Surveillance System in Service-Oriented Manner

Mierzwinski, Damian; Walczak, Dariusz; Wolski, Marcin; Wrzos, Marcin

doi:10.1109/synasc.2010.83

Cited by 2 publications

(1 citation statement)

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“…Such a framework's construction would be challenging. Factors like redundancy, reliability and severity [25] have to be considered and validated for combining data from disparate sources [26] into meaningful and more accurate predictions of traffic flow [27]. Furthermore, few studies concerning data integration in a real highway context exist prior to this present work, revealing a gap in the scientific field, especially the management of traffic peaks remains an open challenge [28].…”

Section: Improving Results In Peak Periods By Multiform Collection An...mentioning

confidence: 99%

Towards a new intelligent traffic system based on deep learning and data integration

Slimani

Yousfi

Amghar

et al. 2023

IJECE

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<span lang="EN-US">Time series forecasting is an important technique to study the behavior of temporal data in order to forecast the future values, which is widely applied in intelligent traffic systems (ITS). In this paper, several deep learning models were designed to deal with the multivariate time series forecasting problem for the purpose of long-term predicting traffic volume. Simulation results showed that the best forecasts are obtained with the use of two hidden long short-term memory (LSTM) layers: the first with 64 neurons and the second with 32 neurons. Over 93% of the forecasts were made with less than ±2.0% error. The analysis of variances is mainly due to peaks in some extreme conditions. For this purpose, the data was then merged between two different sources: electromagnetic loops and cameras. Data fusion is based on a calibration of the reliability of the sources according to the visibility conditions and time of the day. The integration results were then compared with the real data to prove the improvement of the prediction results in peak periods after the data fusion step.</span><p> </p>

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Section: Improving Results In Peak Periods By Multiform Collection An...mentioning

confidence: 99%