机构

• 地理科学与资源研究所 [5]
• 长春光学精密机械与物... [1]

采集方式

• OAI收割 [6]

内容类型

• 期刊论文 [3]
• SCI/SSCI论文 [2]
• 会议论文 [1]

发表日期

• 2020 [1]
• 2019 [2]
• 2014 [1]
• 2013 [1]
• 2011 [1]

学科主题

筛选

浏览/检索结果: 共6条，第1-6条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序作者升序作者降序 A trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2020, 页码: 24 作者:  Li, Bozhao;  Cai, Zhongliang;  Kang, Mengjun;  Su, Shiliang;  Zhang, Shanshan   |  收藏  |  浏览/下载：48/0  |  提交时间：2021/03/16 Trajectory restoration  map-matching  A* shortest path algorithm  low-sampling-rate FCD  complex road networks   Drawing topological properties from a multi-layered network: The case of an air transport network in "the Belt and Road" region 期刊论文  OAI收割 HABITAT INTERNATIONAL, 2019, 卷号: 93, 页码: 11 作者:  Zhang, Lu;  Du Hongru;  Zhao Yannan;  De Maeyer, Philippe;  Dessein, Bart   |  收藏  |  浏览/下载：26/0  |  提交时间：2020/05/19 The Belt and Road  Air transport  Topology  Multi-layered network  Core and bridge layers  Complex networks   Drawing topological properties from a multi-layered network: The case of an air transport network in "the Belt and Road" region 期刊论文  OAI收割 HABITAT INTERNATIONAL, 2019, 卷号: 93, 页码: 11 作者:  Zhang, Lu;  Du Hongru;  Zhao Yannan;  De Maeyer, Philippe;  Dessein, Bart   |  收藏  |  浏览/下载：47/0  |  提交时间：2020/05/19 The Belt and Road  Air transport  Topology  Multi-layered network  Core and bridge layers  Complex networks   Robustness of city road networks at different granularities SCI/SSCI论文  OAI收割 2014 作者:  Lu F. 收藏  |  浏览/下载：38/0  |  提交时间：2014/12/24 City road networks  Robustness analysis  Simulated successive attacks  Structural diversity  complex networks  vulnerability  community  streets   Structural robustness of city road networks based on community SCI/SSCI论文  OAI收割 2013 作者:  Lu F. 收藏  |  浏览/下载：25/0  |  提交时间：2014/12/24 City road networks  Robustness analysis  Community detection  Random  attack  Intentional attack  complex networks   Neural network based online traffic signal controller design with reinforcement training (EI CONFERENCE) 会议论文  OAI收割 14th IEEE International Intelligent Transportation Systems Conference, ITSC 2011, October 5, 2011 - October 7, 2011, Washington, DC, United states Dai Y.; Hu J.; Zhao D.; Zhu F. 收藏  |  浏览/下载：31/0  |  提交时间：2013/03/25 Traffic congestion leads to problems like delays  decreasing flow rate  and higher fuel consumption. Consequently  keeping traffic moving as efficiently as possible is not only important to economy but also important to environment. Traffic system is a large complex nonlinear stochastic system. Traditional mathematical methods have some limitations when they are applied in traffic control. Thus  computational intelligence (CI) technologies gain more and more attentions. Neural Networks (NNs) is a well developed CI technology with lots of promising applications in traffic signal control (TSC). In this paper  a neural network (NN) based signal controller is designed to control the traffic lights in an urban traffic road network. Scenarios of simulation are conducted under a microscopic traffic simulation software. Several criterions are collected. Results demonstrate that through online reinforcement training the controllers obtain better control effects than the widely used pre-time and actuated methods under various traffic conditions. 2011 IEEE.