The already significant volume of freight vehicles moving within city limits is steadily growing, and is expected to continue increasing at an even faster rate. Many European cities, however, have historical urban heritages and constraints that make them a logistics nightmare, where traffic congestion can result from improperly parked vehicles. In this paper, we focus on the location and sizing of commercial parking lay-bys in urban centres, where it is possible to park for a limited amount of time to perform loading/unloading operations and deliveries. The problem has been formulated and addressed with reference to a central district in the city of Bergamo, with a strong commercial presence, and characterized by significant problems of traffic congestion. We present a mixed analytic-Monte Carlo simulation approach in order to find an optimal distribution and relative sizes of the lay-by areas according to the demand and location of the business activities.

(2016). Loading/unloading lay-by areas location and sizing: a mixed analytic-Monte Carlo simulation approach . Retrieved from http://hdl.handle.net/10446/79401

Loading/unloading lay-by areas location and sizing: a mixed analytic-Monte Carlo simulation approach

Pinto, Roberto;Golini, Ruggero;Lagorio, Alexandra
2016-01-01

Abstract

The already significant volume of freight vehicles moving within city limits is steadily growing, and is expected to continue increasing at an even faster rate. Many European cities, however, have historical urban heritages and constraints that make them a logistics nightmare, where traffic congestion can result from improperly parked vehicles. In this paper, we focus on the location and sizing of commercial parking lay-bys in urban centres, where it is possible to park for a limited amount of time to perform loading/unloading operations and deliveries. The problem has been formulated and addressed with reference to a central district in the city of Bergamo, with a strong commercial presence, and characterized by significant problems of traffic congestion. We present a mixed analytic-Monte Carlo simulation approach in order to find an optimal distribution and relative sizes of the lay-by areas according to the demand and location of the business activities.
2016
Pinto, Roberto; Golini, Ruggero; Lagorio, Alexandra
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/79401
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