Analysis of Freight Trip Generation Model for Food and Beverage in Belo Horizonte (Brazil)


  • Leise Kelli de Oliveira Universidade Federal de Minas Gerais
  • Rodrigo Affonso de Albuquerque Nóbrega Universidade Federal de Minas Gerais
  • Daniel Gonçalves Ebias Centro Universitário Formiguense
  • Bruno Gomes e Souza Corrêa Universidade Federal de Minas Gerais



Today, one of the main challenges faced in urban logistics is the distribution of goods. In Brazil, mid to large cities have experienced consequences of unplanned urban sprawl and lack of adequate transportation infrastructure. The relationship between urban planning and transport stands out the attractiveness of some urban activities with direct impacts on the movement of people and goods and other component elements of urban space. The segment of bars and restaurants falls within this context, therefore is a vital activity responsible for significant percentage of jobs and revenue in a city. Altogether, foods & beverages commercial activities move daily large volumes of goods to meet the need of customers. This paper presents the results of a freight trip generation model developed for pubs and restaurants in Belo Horizonte (Brazil). Once performed the model determined the number of trips generated per day per establishment. In order to expand the discrete result to a continuous one, the results were geographically interpolated to a continuous surface and extrapolated within the city limits. The data for the freight trip generation model were obtained by survey. For this, we designed a structured questionnaire to obtain information about goods, frequency, operational time, place of performance of the loading/unloading of goods, establishment size and the number of employees. Besides these information, we investigated the acceptance of alternative practices in the delivery of goods, such as off-peak delivery. To accomplish the proposed models, we applied a simple linear regression, correlating the following variables: (i) Number of trips versus area of the establishment; (ii) Number of trips versus number of employees; (iii) Number of trips versus operation day of the establishment. With the results of the linear regression for travel generations, conducted the data interpolation based on the standard deviation of the results to define the sample classification bands. This interpolation method was chosen because it is one of the most suitable for analysis of spatially scattered points due to the straightforwardness of the model and because it does not consider extra noise such as slope and spatial constraints as barriers. In this method, interpolation is determined by the value assigned to each point (in this case the number of trips), wherein the closer the points the higher the correlation trend. Finally, the resulting trip generation surface was analysed together with other geographic data such as demographic data, road network density and socioeconomic data. Findings indicate the importance of a mathematic-geographic model for trip generation as a feasible approach for support transportation planning & operation for urban goods distribution. Critical information such as the high concentration of pubs and restaurants in the same region can reinforce the vocation of the city for trading. However, an elevated number of freight vehicles to meet a high and growing demand becomes a problem specially in areas where urban road network is not efficient (not properly designed and parking spaces not properly used). This study also highlights the need for an urban freight mobility plan and public policies, by offering sustainable alternatives for urban goods distribution, which improve the urban environment. By using geospatial analysis, the study delivered statistics data and maps to catch the attention of decision makers and transportation managers, therefore facilitate the discussion on transportation policies in the city of Belo Horizonte.


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How to Cite

Oliveira, L. K. de, Nóbrega, R. A. de A., Ebias, D. G. and Corrêa, B. G. e S. (2017) “Analysis of Freight Trip Generation Model for Food and Beverage in Belo Horizonte (Brazil)”, REGION, 4(1), pp. 17–30. doi: 10.18335/region.v4i1.102.