# Last-Mile Optimization Using Neural Networks
## 2 Related Work
### Optimizing Logistics: A Focus on Classification and Last-Mile Efficiency  
We found recent relevant articles on logistics in which ML techniques were used to solve many problems related to classification. One study [28] focused on addressing roadaccidents in logistics transportation caused by careless driver behavior. It introduced an autonomous system based on a convolutional neural network to recognize and classify various risky driving positions. The proposed model achieved an impressive 98.98% accuracy when evaluated against the State Farm Distracted Driver Database, outperforming other algorithms with different descriptors and classification techniques. The authors explored the optimization of last-mile logistics using machine learning approaches. Their study involved a bibliometric analysis and a critical review to highlight key issues, utilized algorithms, and case studies in the field ([PERSON] et al., 2023) [29]. Another study pioneered a hybrid approach, fusing Reinforcement Learning and rule-based agent models for microlevel traffic flow optimization. Agents adapt decisions based on local sensors, departing from traditional system-level methods. The innovation extends applicability beyond traffic to logistics and general transport, demonstrating efficacy through agent-based modeling [30]. The study of [PERSON] and [PERSON] (2022) [31] presented an efficient e-commerce logistics path optimization algorithm using NSGA-II and an enhanced genetic algorithm. Integration of an ant colony algorithm achieved a 6.6% reduction in route length, demonstrating substantial time savings in e-commerce logistics distribution. One study ([PERSON] and [PERSON], 2021) [32] addressed urban traffic congestion issues by proposing sustainable alternatives to road transportation through a co-creation methodology involving local authorities, companies, and citizens. The paper introduced a decision-aided simulation tool architecture for optimizing these alternative solutions and emphasized the collaboration of stakeholders in developing sustainable urban transportation strategies. Another prior study introduced a Convolutional Neural Network-Gated Recurrent Units (CNN-GRU) combined model for predicting railway logistics traffic speed, leveraging both spatial and temporal features. The experiment demonstrated superior accuracy compared to individual GRU and CNN models. The prediction results were further utilized in a multiobjective optimization model for efficient train route planning [33]. The study of [PERSON] and [PERSON] (2022) [34] emphasized the crucial role of the Transportation Management System (TMS) and data analysis techniques, including machine learning and data mining, in enhancing logistics distribution performance. The proposed methodology compares three prediction methods, highlighting the effectiveness of the Artificial Neural Network (ANN) in achieving more accurate results for freight transport demand forecasting. [PERSON] et al. (2020) [35] focused on enhancing freight transport efficiency through accurate travel time prediction using ML algorithms like ExtraTrees, AdaBoost, and SVR. The study utilized real-world multimodal container transport data and found SVR to achieve the highest prediction accuracy compared to other models, outperforming average-based approaches with a mean absolute error of 17 h for transport times up to 30 days. One study addressed the sustainable development of road freight transport by proposing a supervised ML model with intelligent classification algorithms and key performance indicators. SVR demonstrated the highest accuracy (98%) among various algorithms, enabling the establishment of sustainability levels. The model was applied to a European road freight company, facilitating the formulation of green strategies for sustainable development ([PERSON] et al., 2021) [36].