## 2 Literature Review and Conceptual Context  
This literature review was conducted following the PRISMA framework [9]. No previous research protocol was followed to conduct this literature review. The search strategy focused on the existing solutions to last-mile dynamic VRPs, with special attention to solutions including deep learning and weather/social media factors influencing traffic conditions. The search only considered peer review journal articles and conference papers from Compendex/Inspect, Web of Science, and IEE that were published during the last six years (2014-2020) and the ones that will be published in 2021. The following combination of topics was searched in the title, keywords, and/or abstract: Weather AND Traffic, Weather AND Route, Weather AND Last-Mile, Route AND Traffic, Last-mile AND Route, Last-mile AND Machine Learning, Machine Learning AND Route, Weather AND Traffic AND Route, Last-mile AND Route AND Machine Learning, Route AND Deep Learning AND Weather, Social Media AND Traffic, Social Medi AND Last-mile, Social Media AND Route. The operator OR was used to group all the keywords from a topic. A detailed list of the topics with their respective keywords is shown in Table 1.  
Some topics were excluded during the exploration phase, such as optics, telecommunication networks, ship routing, offshore delivery, air traffic, air control, air pollution control, unmanned aerial vehicles, power grids, public transportation, road safety, bike trips, etc. The PRISMA framework for systematic search steps was conducted. A summary of the steps is shown in Figure 1.  
Using the steps and framework from PRISMA guidelines, 4957 papers were selected during the Identification phase. After duplicate removal, there were 2370 papers. Next, during the Screening phase, titles and abstracts were evaluated, and 1837 papers were excluded because they included one or more of the following topics: traffic accident analysis, parking problems, ship routing, speed analysis (without considering traffic), traffic psychology, fiber/optical networks, air traffic, road conditions, the crowd flows, pollution analysis, data collection methods, electric vehicle, unmanned aerial vehicles, wireless networks, humanitarian logistics, drone deliveries, traffic assignment problem, green routing, traffic flow distribution, emergency routing, parking search, arc routing problems, inventory problems, aviation, and VANETs. After discarding papers, 137 articles were assessed for eligibility, and 396 were excluded due to eligibility criteria stated in \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Weather** & **Last-Mile** & **Route** & **Machine Learning** & **Traffic** & **Social Media** \\ \hline Weather & Last mile & Routing & Neural networks & Traffic flow & Social media \\ Climate & Last mile & Route & Deep learning & Traffic volume & Social networks \\  & & Routes & Reinforcement learning & Traffic speed & Geotag \\  & & Vehicle routing & Neural network & Traffic prediction & Geolocation \\  & & Vehicles routing & Expert systems & Traffic & Location-based \\  & & Urban routing & Expert system & Traffic congestion & Location based \\  & & & Machine learning & Transit & Twitter \\  & & & Data mining & Traffic jam & Instagram \\  & & & & Google places \\  & & & & Facebook \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of topics and respective keywords. Figure 1: PRISMA framework for systematic search steps. Source: [9]. previous steps or were not aligned with research questions. Finally, 137 articles were analyzed, and 56 were included in the literature review. Figure 2 presents a co-occurrence word graph visually representing the relationships between key terms in traffic prediction, urban logistics, and vehicle routing vocabulary. The graph was created by analyzing a corpus of relevant literature in these fields and identifying the terms that frequently appear near each other. The main clusters in the graph represent groups of terms that are closely related to each other.