{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:50:14Z","timestamp":1760237414462,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,5,16]],"date-time":"2020-05-16T00:00:00Z","timestamp":1589587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZR2017MF011"],"award-info":[{"award-number":["ZR2017MF011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Natural Science Foundation of China","award":["Grant no. ZR2018MF027"],"award-info":[{"award-number":["Grant no. ZR2018MF027"]}]},{"name":"National Statistical Science Research of China","award":["2017LZ22"],"award-info":[{"award-number":["2017LZ22"]}]},{"name":"Shandong Key Research and Development Project of China","award":["2016GSF120009, 2017GGX50110, GG201809240117"],"award-info":[{"award-number":["2016GSF120009, 2017GGX50110, GG201809240117"]}]},{"name":"Technology Research Program of the Ministry of Public Security, China","award":["2018JSYJB05"],"award-info":[{"award-number":["2018JSYJB05"]}]},{"name":"Jinan Philosophy and Social Science Planning Program of China","award":["JNSK18D15"],"award-info":[{"award-number":["JNSK18D15"]}]},{"name":"Jinan Self-developed Innovation Team Project of &quot;20 Policies for Colleges and Universities&quot; Program, China","award":["2019GXRC022"],"award-info":[{"award-number":["2019GXRC022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Travel time is one of the most critical indexes to describe urban traffic operating states. How to obtain accurate and robust travel time estimates, so as to facilitate to make traffic control decision-making for administrators and trip-planning for travelers, is an urgent issue of wide concern. This paper proposes a reliable estimation method of urban link travel time using multi-sensor data fusion. Utilizing the characteristic analysis of each individual traffic sensor data, we first extract link travel time from license plate recognition data, geomagnetic detector data and floating car data, respectively, and find that their distribution patterns are similar and follow logarithmic normal distribution. Then, a support degree algorithm based on similarity function and a credibility algorithm based on membership function are developed, aiming to overcome the conflicts among multi-sensor traffic data and the uncertainties of single-sensor traffic data. The reliable fusion weights for each type of traffic sensor data are further determined by integrating the corresponding support degree with credibility. A case study was conducted using real-world data from a link of Jingshi Road in Jinan, China and demonstrated that the proposed method can effectively improve the accuracy and reliability of link travel time estimations in urban road systems.<\/jats:p>","DOI":"10.3390\/info11050267","type":"journal-article","created":{"date-parts":[[2020,5,18]],"date-time":"2020-05-18T02:43:42Z","timestamp":1589769822000},"page":"267","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Reliable Estimation of Urban Link Travel Time Using Multi-Sensor Data Fusion"],"prefix":"10.3390","volume":"11","author":[{"given":"Yajuan","family":"Guo","sequence":"first","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Jinan 250061, China"},{"name":"School of Traffic and Logistics Engineering, Shandong Jiaotong University, Jinan 250357, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Licai","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Jinan 250061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"61","DOI":"10.3141\/2526-07","article-title":"Use of Multisensor Data in Reliable Short-Term Travel Time Forecasting for Urban Roads","volume":"2526","author":"Nie","year":"2015","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_2","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":"2010","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1080\/15472450.2013.773225","article-title":"Network-Wide Traffic State Estimation Using Loop Detector and Floating Car Data","volume":"18","author":"Yuan","year":"2014","journal-title":"J. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2273","DOI":"10.1109\/TITS.2014.2314732","article-title":"Mobile Traffic Sensor Routing in Dynamic Transportation Systems","volume":"15","author":"Zhu","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","first-page":"1647","article-title":"Performance Evaluation of Vehicle-Based Mobile Sensor Networks for Traffic Monitoring","volume":"58","author":"Li","year":"2008","journal-title":"IEEE Trans. Veh. Technol."},{"doi-asserted-by":"crossref","unstructured":"Shi, C., Chen, B., Lam, W.H., and Li, Q. (2017). Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks. Sensors, 17.","key":"ref_6","DOI":"10.3390\/s17122822"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1080\/714040818","article-title":"A data fusion algorithm for estimating link travel time","volume":"7","author":"Choi","year":"2002","journal-title":"J. Intell. Transp. Syst. Technol. Plan. Oper."},{"unstructured":"Tarko, A., and Rouphail, N. (1993, January 25\u201328). Travel time data fusion in ADVANCE. Proceedings of the Pacific Rim Trans Tech Conference, Seattle, WA, USA.","key":"ref_8"},{"key":"ref_9","first-page":"351","article-title":"Data-driven aggregative schemes for multisource estimation fusion: A road travel time application","volume":"5434","year":"2004","journal-title":"Defense Secur."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.trc.2015.07.005","article-title":"Real-time traffic state estimation in urban corridors from heterogeneous data","volume":"66","author":"Nantes","year":"2016","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.asoc.2018.06.046","article-title":"Modified Bayesian data fusion model for travel time estimation considering spurious data and traffic conditions","volume":"72","author":"Mil","year":"2018","journal-title":"Appl. Soft Comput."},{"doi-asserted-by":"crossref","unstructured":"El Faouzi, N., and Lefevre, E. (2006, January 19\u201320). Classifiers and distance-based evidential fusion for road travel time estimation. Proceedings of the Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications, Kissimmee, FL, USA.","key":"ref_12","DOI":"10.1117\/12.666745"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.4028\/www.scientific.net\/AMM.488-489.1419","article-title":"A method to urban road travel time estimation through its data fusion based on DS evidential theory","volume":"488","author":"Xia","year":"2014","journal-title":"Appl. Mech. Mater."},{"doi-asserted-by":"crossref","unstructured":"Xiao, F. (2017). A Novel Evidence Theory and Fuzzy Preference Approach-Based Multi-Sensor Data Fusion Technique for Fault Diagnosis. Sensors, 17.","key":"ref_14","DOI":"10.3390\/s17112504"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1007\/s10489-015-0661-2","article-title":"Generalized evidence theory","volume":"43","author":"Deng","year":"2015","journal-title":"Appl. Intell."},{"key":"ref_16","first-page":"236","article-title":"Multisensor data fusion using Elman neural networks","volume":"319","author":"Kolanowski","year":"2018","journal-title":"Appl. Math. Comput."},{"doi-asserted-by":"crossref","unstructured":"Chen, D., Zhang, K., and Liao, T. (2010, January 10\u201312). Practical travel time prediction algorithms based on neural network and data fusion for urban expressway. Proceedings of the 2010 Sixth International Conference on Natural Computation, Yantai, China.","key":"ref_17","DOI":"10.1109\/ICNC.2010.5584403"},{"key":"ref_18","first-page":"1861","article-title":"Real-Time Travel Time Prediction Using Multi-Level k-Nearest Neighbor Algorithm and Data Fusion Method","volume":"2014","author":"Tak","year":"2014","journal-title":"Comput. Civ. Build. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.inffus.2019.06.016","article-title":"Urban big data fusion based on deep learning: An overview","volume":"53","author":"Liu","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"70463","DOI":"10.1109\/ACCESS.2018.2878799","article-title":"Travel Time Prediction: Based on Gated Recurrent Unit Method and Data Fusion","volume":"6","author":"Zhao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.inffus.2011.08.001","article-title":"Multisensor data fusion: A review of the state-of-the-art","volume":"14","author":"Khaleghi","year":"2013","journal-title":"Inf. Fusion"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.trc.2014.02.001","article-title":"Using high-resolution event-based data for traffic modeling and control: An overview","volume":"42","author":"Wu","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.trc.2017.07.006","article-title":"Speed profile estimation using license plate recognition data","volume":"82","author":"Mo","year":"2017","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.trc.2015.06.001","article-title":"Lane-based real-time queue length estimation using license plate recognition data","volume":"57","author":"Zhan","year":"2015","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1061\/(ASCE)0733-947X(2006)132:8(609)","article-title":"Overtaking Rule Method for the Cleaning of Matched License-Plate Data","volume":"132","author":"Robinson","year":"2006","journal-title":"J. Transp. Eng."},{"key":"ref_26","first-page":"1","article-title":"Measurement of urban traffic congestion","volume":"156","author":"Rothrock","year":"1957","journal-title":"Highw. Res. Board Bull."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/0041-1647(76)90055-1","article-title":"Link capacity functions: A review","volume":"10","author":"Branston","year":"1976","journal-title":"Transp. Res."},{"unstructured":"Bureau of Public Roads (1964). Traffic Assignment Manual.","key":"ref_28"},{"key":"ref_29","first-page":"124","article-title":"Modified BPR functions for travel time estimation of urban arterial road segment","volume":"45","author":"Jiang","year":"2010","journal-title":"J. Southwest Jiaotong Univ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.trc.2013.02.002","article-title":"Path inference from sparse floating car data for urban networks","volume":"30","author":"Rahmani","year":"2013","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_31","first-page":"90","article-title":"Fuzzy distribution fitting for law of traffic accidents based on MATLAB","volume":"39","author":"Wu","year":"2009","journal-title":"Math. Pract. Theory"},{"key":"ref_32","first-page":"107","article-title":"Algorithm research of auto-detecting the travel delay information with vehicle license plate automatic recognition","volume":"6","author":"Song","year":"2008","journal-title":"J. Transp. Eng. 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