Sustainable Smart Cities: Optimization of Demand Responsive Transport by Using Data Science Tools
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Faced with the widely demonstrated disadvantages of individual transport, demand responsive transport (DRT) is the solution chosen by companies to ensure the transportation of its employees, however, the growth of industrial activities generates great difficulty in managing this model of transport. This research can be applied to very broad areas, in our case we aim to apply it to the city of Tangier. The objective is to minimize the collection routes of transport vehicles. In this article, we propose an optimization approach in data mining, by using clustering, multidimensional positioning (MDS), as well as a wide range of graph theory techniques. It is a vital element, of the strategies developed within the smart cities framework. We test our approach by a random data set of 40 employee pick-up points. An optimal cycle is founded, optimizing the collection.
KeywordsOptimization Transport Demand responsive transport Smart cities
- 1.CASTEX E: Le transport à la demande (TAD) en France: de l’état des lieux à l’anticipation. Modélisation des caractéristiques fonctionnelles des TAD pour développer les modes flexibles de demain, Ph.D. thesis, 480 p. (2007)Google Scholar
- 8.Shalizi, C.: Distances between Clustering, Hierarchical Clustering. Carnegie Mellon University, Pittsburgh (2009)Google Scholar
- 9.Ye, Y.: Data mining : Theoris, Algorithms, and Examples. CRC Press/Taylor, Boca Raton/Milton Park (2008)Google Scholar