Loading/Unloading Space Location and Evaluation: An Approach through Real Data

Abstract

This study presents a real-data approach that aims at optimizing the location of urban loading-unloading (L/U) spaces. The originality of this paper is twofold: first, it proposes a data collection methodology in order to integrate real and up-to-date information regarding cartography, L/U parking demand and existing L/U spaces. Second, an optimization model is developed in order to determine the location of new L/U spaces by taking into account real distances, influence radius and physical constraints. Such optimization model can be used to evaluate the relevance of the existing L/U spaces and/or to determine the optimal location of new ones. For this we propose to combine the use of OpenStreetMap, Google Maps APIs, and Open Data portals. This paper provides a framework composed by 3 models, namely: data collection, demand generation and location optimization. The proposed approach is applied to the city of Paris in order to illustrate different case studies and to assess the effectiveness of the framework.