{"id":91,"date":"2017-08-14T06:00:07","date_gmt":"2017-08-14T06:00:07","guid":{"rendered":"http:\/\/216.24.255.190\/teamsite\/?page_id=91"},"modified":"2020-10-27T06:00:51","modified_gmt":"2020-10-27T06:00:51","slug":"chengshifen","status":"publish","type":"page","link":"http:\/\/pervasivegis.group\/teamsite\/members\/chengshifen\/","title":{"rendered":"\u7a0b\u8bd7\u594b"},"content":{"rendered":"<p><a><img loading=\"lazy\" class=\"size-full wp-image-56 aligncenter\" src=\"http:\/\/pervasivegis.group\/teamsite\/wp-content\/uploads\/2020\/08\/chengshifen.jpg\" alt=\"\" width=\"165\" height=\"210\"><\/a><\/p>\n<p style=\"font-size: 80%; text-align: center;\">\u25a0&nbsp; &nbsp;\u7535\u5b50\u90ae\u7bb1\uff1achengsf@lreis.ac.cn<br \/>\n\u25a0&nbsp; &nbsp;\u5916\u90e8\u94fe\u63a5\uff1a<a href=\"https:\/\/scholar.google.com.hk\/citations?user=DsbOLjEAAAAJ\">Google Scholar<\/a><\/p>\n<p style=\"text-indent: 2em;\">\u7a0b\u8bd7\u594b\uff0c\u7537\uff0c1990\u5e74\u751f\uff0c\u6e56\u5317\u6b66\u6c49\u4eba\uff0c\u535a\u58eb\uff0c\u4e2d\u56fd\u79d1\u5b66\u9662\u5730\u7406\u79d1\u5b66\u4e0e\u8d44\u6e90\u7814\u7a76\u6240\u535a\u58eb\u540e\u3001\u7279\u522b\u7814\u7a76\u52a9\u7406\u3002\u5df2\u53d1\u8868\u5b66\u672f\u8bba\u658722\u7bc7\uff08SCI\u8bba\u658716\u7bc7\uff0c\u603b\u5f71\u54cd\u56e0\u5b5066.606\uff0cJCR\u4e00\u533aSCI\u8bba\u658710\u7bc7\uff0c\u5176\u4e2d\u4e2d\u79d1\u96621\u533aTop\u8bba\u65877\u7bc7\uff09\uff0c\u5176\u4e2d\u7b2c\u4e00\u4f5c\u8005SCI\u8bba\u65878\u7bc7\uff08\u603b\u5f71\u54cd\u56e0\u5b5034.584\uff0cJCR\u4e00\u533aSCI\u8bba\u65876\u7bc7\uff0c\u5176\u4e2d\u4e2d\u79d1\u96621\u533aTop\u8bba\u65874\u7bc7\uff09\uff0c\u901a\u8baf\u4f5c\u8005SCI\u8bba\u65871\u7bc7\uff0c\u7b2c\u4e00\u4f5c\u8005EI\u8bba\u65871\u7bc7\uff0c\u53e6\u6709\u90e8\u5206\u8bba\u6587\u5728\u5ba1\u7a3f\u4e2d\u3002\u5df2\u83b7\u5f97\u53d1\u660e\u4e13\u5229\u6388\u67434\u9879\uff0c\u53e6\u67091\u9879\u53d1\u660e\u4e13\u5229\u5728\u5b9e\u5ba1\u4e2d\u3002\u653b\u8bfb\u535a\u58eb\u5b66\u4f4d\u671f\u95f4\u83b7\u5f972017\u5e74\u4e2d\u56fd\u5730\u7406\u4fe1\u606f\u79d1\u5b66\u7406\u8bba\u4e0e\u65b9\u6cd5\u5b66\u672f\u5e74\u4f1a\u4f18\u79c0\u5b66\u751f\u8bba\u6587\u7279\u7b49\u5956\uff0c2018\u5e74\u4e2d\u56fd\u79d1\u5b66\u9662\u5927\u5b66\u4e09\u597d\u5b66\u751f\uff0c2019\u5e74\u4e2d\u56fd\u79d1\u5b66\u9662\u5927\u5b66\u4e09\u597d\u5b66\u751f\u6807\u5175\uff0c2019\u5e74\u6731\u674e\u6708\u534e\u4e13\u9879\u5956\u5b66\u91d1\uff0c2019\u5e74\u535a\u58eb\u7814\u7a76\u751f\u56fd\u5bb6\u5956\u5b66\u91d1\uff0c2020\u5e74\u5730\u7406\u6240\u4f18\u79c0\u6bd5\u4e1a\u751f\uff0c2020\u5e74\u56fd\u79d1\u5927\u4f18\u79c0\u6bd5\u4e1a\u751f\u548c\u9662\u957f\u5956\u3002<\/p>\n<hr>\n<h3 style=\"color: #0c6eb6;\">\u7814\u7a76\u5174\u8da3<\/h3>\n<ul>\n<li>\u65f6\u7a7a\u6570\u636e\u6316\u6398<\/li>\n<\/ul>\n<hr>\n<h3 style=\"color: #0c6eb6;\">\u5de5\u4f5c\u7ecf\u5386<\/h3>\n<ul>\n<li>2020\u5e747\u6708-\u81f3\u4eca\uff0c\u4e2d\u56fd\u79d1\u5b66\u9662\u5730\u7406\u79d1\u5b66\u4e0e\u8d44\u6e90\u7814\u7a76\u6240\uff0c\u535a\u58eb\u540e\u3001\u7279\u522b\u7814\u7a76\u52a9\u7406\uff0c\u5408\u4f5c\u5bfc\u5e08\uff1a\u9646\u950b<\/li>\n<\/ul>\n<hr>\n<h3 style=\"color: #0c6eb6;\">\u5b66\u4e60\u7ecf\u5386<\/h3>\n<ul>\n<li>2016\u5e749\u6708-2020\u5e746\u6708\uff0c\u4e2d\u56fd\u79d1\u5b66\u9662\u5730\u7406\u79d1\u5b66\u4e0e\u8d44\u6e90\u7814\u7a76\u6240\uff0c\u5730\u56fe\u5b66\u4e0e\u5730\u7406\u4fe1\u606f\u7cfb\u7edf\uff0c\u535a\u58eb\uff0c\u5bfc\u5e08\uff1a\u9646\u950b<\/li>\n<li>2013\u5e749\u6708-2015\u5e746\u6708\uff0c\u4e2d\u56fd\u5730\u8d28\u5927\u5b66\uff08\u6b66\u6c49\uff09\uff0c\u8f6f\u4ef6\u5de5\u7a0b\uff0c\u7855\u58eb\uff0c\u5bfc\u5e08\uff1a\u4e07\u6ce2<\/li>\n<li>2009\u5e749\u6708-2013\u5e746\u6708\uff0c\u957f\u6c5f\u5927\u5b66\uff0c\u5730\u7406\u4fe1\u606f\u7cfb\u7edf\uff0c\u5b66\u58eb<\/li>\n<\/ul>\n<hr>\n<h3 style=\"color: #0c6eb6;\">\u53d1\u8868\u8bba\u8457<\/h3>\n<ol>\n<li>Shifen Cheng, Feng Lu*, Peng Peng. 2020. Short-term traffic forecasting by mining the non-stationarity of spatiotemporal patterns. IEEE Transactions on Intelligent Transportation Systems. doi:10.1109\/TITS.2020.2991781. (SCI, IF = 5.744)<\/li>\n<li>Shifen Cheng, Peng Peng, Feng Lu*. 2020. A lightweight ensemble spatiotemporal interpolation model for geospatial data. International Journal of Geographical Information Science. doi: 10.1080\/13658816.2020.1725016. (SCI, IF = 3.545)<\/li>\n<li>Shifen Cheng, Feng Lu*, Peng Peng. 2020. A high-resolution emissions inventory and its spatiotemporal pattern variations for heavy-duty diesel trucks in Beijing, China. Journal of Cleaner Production, 250, 119445. (SCI, IF = 6.395)<\/li>\n<li>Shifen Cheng, Beibei Zhang, Peng Peng, Zhenzhen Yang, Feng Lu*. 2020. Spatiotemporal evolution pattern detection for heavy-duty diesel truck emissions using trajectory mining: A case study of Tianjin, China. Journal of Cleaner Production, 244, 118654. (SCI, IF = 6.395)<\/li>\n<li>Shifen Cheng, Feng Lu*, Peng Peng, Sheng Wu. 2019. Multi-task and multi-view learning based on particle swarm optimization for short-term traffic forecasting. Knowledge-Based Systems, 180: 116-132. (SCI, IF = 5.101)<\/li>\n<li>Shifen Cheng, Feng Lu*, Peng Peng, Sheng Wu. 2018. Short-term traffic forecasting: an adaptive ST-KNN model that considers spatial heterogeneity. Computers, Environment and Urban Systems, 71: 186-198. (SSCI, IF = 3.724)<\/li>\n<li>Shifen Cheng, Feng Lu*, Peng Peng, Sheng Wu. 2018. A spatiotemporal multi-view-based learning method for short-term traffic forecasting. ISPRS International Journal of Geo-Information 7, 6: 218. (SCI, IF = 1.840)<\/li>\n<li>Shifen Cheng, Feng Lu*. 2017. A two-step method for missing spatio-temporal data reconstruction. ISPRS International Journal of Geo-Information, 6, 187. (SCI, IF = 1.840)<\/li>\n<li>Shifen Cheng, Feng Lu*. 2018. Short-Term Traffic Forecasting: A dynamic ST-KNN model considering spatial heterogeneity and temporal non-stationarity. Proceedings of the Workshops of the EDBT\/ICDT 2018 Joint Conference (EDBT\/ICDT 2018), Vienna, Austria, 133-140. (EI)<\/li>\n<li>Beibei Zhang, Sheng Wu, Shifen Cheng*, Feng Lu, Peng Peng. 2019. Spatial Characteristics and factor analysis of pollution emission from heavy-duty diesel trucks in the Beijing\u2013Tianjin\u2013Hebei region, China. International Journal of Environmental Research and Public Health 16, 24. doi:10.3390\/ijerph16244973. (SCI, IF = 2.468)<\/li>\n<li>Peng, Peng, Shifen Cheng, Feng Lu*. 2020. Characterizing the global liquefied petroleum gas trading community using mass vessel trajectory data. Journal of Cleaner Production, 252, 119883. (SCI, IF = 6.395)<\/li>\n<li>Peng Peng, Yu Yang, Shifen Cheng, Feng Lu*, Zimu Yuan. 2019. Hub-and-spoke structure: characterizing the global crude oil transport network with mass vessel trajectories. Energy, 168: 966-974. (SCI, IF = 5.537)<\/li>\n<li>Peng Peng, Jessie P.H. Poon, Yu Yang*, Feng Lu*, Shifen Cheng. 2019. Global oil traffic network and diffusion of influence among ports using real time data. Energy, 172, 333-342. (SCI, IF = 5.537)<\/li>\n<li>Peng Peng, Yu Yang, Feng Lu*, Shifen Cheng, Naixia Mou, Ren Yang. 2018. Modelling the competitiveness of the ports along the Maritime Silk Road with big geo-related data. Transportation Research Part A, 118: 852-867. (SCI, IF = 3.693)<\/li>\n<li>Peng Peng, Shifen Cheng, Jinhai Chen, Mengdi Liao, Lin Wu, Xiliang Liu, Feng Lu*. 2018. A fine-grained perspective on the robustness of global cargo ship transportation networks. Journal of Geographical Sciences 28, 7: 881-899. (SCI, IF = 2.347)<\/li>\n<li>Feng Lu, Kang Liu, Yingying Duan, Shifen Cheng, Fei Du. 2018. Modelling the heterogeneous traffic correlation for city road networks with community detection. Physica A: Statistical Mechanics and Its Applications, 501, 227-237. (SCI, IF = 2.500)<\/li>\n<li>Jie Chen, Tao Pei, Shih-Lung Shaw, Feng Lu, Mingxiao Li, Shifen Cheng, Xiliang Liu, Hengcai Zhang. 2018. Fine-grained prediction of urban population using mobile phone location data. International Journal of Geographical Information Science, 32, 1770-1786. (SCI, IF = 3.545)<\/li>\n<li>Xiliang Liu, Li Yu, Kang Liu, Peng Peng, Shifen Cheng, Mengdi Liao, Feng Lu*. 2018. ST-PF: Spatio-temporal particle filter for floating car data pre-processing. Information Fusion and Intelligent Geographic Information Systems (IF&amp;IGIS\u201917), Springer, 197-211. (EI)<\/li>\n<li>\u5f6d\u6f8e\uff0c\u7a0b\u8bd7\u594b\uff0c\u9648\u95ea\u95ea\uff0c\u9646\u950b*. 2020. \u5168\u7403\u6db2\u5316\u77f3\u6cb9\u6c14\u8fd0\u8f93\u7f51\u7edc\u8d38\u6613\u793e\u533a\u7279\u5f81\u53ca\u5176\u6f14\u5316\u5206\u6790. \u81ea\u7136\u8d44\u6e90\u5b66\u62a5, \u5df2\u63a5\u53d7\u5f85\u520a.<\/li>\n<li>\u5f6d\u6f8e\uff0c\u7a0b\u8bd7\u594b\uff0c\u5218\u5e0c\u4eae\uff0c\u6885\u5f3a\uff0c\u9646\u950b*. 2017. \u5168\u7403\u6d77\u6d0b\u8d27\u8fd0\u7f51\u7edc\u5065\u58ee\u6027\u8bc4\u4f30. \u5730\u7406\u5b66\u62a5 72, 12: 2241-2251.<\/li>\n<li>\u674e\u660e\u6653\uff0c\u5f20\u6052\u624d*\uff0c\u4ec7\u57f9\u5143\uff0c\u9648\u6d01\uff0c\u7a0b\u8bd7\u594b\uff0c\u9646\u950b. 2018. \u4e00\u79cd\u57fa\u4e8eFuzzy-LSTM\u6a21\u578b\u7684\u79fb\u52a8\u5bf9\u8c61\u8f68\u8ff9\u9884\u6d4b\u7b97\u6cd5. \u6d4b\u7ed8\u5b66\u62a5 47, 12: 1660-1669.<\/li>\n<li>\u5218\u5e0c\u4eae\uff0c\u7a0b\u8bd7\u594b\uff0c\u4f59\u4e3d\uff0c\u5218\u5eb7\uff0c\u9646\u950b. 2016. \u67b6\u8d77GIS\u4e0e\u8ba1\u7b97\u673a\u79d1\u5b66\u7684\u6865\u6881:ACM SIGSPATIAL 2015\u4f1a\u8bae\u7efc\u8ff0. \u5730\u7403\u4fe1\u606f\u79d1\u5b66\u5b66\u62a5 18. 11: 1448-1455.<\/li>\n<li>\u4f59\u4e3d\uff0c\u9646\u950b\uff0c\u5218\u5e0c\u4eae\uff0c\u7a0b\u8bd7\u594b\uff0c\u5f20\u96ea\u82f1. 2016. \u7a00\u758f\u5730\u7406\u5b9e\u4f53\u5173\u7cfb\u7684\u5173\u952e\u8bcd\u63d0\u53d6\u65b9\u6cd5. \u5730\u7403\u4fe1\u606f\u79d1\u5b66\u5b66\u62a5 18, 11:1465-1475.<\/li>\n<\/ol>\n<hr>\n<h3 style=\"color: #0c6eb6;\">\u79d1\u7814\u9879\u76ee<\/h3>\n<ol>\n<li>\u4e2d\u56fd\u79d1\u5b66\u9662\u7279\u522b\u7814\u7a76\u52a9\u7406\u9879\u76ee, \u5730\u9762\u4ea4\u901a\u6c61\u67d3\u7cbe\u7ec6\u65f6\u7a7a\u5c3a\u5ea6\u5927\u6570\u636e\u76d1\u6d4b\u65b9\u6cd5\u53ca\u5176\u5e94\u7528<br \/>\n, 2021.01-2023.12, \u5728\u7814, \u4e3b\u6301.<\/li>\n<li>\u4e2d\u56fd\u535a\u58eb\u540e\u79d1\u5b66\u57fa\u91d1\u9762\u4e0a\u9879\u76ee\u7b2c68\u6279, 2021.01-2023.6, \u5728\u7814, \u4e3b\u6301.<\/li>\n<\/ol>\n<hr>\n<h3 style=\"color: #0c6eb6;\">\u7533\u8bf7\u4e13\u5229<\/h3>\n<ol>\n<li>\u9646\u950b, \u7a0b\u8bd7\u594b, \u5f6d\u6f8e. \u4e00\u79cd\u57fa\u4e8e\u591a\u4efb\u52a1\u591a\u89c6\u56fe\u5b66\u4e60\u6a21\u578b\u7684\u77ed\u65f6\u4ea4\u901a\u9884\u6d4b\u65b9\u6cd5.<\/li>\n<li>\u9646\u950b, \u7a0b\u8bd7\u594b, \u5f6d\u6f8e. \u4e00\u79cd\u8f7b\u91cf\u7ea7\u7684\u7f3a\u5931\u65f6\u7a7a\u6570\u636e\u7684\u91cd\u6784\u65b9\u6cd5.<\/li>\n<li>\u9646\u950b, \u7a0b\u8bd7\u594b, \u5f6d\u6f8e. \u4e00\u79cd\u57fa\u4e8e\u52a8\u6001STKNN\u6a21\u578b\u7684\u77ed\u65f6\u4ea4\u901a\u9884\u6d4b\u65b9\u6cd5.<\/li>\n<li>\u7a0b\u8bd7\u594b, \u9646\u950b, \u5f6d\u6f8e. \u4e00\u79cd\u8f66\u8f86\u6392\u653e\u6e05\u5355\u7684\u6784\u5efa\u65b9\u6cd5.<\/li>\n<\/ol>\n<hr>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u25a0&nbsp; &nbsp;\u7535\u5b50\u90ae\u7bb1\uff1achengsf@lreis.ac.cn \u25a0&nbsp; &nbsp;\u5916\u90e8 <a href=\"http:\/\/pervasivegis.group\/teamsite\/members\/chengshifen\/\" rel=\"nofollow\"><span class=\"sr-only\">Read more about \u7a0b\u8bd7\u594b<\/span>[&hellip;]<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":857,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"http:\/\/pervasivegis.group\/teamsite\/wp-json\/wp\/v2\/pages\/91"}],"collection":[{"href":"http:\/\/pervasivegis.group\/teamsite\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/pervasivegis.group\/teamsite\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/pervasivegis.group\/teamsite\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/pervasivegis.group\/teamsite\/wp-json\/wp\/v2\/comments?post=91"}],"version-history":[{"count":14,"href":"http:\/\/pervasivegis.group\/teamsite\/wp-json\/wp\/v2\/pages\/91\/revisions"}],"predecessor-version":[{"id":1501,"href":"http:\/\/pervasivegis.group\/teamsite\/wp-json\/wp\/v2\/pages\/91\/revisions\/1501"}],"up":[{"embeddable":true,"href":"http:\/\/pervasivegis.group\/teamsite\/wp-json\/wp\/v2\/pages\/857"}],"wp:attachment":[{"href":"http:\/\/pervasivegis.group\/teamsite\/wp-json\/wp\/v2\/media?parent=91"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}