###### Abstract  
The proliferation of e-commerce in recent years has been driven in part by the increasing ease of making purchases online and having them delivered directly to the consumer. However, these last-mile delivery logistics have become complex due to external factors (traffic, weather, etc.) affecting the delivery routes' optimization. Intelligent Transportation Systems (ITS) also have a challenge that contributes to the need of delivery companies for traffic sensors in urban areas. The main purpose of this paper is to propose a framework that closes the gap on accurate traffic prediction tailored for last-mile delivery logistics, leveraging social media analysis along with traditional methods. This work can be divided into two stages: (1) traffic prediction, which utilizes advanced deep learning techniques such as Graph Convolutional and Long-Short Term Memory Neural Networks, as well as data from sources such as social media check-ins and Collaborative Innovation Networks (COINs); and (2) experimentation in both short- and long-term settings, examining the interactions of traffic, social media, weather, and other factors within the model. The proposed framework allows for the integration of additional analytical techniques to further enhance vehicle routing, including the use of simulation tools such as agent-based simulation, discrete-event simulation, and system dynamics. t +
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These conditions and requirements can be anything from known deterministic information such as the number of customers to visit, a list of products to deliver to unknown addresses, and stochastic information such as new orders, same-day delivery, changing traffic patterns, changing weather, etc. A study shows that two of the most important online shopping features for users are information quality and online service quality [3]. They argue that for the business to be sustainable, they would need these characteristics embedded in policies, strategies, and on their website. This research is backed up by an investigation where the purpose was to develop the drivers and outcomes of online shopping experiences [4]. The authors found that the principal driver of the online shopping experience is product/service experience quality, which includes delivery as the most important aspect of this category. This driver even comes before retail price. This means that customers prefer how the product is delivered and the customer service over the price of the product they buy. It seems trivial if we portray ourselves as the ultimate customer when we know that we have a package arriving on a certain day and keep checking the tracking history and updates nonstop. However, if the package does not arrive on the predicted date, our customer satisfaction decreases. The industry grows fast for B2B and B2C e-commerce companies, and management faces a big challenge: the bar keeps rising, and customers will always turn to the best option, usually the faster and most reliable service. Specifically, last-mile delivery has the urge to meet good service delivery, and that usually leads to poor vehicle utilization and service duplication [5]. Therefore, they set a list of challenges that last-mile delivery services face, such as (1) demand patterns and peak times, where processes are pushed nearly out of control; (2) times being shortened between the orders being placed and the delivery of the product by companies who optimize quickly; (3) sets of times too complex for the service to deliver quality, including time windows and restrictions; (4) delivery failures for residential addresses; (5) high return rate due to failures in attempts to manage stated challenges; and (6) lack of logistics, such as distribution centers, budget, vehicles, drivers, etc. to fulfill the demand rate. Even if traffic can be predicted from weather and historical traffic data, it does not have the desired accuracy for more residential areas.