Off-street parking facility location on urban transportation network considering multiple objectives: a case study of Isfahan (Iran)
Abstract
The increasing population in large cities and the unbalanced urban growth associated with massive use of private cars in metropolitan areas often lead to traffic jams and road congestion that warrant the construction of such capital-intensive buildings as off-street public parking facilities. However, the initial problem in such projects is locating a suitable spot where all citizens can be conveniently served and the traffic load in busy city centers can be reduced by removing the need for on-street parking facilities. In this paper, an urban transportation network including, a number of parking demand points, a set of possible sitting locations, and several entry points of traffic flow are considered. Four objectives are generally considered for the public off-street parking location problem that include reducing traffic congestion, maximizing coverage demand, minimizing walking distance between demand points and new parking facilities, and decreasing related costs. The flow-capturing model has been exploited to develop two approaches for minimizing traffic congestion. Based on these approaches and other objectives, two models have been proposed. The covering distance of parking facilities will be uncertain in these models. Traffic flow entry points, driver’s path, and different types of parking lots have also been considered. Finally, relevant information and data required for implementing the proposed models were collected on two traffic zones in Central Business Districts (CBD) of Isfahan (Iran). Then, the ε-constraint method was used to solve the proposed multi-objectives models and the best candidate points for establishing new off-street parking facilities were determined.
First Published Online: 4 Sept 2017
Keyword : facility location, public parking lots, off-street parking facility, flow-capturing, multi-objective decisionmaking, mathematical modelling
This work is licensed under a Creative Commons Attribution 4.0 International License.
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