6月10日~12日,CRTS CHINA2015国际轨道交通展（第十一届中国国际轨道交通技术展览会）在上海新国际博览中心E6馆举行。近千位上下游客户齐聚一堂,展示、交流铁路与城市轨道交通领域的最新产品、顶尖技术和解决方案。据了解,CRTS CHINA 2...6月10日~12日,CRTS CHINA2015国际轨道交通展（第十一届中国国际轨道交通技术展览会）在上海新国际博览中心E6馆举行。近千位上下游客户齐聚一堂,展示、交流铁路与城市轨道交通领域的最新产品、顶尖技术和解决方案。据了解,CRTS CHINA 2015国际轨道交通展（以下简称＂CRTS CHINA2015＂）是由RT《轨道交通》杂志、RT轨道交通网主办,邀请国内外轨道交通行业相关政府主管单位、协会/学会等共同参与组织。自首办以来,得到了中国土木工程学会、中国轨道交通产业联盟、西班牙铁路协会、美国铁路工程和养路协会等机构的协办支持参与，并于2009年正式获得国家住建部、科技部批准主办，成为国内城市轨道交通领域唯一由两个部委批准的权威国际大型综合展。展开更多
The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagg...The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagging of the signal timing plans to traffic conditions. Utilizing the traffic conditions in current and former intervals, the network topology of the state-space neural network （SSNN）, which is derived from the geometry of urban arterial routes, is used to predict the optimal timing plan corresponding to the traffic conditions in the next time interval. In order to improve the effectiveness of the SSNN, the extended Kalman filter （EKF） is proposed to train the SSNN instead of conventional approaches. Raw traffic data of the Guangzhou Road, Nanjing and the optimal signal timing plan generated by a multi-objective optimization genetic algorithm are applied to test the performance of the proposed model. The results indicate that compared with the SSNN and the BP neural network, the proposed model can closely match the optimal timing plans in futuristic states with higher efficiency.展开更多
文摘6月10日~12日,CRTS CHINA2015国际轨道交通展（第十一届中国国际轨道交通技术展览会）在上海新国际博览中心E6馆举行。近千位上下游客户齐聚一堂,展示、交流铁路与城市轨道交通领域的最新产品、顶尖技术和解决方案。据了解,CRTS CHINA 2015国际轨道交通展（以下简称＂CRTS CHINA2015＂）是由RT《轨道交通》杂志、RT轨道交通网主办,邀请国内外轨道交通行业相关政府主管单位、协会/学会等共同参与组织。自首办以来,得到了中国土木工程学会、中国轨道交通产业联盟、西班牙铁路协会、美国铁路工程和养路协会等机构的协办支持参与，并于2009年正式获得国家住建部、科技部批准主办，成为国内城市轨道交通领域唯一由两个部委批准的权威国际大型综合展。
基金The National Natural Science Foundation of China (No.50422283)the Soft Science Research Project of Ministry of Housing and Urban-Rural Development of China (No.2008-K5-14)
文摘The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagging of the signal timing plans to traffic conditions. Utilizing the traffic conditions in current and former intervals, the network topology of the state-space neural network （SSNN）, which is derived from the geometry of urban arterial routes, is used to predict the optimal timing plan corresponding to the traffic conditions in the next time interval. In order to improve the effectiveness of the SSNN, the extended Kalman filter （EKF） is proposed to train the SSNN instead of conventional approaches. Raw traffic data of the Guangzhou Road, Nanjing and the optimal signal timing plan generated by a multi-objective optimization genetic algorithm are applied to test the performance of the proposed model. The results indicate that compared with the SSNN and the BP neural network, the proposed model can closely match the optimal timing plans in futuristic states with higher efficiency.
