## 6 Conclusions and Future Research  
The conclusion of this paper highlights the significance of the proposed methodology in traffic prediction and dynamic vehicle routing problems. The combination of deep learning and social media analytics is expected to bring a new perspective to real-time dynamic vehicle routing problems and improve decision making. The case study results show that applying deep learning techniques can lead to more accurate and efficient predictions. At the same time, integrating social media analytics, point of interest analysis, and network analysis allows for the adaptation of the solution to various scenarios. Some limitations of this proposed methodology would be computational time, since GCNs can be very time demanding as the dataset grows, and there is a lack of data availability for underdeveloped countries (for example, some cities in Latin America). However, these \begin{table}
\begin{tabular}{c c c} \hline  & **GCN-LSTM MAE**[70] & **GCN-BiLSTM MAE** \\ \hline
1 h & 0.13 & 0.161 \\
6 h & 397,956.564 & 0.161 \\
12 h & 84,232.415 & 5,451,617.781 \\
24 h & \(5.48\times 10^{23}\) & 1.5 \\ \hline \end{tabular}
\end{table} 
Table 6: California’s District 7 traffic prediction experiments using [70] as a benchmark. two issues can be resolved in future studies by implementing an attention mechanism and using data from cities that portray a similar lifestyle to Latin America, respectively. Future research should address the challenges of urban last-mile delivery problems in developing countries with limited Intelligent Transportation Systems data. This study is a crucial first step in developing a framework to support these countries in improving their logistics systems. Further research should aim to identify the most influential variables affecting traffic prediction and the impact of social media and other external factors on traffic patterns. Moreover, this framework provides a platform for further advancements in traffic prediction and vehicle routing. It opens the door for new and innovative methods for predicting traffic patterns and optimizing delivery routes. For example, the co-occurrence word graph used in this study can be expanded to include more terms and variables, providing a more comprehensive and nuanced view of the relationships between key concepts in the field. Additionally, the proposed methodology can be modified to account for other factors impacting traffic prediction, such as weather conditions, road construction, etc. In conclusion, the proposed framework for urban last-mile delivery traffic forecasting using social media analytics and deep learning represents a significant contribution to traffic prediction and dynamic vehicle routing problem research. The literature review and case studies indicate the potential of this methodology to bring improved accuracy and efficiency to traffic prediction and to provide new insights into the complex problem of last-mile delivery in urban areas. However, further research is needed to fully realize this framework's potential and continue to advance our understanding of traffic prediction and vehicle routing. Conceptualization, V.L.-F.; methodology, V.L.-F. and E.G.-F  
* [PERSON] et al. (2009) [PERSON]; [PERSON]; [PERSON]; [PERSON]; the PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. _BMJ_**2009**, _339_, b2535. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] Traffic Congestion Level Prediction Based on Recurrent Neural Networks. In Proceedings of the 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Fukuoka, Japan, 19-21 February 2020. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Traffic Speed Prediction Under Non-Recurrent Congestion: Based on LSTM Method and BeiDoN Navigation Satellite System Data. _IEEE Intell. Transp. Syst. Mag._**2019**, _11_, 70-81. [CrossRef]
* [PERSON] et al.