Sensors, Volume 22, Issue 1
2022 January-1 - 408 articles
Cover Story: A reliable estimation of the traffic state in a network is essential, as it is the input of any traffic management strategy. However, the problem of estimating the traffic state alongside combining input data from multiple sensors is complex (e.g., noise, heterogeneity) and involves challenging data fusion. We organized an experiment campaign in an urban area in Zurich, Switzerland, to assess the accuracy of traditional and novel sensors and to propose a fusion methodology. The work focuses on capturing traffic states regarding traffic flows and travel times. Finally, we propose an estimation baseline, multiple linear regression (MLR) (5% data sample), that is compared to a final MLR model fusing the 5% sample with loop detector and traffic signal data. Results compared with ground-truth data demonstrate the effectiveness of the proposed methodology. View this paper
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