Studying Solutions of the p-Median Problem for the Location of Public Bike Stations

Abstract

The use of bicycles as a means of transport is becoming more and more popular today, especially in urban areas, to avoid the disadvantages of individual car traffic. In fact, city managers react to this trend and actively promote the use of bicycles by providing a network of bicycles for public use and stations where they can be stored. Establishing such a network involves the task of finding best locations for stations, which is, however, not a trivial task. In this work, we examine models to determine the best location of bike stations so that citizens will travel the shortest distance possible to one of them. Based on real data from the city of Malaga, we formulate our problem as a p-median problem and solve it with a variable neighborhood search algorithm that was automatically configured with irace. We compare the locations proposed by the algorithm with the real ones used currently by the city council. We also study where new locations should be placed if the network grows.

Solving a Real-World Urban Postal Service System Redesign Problem

“…Wheeler [52] improved the location decisions on police patrol areas with a modified PMP so that both the total travel distance and the inequality of call allocation can be reduced. Cintrano et al [53] applied a neighborhood search algorithm to determine the best locations of public bike stations in Malaga. In addition, comparisons between both the MCLP and the PMP were reported in several practical applications, e.g., healthcare services [54], printer locations in an university campus [55], and public charging stations for the electric vehicle [16].…”

Placement optimisation for station‐free bicycle‐sharing under 1D distribution assumption

“…Conrow et al [26] considered both the service demand and residential population in their objective function. Cintrano et al [27] studied the impacts of different kinds of distance (such as Euclidean distance, real distance calculated by Dijkstra algorithm, and distance weighted by population densities) on the optimal solution of the p-median model. They found that the solutions based on the weighted real distances are the best.…”