Abstract
Traffic in a road point is counted, by a device, for more than 1 year, giving us time series. The obtained data have a trend with a clear seasonality. This allows us to split taking each season (1 week or 1 day, depending on the series) as a separated path. We see the set of paths we have like a random sample of paths for a stochastic process. In this case seasonality is a common behaviour for every path that allow us to estimate the parameters of the model. We use a Gompertz-lognormal diffusion process to model the paths. With this model we use a parametric function for short-time forecasts that improves some classical methodologies.
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Acknowledgements
This work has been supported in part by FQM147 (Junta de Andalucía), SIPESCA (Programa Operativo FEDER de Andalucía 2007–2013), TIN2011-28627-C04-02 and TIN2014-56494-C4-3-P (Spanish Ministry of Economy and Competitivity), SPIP2014-01437 (Dirección General de Tráfico), PRY142/14 (Este proyecto ha sido financiado íntegramente por la Fundación Pública Andaluza Centro de Estudios Andaluces en la IX Convocatoria de Proyectos de Investigación) and PYR-2014-17 GENIL project (CEI-BIOTIC Granada). Thanks to the Granada Council, Concejalía de protección ciudadana y movilidad.
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Romero, D., Rico, N., Garcia-Arenas, M.I. (2016). Modellation and Forecast of Traffic Series by a Stochastic Process. In: Rojas, I., Pomares, H. (eds) Time Series Analysis and Forecasting. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-28725-6_21
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DOI: https://doi.org/10.1007/978-3-319-28725-6_21
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