An integrated simulation and business intelligence framework for designing and planning demand responsive transport systems

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Abstract

Transportation planners, analysts and decision-makers usually make use of simulation tools in order to accurately predict the performance of new practices and policies before their implementation. Additionally, most of them, recently introduced new auxiliary Business Intelligence decision support tools in order to transform their available huge amount of real operating data into timely and accurate information for their decisions. However, although both of those types of automated decision support are valuable, very few attempts were already made by researchers to integrate them into a unified tool. This paper proposes a general conceptual framework for a strategic and tactical decision support system that integrates both of these technologies, simulation and Business Intelligence, in order to enhance the overall practical interest for its usage by taking advantage of the foreseen synergy of the integration. This approach is then extended and illustrated to the case of demand responsive transport (DRT) systems. DRT systems provide transport on demand for users, using flexible schedules and routes to satisfy their travel needs. The paper discusses the potential interest of the proposed integration, identifying a set of questions that can then be answered with effectiveness and more efficiently than before. In particular, such set comprises a list of strategic and tactical issues that are of crucial importance to the design and planning of sustainable DRT systems, before its implementation, thus overcoming the current lack of analytical tools to this end. © 2013 Springer-Verlag.

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APA

Telhada, J., Dias, A. C., Sampaio, P., Pereira, G., & Carvalho, M. S. (2013). An integrated simulation and business intelligence framework for designing and planning demand responsive transport systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8197 LNCS, pp. 98–112). Springer Verlag. https://doi.org/10.1007/978-3-642-41019-2_8

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