Sensors, Vol. 22, Pages 144: An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation

Sensors, Vol. 22, Pages 144: An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation Sensors doi: 10.3390/s22010144 Authors: Alexander Genser Noel Hautle Michail Makridis Anastasios Kouvelas A reliable estimation of the traffic state in a network is essential, as it is the input of any traffic management strategy. The idea of using the same type of sensors along large networks is not feasible; as a result, data fusion from different sources for the same location should be performed. However, the problem of estimating the traffic state alongside combining input data from multiple sensors is complex for several reasons, such as variable specifications per sensor type, different noise levels, and heterogeneous data inputs. To assess sensor accuracy and propose a fusion methodology, we organized a video measurement campaign in an urban test area in Zurich, Switzerland. The work focuses on capturing traffic conditions regarding traffic flows and travel times. The video measurements are processed (a) manually for ground truth and (b) with an algorithm for license plate recognition. Additional processing of data from established thermal imaging cameras and the Google Distance Matrix allows for evaluating the various sensors’ accuracy and robustness. Finally, we propose an estimation baseline MLR (multiple linear regression) model (5% of ground truth) that is compared to a final MLR model that fuses the 5% sample with ...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research