{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T21:08:20Z","timestamp":1761340100392},"reference-count":28,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of china","doi-asserted-by":"crossref","award":["71621001","71671014","71631007"],"award-info":[{"award-number":["71621001","71671014","71631007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key R&D Program of china","award":["2018YFB1601200"],"award-info":[{"award-number":["2018YFB1601200"]}]},{"name":"Central Public-interest Scientific Institution Basal Research Fund","award":["20196104"],"award-info":[{"award-number":["20196104"]}]},{"name":"Strategic planning policy of the Ministry of transport","award":["2019-17-4"],"award-info":[{"award-number":["2019-17-4"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Model. Simul. Sci. Comput."],"published-print":{"date-parts":[[2020,6]]},"abstract":"<jats:p> Driving behavior is heterogeneous for various drivers due to the different influencing factors as reaction time, gender, driving years and so on. Some existing works tried to reproduce some of the complex characteristics of real traffic flow by taking into account the heterogeneous driving behavior, and the drivers are generally divided into two classes (including aggressive drivers and careful drivers) or three classes (including aggressive drivers, normal drivers and careful drivers). Nevertheless, the classification approaches have not been verified, and the rationality of the classifications has not been confirmed as well. In this study, the trajectory data of drivers is extracted from the NGSIM datasets. By combining the K-Means method and Silhouette measure index, the drivers are classified into four clusters (named as clusters A, B, C and D, respectively) in accordance with the acceleration and time headway. The two-dimensional approach is applied to analyze the characteristics of different clusters. Here, one dimension consists of \u201cCautious\u201d and \u201cAggressive\u201d behaviors in terms of velocity and acceleration, and the other dimension consists of \u201cSensitive\u201d and \u201cInsensitive\u201d behaviors in terms of reaction time. Finally, the fuel consumption and emissions for different clusters are calculated by using the VT-Micro model. A surprising result indicates that overly \u201ccautious\u201d and \u201csensitive\u201d behaviors may result in more fuel consumption and emissions. Therefore, it is necessary to find the balance between the driving characteristics. <\/jats:p>","DOI":"10.1142\/s1793962320500233","type":"journal-article","created":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T13:59:04Z","timestamp":1592575144000},"page":"2050023","source":"Crossref","is-referenced-by-count":7,"title":["Capturing driving behavior Heterogeneity based on trajectory data"],"prefix":"10.1142","volume":"11","author":[{"given":"Dong-Fan","family":"Xie","sequence":"first","affiliation":[{"name":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing, P. R. China"},{"name":"Key Laboratory of Transport Industry of Big Data Application, Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing, P. R. China"}]},{"given":"Tai-Lang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Transport Industry of Big Data Application, Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing, P. R. China"}]},{"given":"Qian","family":"Li","sequence":"additional","affiliation":[{"name":"China Academy of Transportation Science, Beijing, P. R. China"}]}],"member":"219","published-online":{"date-parts":[[2020,6,6]]},"reference":[{"key":"S1793962320500233BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/S0370-1573(99)00117-9"},{"key":"S1793962320500233BIB002","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.73.1067"},{"key":"S1793962320500233BIB003","doi-asserted-by":"publisher","DOI":"10.1016\/j.trf.2017.06.009"},{"key":"S1793962320500233BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/j.trf.2016.06.017"},{"key":"S1793962320500233BIB005","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2009.07.009"},{"key":"S1793962320500233BIB006","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2013.08.003"},{"key":"S1793962320500233BIB007","doi-asserted-by":"publisher","DOI":"10.3141\/2188-10"},{"key":"S1793962320500233BIB008","doi-asserted-by":"publisher","DOI":"10.1177\/0361198105193400102"},{"key":"S1793962320500233BIB009","doi-asserted-by":"publisher","DOI":"10.3141\/1999-11"},{"key":"S1793962320500233BIB010","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2010.05.006"},{"key":"S1793962320500233BIB011","doi-asserted-by":"publisher","DOI":"10.3141\/2320-09"},{"key":"S1793962320500233BIB012","doi-asserted-by":"publisher","DOI":"10.1016\/j.amar.2014.10.001"},{"key":"S1793962320500233BIB013","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2014.02.021"},{"key":"S1793962320500233BIB014","doi-asserted-by":"publisher","DOI":"10.1177\/0361198106196500113"},{"key":"S1793962320500233BIB015","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-47641-2_67"},{"key":"S1793962320500233BIB016","doi-asserted-by":"publisher","DOI":"10.3141\/2249-09"},{"key":"S1793962320500233BIB017","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2010.2050141"},{"key":"S1793962320500233BIB018","doi-asserted-by":"publisher","DOI":"10.1088\/1674-1056\/19\/2\/020513"},{"key":"S1793962320500233BIB019","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.74.016123"},{"key":"S1793962320500233BIB020","first-page":"997","volume-title":"IEEE Int. Conf. Industrial Technology","author":"Brombacher P."},{"key":"S1793962320500233BIB021","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2014.12.009"},{"key":"S1793962320500233BIB022","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.07.002"},{"key":"S1793962320500233BIB023","doi-asserted-by":"publisher","DOI":"10.1080\/21680566.2020.1715310"},{"key":"S1793962320500233BIB024","doi-asserted-by":"publisher","DOI":"10.1145\/331499.331504"},{"key":"S1793962320500233BIB025","doi-asserted-by":"publisher","DOI":"10.1016\/0377-0427(87)90125-7"},{"key":"S1793962320500233BIB026","volume-title":"Data Mining: Concepts and Techniques","author":"Han J.","year":"2006","edition":"2"},{"issue":"5","key":"S1793962320500233BIB027","first-page":"369","volume":"24","author":"Jawarkar N. P.","year":"2007","journal-title":"IETE Technical Rev."},{"key":"S1793962320500233BIB029","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2002)128:2(182)"}],"container-title":["International Journal of Modeling, Simulation, and Scientific Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S1793962320500233","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T13:59:53Z","timestamp":1592575193000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S1793962320500233"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6]]},"references-count":28,"journal-issue":{"issue":"03","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["10.1142\/S1793962320500233"],"URL":"https:\/\/doi.org\/10.1142\/s1793962320500233","relation":{},"ISSN":["1793-9623","1793-9615"],"issn-type":[{"value":"1793-9623","type":"print"},{"value":"1793-9615","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6]]}}}