{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T06:24:11Z","timestamp":1784615051990,"version":"3.55.0"},"reference-count":33,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,26]],"date-time":"2021-12-26T00:00:00Z","timestamp":1640476800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.3390\/s22010144","type":"journal-article","created":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T01:06:54Z","timestamp":1640567214000},"page":"144","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation"],"prefix":"10.3390","volume":"22","author":[{"given":"Alexander","family":"Genser","sequence":"first","affiliation":[{"name":"Department of Civil, Environmental and Geomatic Engineering, Institute for Transport Planning and Systems, ETH Zurich, CH-8093 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noel","family":"Hautle","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Geomatic Engineering, Institute for Transport Planning and Systems, ETH Zurich, CH-8093 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7462-4674","authenticated-orcid":false,"given":"Michail","family":"Makridis","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Geomatic Engineering, Institute for Transport Planning and Systems, ETH Zurich, CH-8093 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4571-2530","authenticated-orcid":false,"given":"Anastasios","family":"Kouvelas","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Geomatic Engineering, Institute for Transport Planning and Systems, ETH Zurich, CH-8093 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8498054","DOI":"10.1155\/2018\/8498054","article-title":"Emerging Information and Communication Technologies for Traffic Estimation and Control","volume":"2018","author":"Kouvelas","year":"2018","journal-title":"J. Adv. Transp."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1080\/15472450.2012.696449","article-title":"Fusing a Bluetooth Traffic Monitoring System With Loop Detector Data for Improved Freeway Traffic Speed Estimation","volume":"17","author":"Bachmann","year":"2013","journal-title":"J. Intell. Transp. Syst."},{"key":"ref_3","unstructured":"Zheng, F. (2011). Modelling Urban Travel Times. [Ph.D. Thesis, TRAIL Research School]."},{"key":"ref_4","unstructured":"Genser, A., and Kouvelas, A. (2020, January 12\u201316). Optimum route guidance in multi-region networks. A linear approach. Proceedings of the 99th Annual Meeting of the Transportation Research Board, Washington, DC, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103485","DOI":"10.1016\/j.trc.2021.103485","article-title":"Dynamic optimal congestion pricing in multi-region urban networks by application of a Multi-Layer-Neural network","volume":"134","author":"Genser","year":"2022","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Genser, A., Amb\u00fchl, L., Yang, K., Menendez, M., and Kouvelas, A. (2020, January 20\u201323). Time-to-Green predictions: A framework to enhance SPaT messages using machine learning. Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece.","DOI":"10.1109\/ITSC45102.2020.9294548"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chavoshi, K., Genser, A., and Kouvelas, A. (2021). A Pairing Algorithm for Conflict-Free Crossings of Automated Vehicles at Lightless Intersections. Electronics, 10.","DOI":"10.3390\/electronics10141702"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.trb.2013.03.006","article-title":"Experienced travel time prediction for congested freeways","volume":"53","author":"Yildirimoglu","year":"2013","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"60","DOI":"10.3141\/2160-07","article-title":"Data Collection of Freeway Travel Time Ground Truth with Bluetooth Sensors","volume":"2160","author":"Haghani","year":"2010","journal-title":"Transp. Res. Rec."},{"key":"ref_10","unstructured":"Sharifi, E., Hamedi, M., Haghani, A., and Sadrsadat, H. (2011, January 16\u201320). Analysis of Vehicle Detection Rate for Bluetooth Traffic Sensors: A Case Study in Maryland and Delaware. Proceedings of the 18th ITS World Congress, Orlando, FL, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/s40534-016-0101-y","article-title":"Bluetooth as a traffic sensor for stream travel time estimation under Bogazici Bosporus conditions in Turkey","volume":"24","author":"Erkan","year":"2016","journal-title":"J. Mod. Transp."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3141\/2175-03","article-title":"Travel Time Forecasting and Dynamic Origin-Destination Estimation for Freeways Based on Bluetooth Traffic Monitoring","volume":"2175","author":"Montero","year":"2010","journal-title":"Transp. Res. Rec."},{"key":"ref_13","unstructured":"Barcel\u00f3, J., Montero, L., Bullejos, M., Serch, O., and Carmona, C. (2012, January 22\u201326). A Kalman Filter Approach for the Estimation of Time Dependent OD Matrices Exploiting Bluetooth Traffic Data Collection. Proceedings of the 91st Transportation Research Board 2012 Annual Meeting, Washington, DC, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1111\/anzs.12294","article-title":"Modelling the travel time of transit vehicles in real-time through a GTFS-based road network using GPS vehicle locations","volume":"62","author":"Elliott","year":"2020","journal-title":"Aust. N. Z. J. Stat."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.trc.2015.08.010","article-title":"Assessment of antenna characteristic effects on pedestrian and cyclists travel-time estimation based on Bluetooth and WiFi MAC addresses","volume":"60","author":"Abedi","year":"2015","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1002\/atr.180","article-title":"Practical approach for travel time estimation from point traffic detector data","volume":"47","author":"Shen","year":"2013","journal-title":"J. Adv. Transp."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.trb.2007.08.005","article-title":"Travel time estimation on a freeway using Discrete Time Markov Chains","volume":"42","author":"Yeon","year":"2008","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1111\/j.1467-8667.2010.00697.x","article-title":"Fusing Loop Detector and Probe Vehicle Data to Estimate Travel Time Statistics on Signalized Urban Networks","volume":"26","author":"Bhaskar","year":"2011","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bock, J., Krajewski, R., Moers, T., Runde, S., Vater, L., and Eckstein, L. (November, January 19). The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA.","DOI":"10.1109\/IV47402.2020.9304839"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3141\/2046-01","article-title":"Using Travel Time Reliability Measures to Improve Regional Transportation Planning and Operations","volume":"2046","author":"Lyman","year":"2008","journal-title":"Transp. Res. Rec."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1007\/s40534-019-00195-6","article-title":"Data analytics approach for travel time reliability pattern analysis and prediction","volume":"27","author":"Chen","year":"2019","journal-title":"J. Mod. Transp."},{"key":"ref_22","unstructured":"Benesty, J., Chen, J., Huang, Y., and Cohen, I. (2009). Noise Reduction in Speech Processing, Springer."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/j.ijforecast.2015.12.003","article-title":"A new metric of absolute percentage error for intermittent demand forecasts","volume":"32","author":"Kim","year":"2016","journal-title":"Int. J. Forecast."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/0041-1647(78)90107-7","article-title":"The estimation of saturation flow, effective green time and passenger car equivalents at traffic signals by multiple linear regression","volume":"12","author":"Branston","year":"1978","journal-title":"Transp. Res."},{"key":"ref_25","unstructured":"TomTom International, BV (2021, July 04). Zurich Traffic Report. Available online: https:\/\/www.tomtom.com\/en_gb\/traffic-index\/zurich-traffic."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Silva, S.M., and Jung, C.R. (2018, January 8\u201314). License plate detection and recognition in unconstrained scenarios. Proceedings of the 2018 European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01258-8_36"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s00138-013-0570-5","article-title":"Thermal cameras and applications: A survey","volume":"25","author":"Gade","year":"2014","journal-title":"Mach. Vis. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5142732","DOI":"10.1155\/2017\/5142732","article-title":"Automatic traffic data collection under varying lighting and temperature conditions in multimodal environments: Thermal versus visible spectrum video-based systems","volume":"2017","author":"Fu","year":"2017","journal-title":"J. Adv. Transp."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ding, F., Chen, X., He, S., Shou, G., Zhang, Z., and Zhou, Y. (2019). Evaluation of a wi-fi signal based system for freeway traffic states monitoring: An exploratory field test. Sensors, 19.","DOI":"10.3390\/s19020409"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.trc.2009.04.003","article-title":"Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensors","volume":"17","author":"Kwong","year":"2009","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bakhtan, M.A.H., Abdullah, M., and Rahman, A.A. (2016, January 16\u201317). A review on License Plate Recognition system algorithms. Proceedings of the 2016 International Conference on Information and Communication Technology (ICICTM), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICICTM.2016.7890782"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_33","unstructured":"Google Developers (2021, July 23). Distance Matrix API\u2014Documentation. Available online: https:\/\/developers.google.com\/maps\/documentation\/distance-matrix\/overview."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/144\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:53:46Z","timestamp":1760169226000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/144"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,26]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22010144"],"URL":"https:\/\/doi.org\/10.3390\/s22010144","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,26]]}}}