An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation
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. How...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/1/144 |
| _version_ | 1827667609740378112 |
|---|---|
| author | Alexander Genser Noel Hautle Michail Makridis Anastasios Kouvelas |
| author_facet | Alexander Genser Noel Hautle Michail Makridis Anastasios Kouvelas |
| author_sort | Alexander Genser |
| collection | DOAJ |
| description | 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 conventional loop detector and traffic signal data. The comparison results with the ground truth demonstrate the efficiency and robustness of the proposed assessment and estimation methodology. |
| first_indexed | 2024-03-10T03:21:42Z |
| format | Article |
| id | doaj.art-00a5ef873ac44ec1a6c8147e9b4b0fa3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:21:42Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-00a5ef873ac44ec1a6c8147e9b4b0fa32023-11-23T12:17:35ZengMDPI AGSensors1424-82202021-12-0122114410.3390/s22010144An Experimental Urban Case Study with Various Data Sources and a Model for Traffic EstimationAlexander Genser0Noel Hautle1Michail Makridis2Anastasios Kouvelas3Department of Civil, Environmental and Geomatic Engineering, Institute for Transport Planning and Systems, ETH Zurich, CH-8093 Zurich, SwitzerlandDepartment of Civil, Environmental and Geomatic Engineering, Institute for Transport Planning and Systems, ETH Zurich, CH-8093 Zurich, SwitzerlandDepartment of Civil, Environmental and Geomatic Engineering, Institute for Transport Planning and Systems, ETH Zurich, CH-8093 Zurich, SwitzerlandDepartment of Civil, Environmental and Geomatic Engineering, Institute for Transport Planning and Systems, ETH Zurich, CH-8093 Zurich, SwitzerlandA 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 conventional loop detector and traffic signal data. The comparison results with the ground truth demonstrate the efficiency and robustness of the proposed assessment and estimation methodology.https://www.mdpi.com/1424-8220/22/1/144urban traffic statetravel time estimationtraffic managementtraffic flowlicense plate detectionempirical measurements |
| spellingShingle | Alexander Genser Noel Hautle Michail Makridis Anastasios Kouvelas An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation Sensors urban traffic state travel time estimation traffic management traffic flow license plate detection empirical measurements |
| title | An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation |
| title_full | An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation |
| title_fullStr | An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation |
| title_full_unstemmed | An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation |
| title_short | An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation |
| title_sort | experimental urban case study with various data sources and a model for traffic estimation |
| topic | urban traffic state travel time estimation traffic management traffic flow license plate detection empirical measurements |
| url | https://www.mdpi.com/1424-8220/22/1/144 |
| work_keys_str_mv | AT alexandergenser anexperimentalurbancasestudywithvariousdatasourcesandamodelfortrafficestimation AT noelhautle anexperimentalurbancasestudywithvariousdatasourcesandamodelfortrafficestimation AT michailmakridis anexperimentalurbancasestudywithvariousdatasourcesandamodelfortrafficestimation AT anastasioskouvelas anexperimentalurbancasestudywithvariousdatasourcesandamodelfortrafficestimation AT alexandergenser experimentalurbancasestudywithvariousdatasourcesandamodelfortrafficestimation AT noelhautle experimentalurbancasestudywithvariousdatasourcesandamodelfortrafficestimation AT michailmakridis experimentalurbancasestudywithvariousdatasourcesandamodelfortrafficestimation AT anastasioskouvelas experimentalurbancasestudywithvariousdatasourcesandamodelfortrafficestimation |