dc.contributor.author Ramírez, Manuel dc.contributor.author Gavilán, José Manuel dc.contributor.author Aguilera-Venegas, Gabriel dc.contributor.author Galán-García, José Luis dc.contributor.author Galán-García, María Ángeles dc.contributor.author Rodriguez-Cielos, Pedro dc.date.accessioned 2015-07-28T09:11:26Z dc.date.available 2015-07-28T09:11:26Z dc.date.created 2015 dc.date.issued 2015-07-28 dc.identifier.uri http://hdl.handle.net/10630/10173 dc.description.abstract Traffic simulations usually require the search of a path to join two different es_ES points. Dijkstra’s algorithm [1] is one of the most commonly used for this task due to its easiness and quickness. In [2, 3] we developed an accelerated time simulation of car traffic in a smart city using Dijkstra’s algorithm to compute the paths. Dijkstra’s algorithm provides a shortest path between two different points but this is not a realistic situation for simulations. For example, in a car traffic situa- tion, the driver may not know the shortest path to follow. This ignorance can be produced, among others, because one of the following two facts: the driver may not know the exact length of the lanes, or, even knowing the exact length, the driver may not know how to find the shortest path. Even more, in many cases, a mixture of both facts occurs. A more realistic simulation should therefore consider these kind of facts. The algorithm used to compute the path from one point to another in a traffic simulation might consider the possibility of not using the shortest path. In this talk, we use a new probabilistic extension of Dijkstra’s algorithm which covers the above two situations. For this matter, two different modifications in Di- jkstra’s algorithm have been introduced: using non-exact length in lanes, and the choice of a non-shortest path between two different points. Both modifications are used in a non-deterministic way by means of using probability distributions (classi- cal distributions such as Normal or Poisson distributions or even "ad hoc" ones). A precise, fast, natural and elegant way of working with such probability distributions is the use of a CAS in order to deal with exact and explicit computations. As an example of use of this extension of Dijkstra’s algorithm, we will show the ATISMART+ model. This model provides more realistic accelerated time sim- ulations of car traffics in a smart city and was first introduced in [4] and extended in [5]. This model was developed combining J AVA for the GUI and M AXIMA for the mathematical core of the algorithm. The studies developed in the above mentioned works, dealt with Poisson, Ex- ponential, Uniform and Normal distributions. In this talk we will introduce, as a novelty, the possibility of using other continuous probability distributions such as: Lognormal, Weibul, Gamma, Beta, Chi-Square, Student’s t, Z, Pareto, Lo- gistic, Cauchy or Irwin-Hall, and other discrete distributions such as: Bernouille, Rademacher, Binomial, Geometric, Negative Binomial or Hypergeometric. Even 1 more, this new version allows to deal with any “ad-hoc” continuous, discrete or mixed user’s distributions. This fact improves the flexibility of ATISMART+ model. dc.description.sponsorship Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. es_ES dc.language.iso eng es_ES dc.rights info:eu-repo/semantics/openAccess es_ES dc.subject Tráfico - Simulación por ordenador es_ES dc.subject.other Traffic Simulation es_ES dc.subject.other CAS es_ES dc.subject.other JAVA es_ES dc.subject.other Accelerated-time simulation es_ES dc.subject.other Smart city es_ES dc.subject.other Dijkstra's Algorithm es_ES dc.title Making more flexible ATISMART+ model for traffic simulations using a CAS es_ES dc.type info:eu-repo/semantics/conferenceObject es_ES dc.centro Escuela Politécnica Superior es_ES dc.relation.eventtitle ACA 2015 es_ES dc.relation.eventplace Kalamata, Grecia es_ES dc.relation.eventdate 07/2015 es_ES dc.identifier.orcid http://orcid.org/0000-0002-8773-6998 es_ES dc.cclicense by-nc-nd es_ES
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