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Many negative externalities are associated with home deliveries, the main ecommerce delivery destination. Despite many solutions that address this problem, the lack of understanding of the spatial pattern of urban deliveries makes it challenging to implement these strategies. This paper analyzed the spatial relationship between sociodemographic, retail and e-commerce deliveries in Belo Horizonte (Brazil) by using official data at the neighborhood level (number of retail shops, gender, income, age, race, and household size), and e-commerce deliveries performed by a transportation company. Global Moran's I indicated the spatial dependence of the e-commerce deliveries. Results of a geographically weighted regression model showed a positive spatial effect of retail, women, Asian population, age from 20 to 29 years old, and income. In addition, a negative spatial effect was identified for the size of the household, 18 to 19 years old, and the black population. Furthermore, the estimated coefficients show small spatial variability, indicating homogeneity in the spatial relation. The uniformity of the parameters indicates that alternative strategies can be implemented throughout the territory to reduce e-commerce deliveries.

The lack of efficiency in the use of the on-street infrastructure dedicated to loading and unloading freight in the city, with their shortage and inefficient placement, generates congestion and delays during urban freight deliveries. An exploratory study is presented on the feasibility of incorporating technology into the management of dedicated loading/unloading areas for urban logistics in downtown Montevideo (Uruguay). After data of the neighborhood was collected, and the main stakeholder interest gathered, the paper analyzes a feasible technological solution to the problem, considering the restrictions posed by the stakeholders for sharing information. Due to the increasing influence of blockchain and its implementation for specific logistic problems, such as product traceability, the introduction of blockchain as part of the solution is considered and discussed in the system architecture

The warehouse location is a critical factor in the efficiency of urban freight transport. However, several factors influence this location. This paper analyses the spatial correlation of characteristics of cities in a metropolitan region and the location of the warehouses through a case study in the Belo Horizonte metropolitan region. The municipalities' socioeconomic and fleet data were analysed using the Local Moran Index and Bivariate Moran Index statistics. The results were presented by clusters using the Local Indicator of Spatial Association. Populational density, land cost, and truck fleet are spatially correlated with the warehouses' location. Furthermore, the characteristics of some municipalities, as Contagem, Ribeirão das Neves, and Ibirité, create high-high clusters, with high spatial correlation in the municipalities of cluster and, also, in the surroundings. Findings suggest the importance of land use, logistics, and real estate sector public policies minimise warehouses' externalities and contribute to economic development.

In an urban freight distribution system, determination of the number and location of loading-unloading places is required to regulate loading-unloading operations. This pa­per presents mathematical models for on-street loading-unloading space location based on set-covering problem and p-median problem formulations. The approaches was tested with real data: an area has 160 city blocks and 60 on-street loading-un­loading spaces, in Fortaleza, Brazil. We evaluated four scenarios considering different radius of influence of a loading/unloading spaces. The results indicate this approach has potential for achieving gains in terms of reduction of the distance between the clients and the loading and unloading places: considering that the average distance is a performance indicator (ratio between the total distance and the covered clients), a radius of influence of 400 meters has best relation (0.489) and all clients are covered. The results indicate that the model can be used by planners to allocate loading and unloading areas.