检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Li, Yawei Zhang, Kai Timofte, Radu Van Gool, Luc Kong, Fangyuan Li, Mingxi Liu, Songwei Du, Zongcai Liu, Ding Zhou, Chenhui Chen, Jingyi Han, Qingrui Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Qiao, Yu Dong, Chao Sun, Long Pan, Jinshan Zhu, Yi Zong, Zhikai Liu, Xiaoxiao Hui, Zheng Yang, Tao Ren, Peiran Xie, Xuansong Hua, Xian-Sheng Wang, Yanbo Ji, Xiaozhong Lin, Chuming Luo, Donghao Tai, Ying Wang, Chengjie Zhang, Zhizhong Xie, Yuan Cheng, Shen Luo, Ziwei Yu, Lei Wen, Zhihong Wu, Qi Li, Youwei Fan, Haoqiang Sun, Jian Liu, Shuaicheng Huang, Yuanfei Jin, Meiguang Huang, Hua Liu, Jing Zhang, Xinjian Wang, Yan Long, Lingshun Li, Gen Zhang, Yuanfan Cao, Zuowei Sun, Lei Alexander, Panaetov Wang, Yucong Cai, Minjie Wang, Li Tian, Lu Wang, Zheyuan Ma, Hongbing Liu, Jie Chen, Chao Cai, Yidong Tang, Jie Wu, Gangshan Wang, Weiran Huang, Shirui Lu, Honglei Liu, Huan Wang, Keyan Chen, Jun Chen, Shi Miao, Yuchun Huang, Zimo Zhang, Lefei Ayazoglu, Mustafa Xiong, Wei Xiong, Chengyi Wang, Fei Li, Hao Wen, Ruimian Yang, Zhijing Zou, Wenbin Zheng, Weixin Ye, Tian Zhang, Yuncheng Kong, Xiangzhen Arora, Aditya Zamir, Syed Waqas Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Gao, Dandan Zhou, Dengwen Ning, Qian Tang, Jingzhu Huang, Han Wang, Yufei Peng, Zhangheng Li, Haobo Guan, Wenxue Gong, Shenghua Li, Xin Liu, Jun Wang, Wanjun Zeng, Kun Lin, Hanjiang Chen, Xinyu Fang, Jinsheng Zhang, Shuhao Zhang, Yuhao Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Tan, Wei Chen, Hao Xu, Qian Narang, Pratik Singh, Usneek Sameen, Syed Khaitan, Harsh Yinghua, Liu Tianlin, Zhang Xiaoming, Zhang Meng, Dingxuan Tian, Chunwei Morshed, Mashrur M. Ahsan, Ahmad Omar Computer Vision Lab ETH Zurich Switzerland University of Würzburg Germany ByteDance Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University China ByteDance Inc China NetEase Inc. China Shenzhen Institutes of Advanced Technology CAS China University of Macau China Shanghai AI Lab Shanghai China Nanjing University of Science and Technology China Amazon Web Services United States China Alibaba DAMO Academy EFC Yuhang District Zhejiang Hangzhou China East China Normal University China Youtu Lab Tencent China Megvii Technology China University of Electronic Science and Technology of China China School of Artificial Intelligence Beijing Normal University China Alibaba Group China Bilibili AI China Nankai-Baidu Joint Lab Nankai University Tianjin China Platform Technologies Tencent Online Video China Higher School of Economics Russia Huawei Moscow Research Center Russia Hunan University China Xidian University China Xilinx Technology Beijing Limited China College of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Nanjing University China McMaster University Canada School of Telecommunication Engineering Xidian University Xi’an China School of Computer Science Wuhan University Wuhan China School of Mathematical Science University of Electronic Science and Technology of China Chengdu China School of Computer Science The University of Sydney Sydney Australia Aselsan Research Ankara Turkey School of Electronic and Information Engineering South-Central University for Nationalities Wuhan China Guangdong University of Technology China Fujian Normal University Fuzhou University Jimei University China Design Group China Abu Dhabi United Arab Emirates Monash University Melbourne Australia Mohamed bin Zayed University of AI United Arab Emirates North China Electric Power University Changping District Beijing China The School of Art

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution. © 2022, CC BY.

关键词： Chemical activation

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Intelligent reflecting surface based passive information transmission: A symbol-level precoding approach

arXiv

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arXiv 2020年

作者： Liu, Rang Li, Ming Liu, Qian Swindlehurst, A. Lee Wu, Qingqing School of Information and Communication Engineering Dalian University of Technology Dalian116024 China School of Computer Science and Technology Dalian University of Technology Dalian116024 China Center for Pervasive Communications and Computing University of California IrvineCA92697 United States State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering University of Macau 999078 China

Intelligent reflecting surfaces (IRS) have been proposed as a revolutionary technology owing to its capability of adaptively reconfiguring the propagation environment in a cost-effective and hardware-efficient fashion. While the application of IRS as a passive reflector to enhance the performance of wireless communications has been widely investigated in the literature, using IRS as a passive transmitter recently is emerging as a new concept and attracting steadily growing interest. In this paper, we propose two novel IRS-based passive information transmission systems using advanced symbol-level precoding. One is a standalone passive information transmission system, where the IRS operates as a passive transmitter serving multiple receivers by adjusting its elements to reflect unmodulated carrier signals. The other is a joint passive reflection and information transmission system, where the IRS not only enhances transmissions for multiple primary information receivers (PIRs) by passive reflection, but also simultaneously delivers additional information to a secondary information receiver (SIR) by embedding its information into the primary signals at the symbol level. Two typical optimization problems, i.e., power minimization and quality-of-service (QoS) balancing, are investigated for the proposed IRS-based passive information transmission systems. Simulation results demonstrate the feasibility of IRS-based passive information transmission and the effectiveness of our proposed algorithms, as compared to other benchmark schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Transmitters

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Trorf: Building trading areas around organizations based on machine learning techniques

Trorf: Building trading areas around organizations based on ...

引用

2019 International Conference on Intelligent computing, Automation and Systems, ICICAS 2019

作者： Xia, Yuwei Fu, Min Zheng, Xi Wu, Binqing Ke, Zhihao School of Economic Information Engineering Southwestern University of Finance and Economics Chengdu China Alibaba Group Hangzhou China Department of Computing Macquarie University Sydney Australia Department of Computer Science Leeds Joint School Southwest Jiaotong University Chengdu China State Key Laboratory of Traction Power Southwest Jiaotong University Chengdu China

ISBN: (纸本)9781728161068

Nowadays, making investments in trading areas around organizations is becoming increasingly pervasive since more and more organizations (e.g. universities) have been relocated in suburban-districts far from downtown. It generally requires a fairly long time for investors to make decisions and establish mature trading areas around organizations. Existing researches that focus on optimal location selection ignore the latent relationship between organizations and their surrounding business types. Therefore, this paper proposes a machine-learning based methodology of determining business types and trading areas, named TrORF, to help investors select business types and build trading areas around organizations. We implement our approach and evaluate it by using 13 real-world universities as our case studies. We compare the business type recommendation accuracy of the 13 real-world universities between our method and the other four existing methods. The experiment results indicate that our approach has strong ability to build the suitable business types around each university's trading area since its average recommendation accuracy is 98.23%, and it improves on other approaches by at least 13%. © 2019 IEEE.

关键词： Machine learning

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SOUA: Towards intelligent recommendation for applying for overseas universities

SOUA: Towards intelligent recommendation for applying for ov...

引用

2019 International Conference on Intelligent computing, Automation and Systems, ICICAS 2019

作者： Wu, Binqing Ke, Zhihao Fu, Min Xia, Yuwei Department of Computer Science Leeds Joint School Southwest Jiaotong University Chengdu China State Key Laboratory of Traction Power Southwest Jiaotong University Chengdu China Alibaba Group Hangzhou China Department of Computing Macquarie University Sydney Australia School of Economic Information Engineering Southwestern University of Finance and Economics Chengdu China

ISBN: (纸本)9781728161068

In China, to pursue a master's degree in foreign countries is becoming an increasingly popular choice for undergraduate students. However, predicting the results of university applications is mostly done by applying empirical rules and referring to previous admission cases similar to the applicant's conditions, which is unreliable. In this paper, we propose an intelligent recommendation method, called SOUA, to make recommendations for students who apply for the admissions of overseas universities to study abroad. Our method is proposed based on a dataset obtained by us, which is about Chinese undergraduate students' applying for master's degrees in computer science-related majors of American universities. We apply data mining techniques on this dataset. A new data augmentation method named ILGA is proposed to expand the size of the training set. In the experiments, machine learning models such as LR, KNN, DT, SVM, RF, GBDT and MLP are investigated using this dataset. The experimental results show that RF achieving a weighed f1-score of 71.2% on the test set outperforms other considered machine learning models. And RF produces the optimal university recommendation accuracy of 80.9%. © 2019 IEEE.

关键词： Data mining

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引用

作者： Yang, Peng Zhang, Ning Zhang, Shan Yu, Li Zhang, Junshan Shen, Xuemin School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan 430074 China Department of Computing Sciences Texas AandM University-Corpus Christi Corpus Christi TX 78412 United States Beijing Key Laboratory of Computer Networks School of Computer Science and Engineering Beihang University Beijing 100191 China School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ 85287 United States Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada

Mobile edge caching aims to enable content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case with zero-mean noise, a ridge regression-based online algorithm with positive perturbation is proposed. Regret analysis indicates that the hit rate achieved by the proposed algorithm asymptotically approaches that of the optimal caching strategy in the long run. When the noise structure is unknown, an H∞ filter-based online algorithm is devised by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases and, hence, are robust to the time-varying user demands. The estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments using real-world datasets are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving a content hit rate that is comparable to that via the hindsight optimal strategy. © 1999-2012 IEEE.

关键词： Mobile edge computing

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Uplink-downlink duality between multiple-access and broadcast channels with compressing relays

arXiv

引用

arXiv 2020年

作者： Liu, Liang Liu, Ya-Feng Patil, Pratik Yu, Wei the Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong the State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Edward S. Rogers Sr. Department of Electrical and Computer Engineering the University of Toronto the Department of Statistics and Data Science and the Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto 10 King’s College Road TorontoONM5S3G4 Canada

—Uplink-downlink duality refers to the fact that under a sum-power constraint, the capacity regions of a Gaussian multiple-access channel and a Gaussian broadcast channel with Hermitian transposed channel matrices are identical. This paper generalizes this result to a cooperative cellular network, in which remote access-points are deployed as relays in serving the users under the coordination of a central processor (CP). In this model, the users and the relays are connected over noisy wireless links, while the relays and the CP are connected over noiseless but rate-limited fronthaul links. Based on a Lagrangian technique, this paper establishes a duality relationship between such a multiple-access relay channel and broadcast relay channel, under the assumption that the relays use compression-based strategies. Specifically, we show that under the same total transmit power constraint and individual fronthaul rate constraints, the achievable rate regions of the Gaussian multiple-access and broadcast relay channels are identical, when either independent compression or Wyner-Ziv and multivariate compression strategies are used. The key observations are that if the beamforming vectors at the relays are fixed, the sum-power minimization problems under the achievable rate and fronthaul constraints in both the uplink and the downlink can be transformed into either a linear programming or a semidefinite programming problem depending on the compression technique, and that the uplink and downlink problems are Lagrangian duals of each other. Moreover, the dual variables corresponding to the downlink rate constraints become the uplink powers;the dual variables corresponding to the downlink fronthaul constraints become the uplink quantization noises. This duality relationship enables an efficient algorithm for optimizing the downlink transmission and relaying strategies based on the uplink. Copyright © 2020, The Authors. All rights reserved.

关键词： Linear programming

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Voltage Manipulation of Desired Magnetization Orientation in Multiferroic Nanomagnets

SSRN

引用

SSRN 2020年

作者： Liu, Jiahao Yang, Xiaokuo Hong, Dunhua Li, Cheng Xu, Nuo Yang, Binbin Fang, Liang Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Department of Basic Sciences Air Force Engineering University Xi’an710051 China School of Physics and Electronic Science Zhunyi Normal University Zhunyi563006 China

We report the pure voltage manipulation of the desired magnetization orientation of the multiferroic nanomagnet: through integrating spintronics and multiferroics, we investigate magnetostrictive elliptical two-step-shaped nanomagnets. A pair of electrodes is fabricated on the piezoelectric substrate to generate localized stress by applying voltage. Through the inverse magnetoelectric coupling, slightly inclined stress induces the magnetization of the nanomagnet to desired orientation at room temperature. Our method liberates the electrical manipulation of multiferroic nanomagnets from the strict requirements of the voltage waveform. The results can be effectively applied to electrical induced information storage and logic writing of low- energy-consumption spintronic devices. © 2020, The Authors. All rights reserved.

关键词： Nanomagnetics

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

引用

arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Dissipative Landau-Zener tunneling in the crossover regime from weak to strong environment coupling

arXiv

引用

arXiv 2022年

作者： Dai, X. Trappen, R. Chen, H. Melanson, D. Yurtalan, M.A. Tennant, D.M. Martinez, A.J. Tang, Y. Mozgunov, E. Gibson, J. Grover, J.A. Disseler, S.M. Basham, J.I. Novikov, S. Das, R. Melville, A.J. Niedzielski, B.M. Hirjibehedin, C.F. Serniak, K. weber, S.J. Yoder, J.L. Oliver, W.D. Zick, K.M. Lidar, D.A. Lupascu, A. Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Department of Electrical & Computer Engineering University of Southern California Los AngelesCA90089 United States Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada University of Southern California - Information Sciences Institute ArlingtonVA22203 United States Northrop Grumman Corporation LinthicumMD21090 United States Department of Physics and Astronomy Dartmouth College HanoverNH03755 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Lincoln Laboratory Massachusetts Institute of Technology LexingtonMA02421 United States QuEra Computing Inc. 1284 Soldiers Field Road BostonMA02135 United States Atlantic Quantum Corp. CambridgeMA02139 United States Department of Chemistry University of Southern California Los AngelesCA90089 United States Department of Physics University of Southern California Los AngelesCA90089 United States Waterloo Institute for Nanotechnology University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics ETH Zurich ZurichCH-8093 Switzerland Department of Physics Harvard University CambridgeMA02138 United States Rigetti Computing BerkeleyCA94710 United States VancouverBCV6E 4B1 Canada

Landau-Zener tunneling, which describes the transition in a two-level system during a sweep through an anti-crossing, is a model applicable to a wide range of physical phenomena. Realistic quantum systems are affected by dissipation due to coupling to their environments. An important aspect of understanding such open quantum systems is the relative energy scales of the system itself and the system-environment coupling, which distinguishes the weak- and strong-coupling regimes. Using a tunable superconducting flux qubit, we observe the crossover from weak to strong coupling to the environment in Landau-Zener tunneling. Our results confirm previous theoretical studies of dissipative Landau-Zener tunneling in the weak and strong coupling limits. We devise a spin bath model that effectively captures the crossover regime. This work is relevant for understanding the role of dissipation in quantum annealing, where the system is expected to go through a cascade of Landau-Zener transitions before reaching the target state. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum optics

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A Dynamic Road Incident information Delivery Strategy to Reduce Urban Traffic Congestion

引用

IEEE/CAA Journal of Automatica Sinica 2018年第5期5卷 934-945页

作者： Liang Qi Mengchu Zhou Wenjing Luan IEEE Department of Computer Scicnce and Technology Shandong University of Science and TechnologyQingdao 266590China Department of Computer Science Tongji UniversityShanghai 201804China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102USA Key Laboratory of Embedded System and Service Computing Ministry of EducationShanghai Electronic Transactions and Information Service Collaborative Innovation CenterDepartment of Com-puter ScienceTongji UniversityShanghai 201804China

Advanced information and communication technolo-gies can be used to facilitate traffic incident *** an incident is detected and blocks a road link,in order to reduce the incident-induced traffic congestion,a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this ***-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information.A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well *** results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles,operation time interval of the proposed strategy,traffic density in the traffic network,and the scope of the area where traffic incident information is *** results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.

关键词： Cell transmission model(CTM) intelligent transportation systems(ITS) traffic incident management(TIM) urban traffic congestion

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