{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:28:31Z","timestamp":1740148111803,"version":"3.37.3"},"reference-count":22,"publisher":"Wiley","license":[{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"funder":[{"name":"National Social Science Foundation of China","award":["11&ZD160"],"award-info":[{"award-number":["11&ZD160"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2014]]},"abstract":"<jats:p>A self-organizing feature map (SOM) was used to represent vehicle-following and to analyze the heterogeneities in vehicle-following behavior. The SOM was constructed in such a way that the prototype vectors represented vehicle-following stimuli (the follower\u2019s velocity, relative velocity, and gap) while the output signals represented the response (the follower\u2019s acceleration). Vehicle trajectories collected at a northbound segment of Interstate 80 Freeway at Emeryville, CA, were used to train the SOM. The trajectory information of two selected pairs of passenger cars was then fed into the trained SOM to identify similar stimuli experienced by the followers. The observed responses, when the stimuli were classified by the SOM into the same category, were compared to discover the interdriver heterogeneity. The acceleration profile of another passenger car was analyzed in the same fashion to observe the interdriver heterogeneity. The distribution of responses derived from data sets of car-following-car and car-following-truck, respectively, was compared to ascertain inter-vehicle-type heterogeneity.<\/jats:p>","DOI":"10.1155\/2014\/561036","type":"journal-article","created":{"date-parts":[[2014,11,5]],"date-time":"2014-11-05T16:02:57Z","timestamp":1415203377000},"page":"1-11","source":"Crossref","is-referenced-by-count":3,"title":["Analysis of Vehicle-Following Heterogeneity Using Self-Organizing Feature Maps"],"prefix":"10.1155","volume":"2014","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7223-5197","authenticated-orcid":true,"given":"Jie","family":"Yang","sequence":"first","affiliation":[{"name":"Development Research Center of Transportation Governed by Law, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruey Long","family":"Cheu","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, The University of Texas at El Paso, El Paso, TX 79968, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiucheng","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alicia","family":"Romo","sequence":"additional","affiliation":[{"name":"Turner-Fairbanks Highway Research Center, Federal Highway Administration, McLean, VA 22101, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/S1369-8478(00)00005-X"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2005.853705"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1287\/opre.9.4.545"},{"year":"2002","key":"4"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.3141\/2188-10"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2010.05.006"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1016\/0191-2615(81)90037-0"},{"issue":"2","key":"11","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1103\/PhysRevE.62.1805","volume":"62","year":"2000","journal-title":"Physical Review E"},{"year":"2009","key":"13"},{"issue":"1876","key":"14","first-page":"62","year":"2004","journal-title":"Transportation Research Record"},{"issue":"1876","key":"15","first-page":"90","year":"2004","journal-title":"Transportation Research Record"},{"issue":"1934","key":"16","first-page":"13","year":"2005","journal-title":"Transportation Research Record"},{"issue":"1934","key":"17","first-page":"53","year":"2005","journal-title":"Transportation Research Record"},{"issue":"1","key":"18","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1177\/0361198106196500113","volume":"1965","year":"2006","journal-title":"Transportation Research Record"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.3141\/2088-16"},{"year":"1987","key":"20"},{"year":"1998","key":"21"},{"year":"2002","key":"22"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1111\/1467-8667.00307"},{"year":"2005","key":"26"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2010.12.007"},{"year":"2004","key":"28"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2014\/561036.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2014\/561036.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2014\/561036.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,16]],"date-time":"2019-08-16T22:43:00Z","timestamp":1565995380000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cin\/2014\/561036\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"references-count":22,"alternative-id":["561036","561036"],"URL":"https:\/\/doi.org\/10.1155\/2014\/561036","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"type":"print","value":"1687-5265"},{"type":"electronic","value":"1687-5273"}],"subject":[],"published":{"date-parts":[[2014]]}}}