a systematic literature review following the PRISMA framework to summarize the contributions conducted in social media usage for traffic prediction and traffic usage for vehicle routing; (2) an illustrative example of the traffic prediction methodology and a proposal for a new social network feature extraction. In Section 2, we conducted a literature review of existing research on traffic prediction and social network analytics to provide context for our study. Section 3 describes the methodology used in our research, including the data collection and analysis techniques applied. Section 4 presents the framework for traffic prediction that we propose, which is based on social network analytics. In Section 5, we provide an illustrative example to demonstrate the effectiveness of our proposed framework.