Abstract
This paper presents a Mixed-Integer Programming Model for the urban freight transport planning problem through the estimation of the Origin-Destination Matrix. The Origin-Destination Matrix is used to know the pattern of travel or vehicle flow between different zones of a city and is estimated from the counting of vehicles on the routes of a road network. For the estimation of the Origin-Destination Matrix, the Entropy Maximization approach is applied. This approach is based on a non-linear optimization model. In order to overcome this difficulty, an optimization model based on the Piecewise Linear Approximation Method is proposed. To test the proposed model, an instance was built based on a road network of a real case. The proposed model obtained good results in a reduced computational time, demonstrating its usefulness for the urban freight transport planning.
Highlights
The problem of urban freight transport, is one of the most complex and challenging problems facing the public sector, since it is responsible for financing new road infrastructure or improvements in these
A Mixed-Integer Programming Model based on the Piecewise Linear Approximation Method is proposed
The difficulty of the Entropy Maximization approach lies in its formulation, which is nonlinear in nature
Summary
The problem of urban freight transport (freight transit or goods orders), is one of the most complex and challenging problems facing the public sector, since it is responsible for financing new road infrastructure or improvements in these. According to [7], the modeling of urban freight transport should focus mainly on the flow of cargo, which is related to road infrastructure and the freight transit between suppliers (origin) and consumers (destination). This modeling is based on estimating the Origin–Destination Matrix (OD Matrix), which measures the level of vehicle congestion in a future road network [8].
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