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Yisheng Lv
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

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Journal article
Published: 23 August 2021 in Applied Sciences
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Fuzzy systems (FSs) are popular and interpretable machine learning methods, represented by the adaptive neuro-fuzzy inference system (ANFIS). However, they have difficulty dealing with high-dimensional data due to the curse of dimensionality. To effectively handle high-dimensional data and ensure optimal performance, this paper presents a deep neural fuzzy system (DNFS) based on the subtractive clustering-based ANFIS (SC-ANFIS). Inspired by deep learning, the SC-ANFIS is proposed and adopted as a submodule to construct the DNFS in a bottom-up way. Through the ensemble learning and hierarchical learning of submodules, DNFS can not only achieve faster convergence, but also complete the computation in a reasonable time with high accuracy and interpretability. By adjusting the deep structure and the parameters of the DNFS, the performance can be improved further. This paper also performed a profound study of the structure and the combination of the submodule inputs for the DNFS. Experimental results on five regression datasets with various dimensionality demonstrated that the proposed DNFS can not only solve the curse of dimensionality, but also achieve higher accuracy, less complexity, and better interpretability than previous FSs. The superiority of the DNFS is also validated over other recent algorithms especially when the dimensionality of the data is higher. Furthermore, the DNFS built with five inputs for each submodule and two inputs shared between adjacent submodules had the best performance. The performance of the DNFS can be improved by distributing the features with high correlation with the output to each submodule. Given the results of the current study, it is expected that the DNFS will be used to solve general high-dimensional regression problems efficiently with high accuracy and better interpretability.

ACS Style

Dewang Chen; Jijie Cai; Yunhu Huang; Yisheng Lv. Deep Neural Fuzzy System Oriented toward High-Dimensional Data and Interpretable Artificial Intelligence. Applied Sciences 2021, 11, 7766 .

AMA Style

Dewang Chen, Jijie Cai, Yunhu Huang, Yisheng Lv. Deep Neural Fuzzy System Oriented toward High-Dimensional Data and Interpretable Artificial Intelligence. Applied Sciences. 2021; 11 (16):7766.

Chicago/Turabian Style

Dewang Chen; Jijie Cai; Yunhu Huang; Yisheng Lv. 2021. "Deep Neural Fuzzy System Oriented toward High-Dimensional Data and Interpretable Artificial Intelligence." Applied Sciences 11, no. 16: 7766.

Journal article
Published: 26 January 2021 in Applied Sciences
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The extensive proliferation of urban transit cards and smartphones has witnessed the feasibility of the collection of citywide travel behaviors and the estimation of traffic status in real-time. In this paper, an urban public traffic dynamic network based on the cyber-physical-social system (CPSS-UPTDN) is proposed as a universal framework for advanced public transportation systems, which can optimize the urban public transportation based on big data and AI methods. Firstly, we introduce three modules and two loops which composes of the novel framework. Then, the key technologies in CPSS-UPTDN are studied, especially collecting and analyzing traffic information by big data and AI methods, and a particular implementation of CPSS-UPTDN is discussed, namely the artificial system, computational experiments, and parallel execution (ACP) method. Finally, a case study is performed. The data sources include both traffic congestion data from physical space and cellular data from social space, which can improve the prediction performance for traffic status. Furthermore, the service quality of urban public transportation can be promoted by optimizing the bus dispatching based on the parallel execution in our framework.

ACS Style

Gang Xiong; Zhishuai Li; Huaiyu Wu; Shichao Chen; Xisong Dong; Fenghua Zhu; Yisheng Lv. Building Urban Public Traffic Dynamic Network Based on CPSS: An Integrated Approach of Big Data and AI. Applied Sciences 2021, 11, 1109 .

AMA Style

Gang Xiong, Zhishuai Li, Huaiyu Wu, Shichao Chen, Xisong Dong, Fenghua Zhu, Yisheng Lv. Building Urban Public Traffic Dynamic Network Based on CPSS: An Integrated Approach of Big Data and AI. Applied Sciences. 2021; 11 (3):1109.

Chicago/Turabian Style

Gang Xiong; Zhishuai Li; Huaiyu Wu; Shichao Chen; Xisong Dong; Fenghua Zhu; Yisheng Lv. 2021. "Building Urban Public Traffic Dynamic Network Based on CPSS: An Integrated Approach of Big Data and AI." Applied Sciences 11, no. 3: 1109.

Journal article
Published: 30 August 2020 in Energy
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Accurate prediction of the photovoltaic (PV) power generation is of great significance for the efficient management of the power grid. In order to strengthen the interpretability of the data-driven models for PV power prediction and to further improve the forecasting accuracy, a novel double-input-rule-modules (DIRMs) stacked deep fuzzy model (DIRM-DFM) is proposed in this study. Firstly, the proposed stacked structure of DIRM-DFM is presented. This novel modular structure adopts a bottom-up, layer-by-layer design scheme by stacking the DIRMs which has only two input variables. This scheme assures the interpretability of the proposed novel fuzzy model. Then, to guarantee the performance of DIRM-DFM, its learning mechanism, including the training data generation, the construction of the DIRMs, are given in detail. This learning mechanism has fast learning speed and excellent approximation ability, because each DIRM is optimized by the popular least square method. Finally, two real-world experiments for predicting the PV power generation are conducted to verify the proposed DIRM-DFM, and detailed comparisons are made with traditional and deep fuzzy models, shallow and deep neural networks. Experimental results clearly demonstrated that the proposed DIRM-DFM has the best accuracy and the reactively fast training speed while having the apparent advantages of interpretability.

ACS Style

Chengdong Li; Changgeng Zhou; Wei Peng; Yisheng Lv; Xin Luo. Accurate prediction of short-term photovoltaic power generation via a novel double-input-rule-modules stacked deep fuzzy method. Energy 2020, 212, 118700 .

AMA Style

Chengdong Li, Changgeng Zhou, Wei Peng, Yisheng Lv, Xin Luo. Accurate prediction of short-term photovoltaic power generation via a novel double-input-rule-modules stacked deep fuzzy method. Energy. 2020; 212 ():118700.

Chicago/Turabian Style

Chengdong Li; Changgeng Zhou; Wei Peng; Yisheng Lv; Xin Luo. 2020. "Accurate prediction of short-term photovoltaic power generation via a novel double-input-rule-modules stacked deep fuzzy method." Energy 212, no. : 118700.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
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Traffic prediction is an elemental function of Intelligent Transportation Systems (ITS), and accurate and timely prediction is significant for both proactive traffic control and providing traveler information. In this paper, we focus on investigating ensemble leaning that benefits from different base models, and propose a traffic-condition-awareness ensemble approach. We apply graph convolution on the network of traffic detectors to capture the spatial patterns embedded in traffic flow. Then, the extracted features are used to formulate a weight matrix to ensemble the predictions of base models according to their performances under a certain condition. We performed a series of experiments on a real dataset to compare the proposed methods with several competitive models, including ensemble methods: Weight Regression model and Gradient Boosting Regression Tree model, and single model approach: Support Vector Regression (SVR), Long Short-term Memory (LSTM) model and Historical Average Model model. Experimental results demonstrate that our method can significantly improve the performances of traffic flow prediction.

ACS Style

Yuanyuan Chen; Yisheng Lv; Peijun Ye; Fenghua Zhu. Traffic-Condition-Awareness Ensemble Learning for Traffic Flow Prediction. IFAC-PapersOnLine 2020, 53, 582 -587.

AMA Style

Yuanyuan Chen, Yisheng Lv, Peijun Ye, Fenghua Zhu. Traffic-Condition-Awareness Ensemble Learning for Traffic Flow Prediction. IFAC-PapersOnLine. 2020; 53 (5):582-587.

Chicago/Turabian Style

Yuanyuan Chen; Yisheng Lv; Peijun Ye; Fenghua Zhu. 2020. "Traffic-Condition-Awareness Ensemble Learning for Traffic Flow Prediction." IFAC-PapersOnLine 53, no. 5: 582-587.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
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Cyber-Physical-Social Systems (CPSS) provides a novel perspective for constructing “Smart City”, which is also known as the Human-Machine-Things-System (HMTS), focusing on the fusion of ternary space: social network of human society, network of machines and the Internet of things. In this paper, we propose a specific implementation framework of CPSS for Smart City based on intelligent loops, including basic modeling and interactive fusion, state perception and cognition, and adaptive learning. On this basis, an overall architecture of the CPSS platform is designed, which is applied in the urban transportation management in Hangzhou. The application results demonstrate that the intelligent loop could optimize the control and management strategies for actual urban transportation.

ACS Style

Gang Xiong; Xiaoyu Chen; Nan Shuo; Yisheng Lv; Fenghua Zhu; Tianci Qu; Peijun Ye. Cyber-Physical-Social Systems for Smart City: An Implementation Based on Intelligent Loop. IFAC-PapersOnLine 2020, 53, 501 -506.

AMA Style

Gang Xiong, Xiaoyu Chen, Nan Shuo, Yisheng Lv, Fenghua Zhu, Tianci Qu, Peijun Ye. Cyber-Physical-Social Systems for Smart City: An Implementation Based on Intelligent Loop. IFAC-PapersOnLine. 2020; 53 (5):501-506.

Chicago/Turabian Style

Gang Xiong; Xiaoyu Chen; Nan Shuo; Yisheng Lv; Fenghua Zhu; Tianci Qu; Peijun Ye. 2020. "Cyber-Physical-Social Systems for Smart City: An Implementation Based on Intelligent Loop." IFAC-PapersOnLine 53, no. 5: 501-506.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
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The integration of signals from physical, social and cyber spaces, known as Cyber-Physical-Social systems (CPSS), is a new research paradigm for urban transportation, where the traffic control and management (C&M) is collaborative optimized among the three sub-systems. Though some technologies and optimization methods have been studied since its proposition, there is a lack of a systemic architecture as well as an overall implementation about how to efficiently exploit the social signals. For this reason, this paper proposes a general framework of CPSS for urban transportation and presents a feasible solution for traffic optimization based on knowledge automation. The specific implementation includes basic modeling of CPSS, knowledge evolution and reasoning, and collaborative optimization of C&P strategies. As a remarkable highlight, the influence of both individual activities and social learning is concerned during knowledge evolution and reasoning part. A case study from the application in the city of Dongguan is also given to validate our proposed framework and methods, showing that they can efficiently improve the average speed of the actual transportation.

ACS Style

Gang Xiong; Xiaoyu Chen; Nan Shuo; Yisheng Lv; Fenghua Zhu; Tianci Qu; Peijun Ye. Collaborative Optimization of Cyber Physical Social Systems for Urban Transportation Based on Knowledge Automation. IFAC-PapersOnLine 2020, 53, 572 -577.

AMA Style

Gang Xiong, Xiaoyu Chen, Nan Shuo, Yisheng Lv, Fenghua Zhu, Tianci Qu, Peijun Ye. Collaborative Optimization of Cyber Physical Social Systems for Urban Transportation Based on Knowledge Automation. IFAC-PapersOnLine. 2020; 53 (5):572-577.

Chicago/Turabian Style

Gang Xiong; Xiaoyu Chen; Nan Shuo; Yisheng Lv; Fenghua Zhu; Tianci Qu; Peijun Ye. 2020. "Collaborative Optimization of Cyber Physical Social Systems for Urban Transportation Based on Knowledge Automation." IFAC-PapersOnLine 53, no. 5: 572-577.

Journal article
Published: 28 January 2019 in Energy and Buildings
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Building energy consumption prediction is becoming increasingly vital for energy management, equipment efficiency improvement, cooperation between building energy and power grid, and so on. But it is still a hard work to obtain accurate prediction results because of the complexity of the building energy behavior and the frequent undulations in the energy demand. In the building energy consumption prediction, the existing historical data are usually used to construct the traditional machine learning models and the deep learning models. However, compared with the data sets in the research domains of image recognition, speech processing and other fields, the data sets in the time series prediction of building energy consumption do not have a large quantity. Although the gray model can reduce the reliability on sufficient data, the model is difficult to develop, and it still needs detailed building information that may be lost in existing buildings. To overcome such issues, based on the parallel learning theory, we propose the parallel prediction scheme for the building energy consumption using Generative Adversarial Nets (GAN). The parallel prediction firstly makes use of a small number of the original data series to generate the parallel data via GAN, and then forms the mixed data set which includes the original data and the artificial data, and finally utilizes the mixed data to train the prediction models. To verify the proposed parallel prediction method, two experiments which adopts different kinds of data sets from two real-world buildings are conducted. In each experiment, the availability of the parallel data and the rationality of the parallel prediction model are evaluated, and detailed comparisons are made. Experimental results show that the parallel data have similar distributions to the original data, and the prediction models trained by the mixed data perform better than those trained only using the original data. Comparison results demonstrated that the proposed method performs best compared with the existing methods such as the information diffusion technology (IDT), the heuristic Mega-trend-diffusion (HMTD) method and the bootstrap method. The proposed parallel prediction scheme can also be extended to other time series forecasting problems, such as the electricity load forecasting, and the traffic flow prediction.

ACS Style

Chenlu Tian; Chengdong Li; Guiqing Zhang; Yisheng Lv. Data driven parallel prediction of building energy consumption using generative adversarial nets. Energy and Buildings 2019, 186, 230 -243.

AMA Style

Chenlu Tian, Chengdong Li, Guiqing Zhang, Yisheng Lv. Data driven parallel prediction of building energy consumption using generative adversarial nets. Energy and Buildings. 2019; 186 ():230-243.

Chicago/Turabian Style

Chenlu Tian; Chengdong Li; Guiqing Zhang; Yisheng Lv. 2019. "Data driven parallel prediction of building energy consumption using generative adversarial nets." Energy and Buildings 186, no. : 230-243.

Journal article
Published: 22 November 2018 in Sensors
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Social sensors perceive the real world through social media and online web services, which have the advantages of low cost and large coverage over traditional physical sensors. In intelligent transportation researches, sensing and analyzing such social signals provide a new path to monitor, control and optimize transportation systems. However, current research is largely focused on using single channel online social signals to extract and sense traffic information. Clearly, sensing and exploiting multi-channel social signals could effectively provide deeper understanding of traffic incidents. In this paper, we utilize cross-platform online data, i.e., Sina Weibo and News, as multi-channel social signals, then we propose a word2vec-based event fusion (WBEF) model for sensing, detecting, representing, linking and fusing urban traffic incidents. Thus, each traffic incident can be comprehensively described from multiple aspects, and finally the whole picture of unban traffic events can be obtained and visualized. The proposed WBEF architecture was trained by about 1.15 million multi-channel online data from Qingdao (a coastal city in China), and the experiments show our method surpasses the baseline model, achieving an 88.1% F1 score in urban traffic incident detection. The model also demonstrates its effectiveness in the open scenario test.

ACS Style

Hao Lu; Kaize Shi; Yifan Zhu; Yisheng Lv; Zhendong Niu. Sensing Urban Transportation Events from Multi-Channel Social Signals with the Word2vec Fusion Model. Sensors 2018, 18, 4093 .

AMA Style

Hao Lu, Kaize Shi, Yifan Zhu, Yisheng Lv, Zhendong Niu. Sensing Urban Transportation Events from Multi-Channel Social Signals with the Word2vec Fusion Model. Sensors. 2018; 18 (12):4093.

Chicago/Turabian Style

Hao Lu; Kaize Shi; Yifan Zhu; Yisheng Lv; Zhendong Niu. 2018. "Sensing Urban Transportation Events from Multi-Channel Social Signals with the Word2vec Fusion Model." Sensors 18, no. 12: 4093.

Journal article
Published: 08 November 2018 in IEEE Transactions on Intelligent Transportation Systems
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Mining traffic-relevant information from social media data has become an emerging topic due to the real-time and ubiquitous features of social media. In this paper, we focus on a specific problem in social media mining which is to extract traffic relevant microblogs from Sina Weibo, a Chinese microblogging platform. It is transformed into a machine learning problem of short text classification. First, we apply the continuous bag-of-word model to learn word embedding representations based on a data set of three billion microblogs. Compared to the traditional one-hot vector representation of words, word embedding can capture semantic similarity between words and has been proved effective in natural language processing tasks. Next, we propose using convolutional neural networks (CNNs), long short-term memory (LSTM) models and their combination LSTM-CNN to extract traffic relevant microblogs with the learned word embeddings as inputs. We compare the proposed methods with competitive approaches, including the support vector machine (SVM) model based on a bag of n-gram features, the SVM model based on word vector features, and the multi-layer perceptron model based on word vector features. Experiments show the effectiveness of the proposed deep learning approaches.

ACS Style

Yuanyuan Chen; Yisheng Lv; Xiao Wang; Lingxi Li; Fei-Yue Wang. Detecting Traffic Information From Social Media Texts With Deep Learning Approaches. IEEE Transactions on Intelligent Transportation Systems 2018, 20, 3049 -3058.

AMA Style

Yuanyuan Chen, Yisheng Lv, Xiao Wang, Lingxi Li, Fei-Yue Wang. Detecting Traffic Information From Social Media Texts With Deep Learning Approaches. IEEE Transactions on Intelligent Transportation Systems. 2018; 20 (8):3049-3058.

Chicago/Turabian Style

Yuanyuan Chen; Yisheng Lv; Xiao Wang; Lingxi Li; Fei-Yue Wang. 2018. "Detecting Traffic Information From Social Media Texts With Deep Learning Approaches." IEEE Transactions on Intelligent Transportation Systems 20, no. 8: 3049-3058.

Journal article
Published: 20 July 2018 in Applied Sciences
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Traffic situation awareness and alerting assisted by adverse weather conditions contributes to improve traffic safety, disaster coping mechanisms, and route planning for government agencies, business sectors, and individual travelers. However, at the city level, the physical sensor-generated data are partly held by different transportation and meteorological departments, which causes problems of “isolated information” for data fusion. Furthermore, it makes traffic situation awareness and estimation challenging and ineffective. In this paper, we leverage the power of crowdsourcing knowledge in social media and propose a novel way to forecast and generate alerts for city-level traffic incidents based on a social approach rather than traditional physical approaches. Specifically, we first collect adverse weather topics and reports of traffic incidents from social media. Then, we extract temporal, spatial, and meteorological features as well as labeled traffic reaction values corresponding to the social media “heat” for each city. Afterwards, the regression and alerting model is proposed to estimate the city-level traffic situation and give the suggestion of warning levels. The experiments show that the proposed model equipped with gcForest achieves the best root mean square error (RMSE) and mean absolute percentage error (MAPE) score on the social traffic incidents test dataset. Moreover, we consider the news report as an objective measurement to flexibly validate the feasibility of proposed model from social cyberspace to physical space. Finally, a prototype system was deployed and applied to government agencies to provide an intuitive visualization solution as well as decision support assistance.

ACS Style

Hao Lu; Yifan Zhu; Kaize Shi; Yisheng Lv; Pengfei Shi; Zhendong Niu. Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting. Applied Sciences 2018, 8, 1193 .

AMA Style

Hao Lu, Yifan Zhu, Kaize Shi, Yisheng Lv, Pengfei Shi, Zhendong Niu. Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting. Applied Sciences. 2018; 8 (7):1193.

Chicago/Turabian Style

Hao Lu; Yifan Zhu; Kaize Shi; Yisheng Lv; Pengfei Shi; Zhendong Niu. 2018. "Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting." Applied Sciences 8, no. 7: 1193.

Journal article
Published: 07 June 2018 in IEEE Intelligent Transportation Systems Magazine
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Generative Adversaria Networks (GANs) have emerged as a promising and effective mechanism for machine learning due to its recent successful applications. GANs share the same idea of producing, testing, acquiring, and utilizing data as well as knowledge based on artificial systems, computational experiments, and parallel execution of actual and virtual scenarios, as outlined in the theory of parallel transportation. Clearly, the adversarial concept is embedded implicitly or explicitly in both GANs and parallel transportation systems. In this article, we first introduce basics of GANs and parallel transportation systems, and then present an approach of using GANs in parallel transportation systems for traffic data generation, traffic modeling, traffic prediction and traffic control. Our preliminary investigation indicates that GANs have a great potential and provide specific algorithm support for implementing parallel transportation systems.

ACS Style

Yisheng Lv; Yuanyuan Chen; Li Li; Fei-Yue Wang. Generative Adversarial Networks for Parallel Transportation Systems. IEEE Intelligent Transportation Systems Magazine 2018, 10, 4 -10.

AMA Style

Yisheng Lv, Yuanyuan Chen, Li Li, Fei-Yue Wang. Generative Adversarial Networks for Parallel Transportation Systems. IEEE Intelligent Transportation Systems Magazine. 2018; 10 (3):4-10.

Chicago/Turabian Style

Yisheng Lv; Yuanyuan Chen; Li Li; Fei-Yue Wang. 2018. "Generative Adversarial Networks for Parallel Transportation Systems." IEEE Intelligent Transportation Systems Magazine 10, no. 3: 4-10.

Journal article
Published: 19 January 2018 in Energies
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To enhance the prediction performance for building energy consumption, this paper presents a modified deep belief network (DBN) based hybrid model. The proposed hybrid model combines the outputs from the DBN model with the energy-consuming pattern to yield the final prediction results. The energy-consuming pattern in this study represents the periodicity property of building energy consumption and can be extracted from the observed historical energy consumption data. The residual data generated by removing the energy-consuming pattern from the original data are utilized to train the modified DBN model. The training of the modified DBN includes two steps, the first one of which adopts the contrastive divergence (CD) algorithm to optimize the hidden parameters in a pre-train way, while the second one determines the output weighting vector by the least squares method. The proposed hybrid model is applied to two kinds of building energy consumption data sets that have different energy-consuming patterns (daily-periodicity and weekly-periodicity). In order to examine the advantages of the proposed model, four popular artificial intelligence methods—the backward propagation neural network (BPNN), the generalized radial basis function neural network (GRBFNN), the extreme learning machine (ELM), and the support vector regressor (SVR) are chosen as the comparative approaches. Experimental results demonstrate that the proposed DBN based hybrid model has the best performance compared with the comparative techniques. Another thing to be mentioned is that all the predictors constructed by utilizing the energy-consuming patterns perform better than those designed only by the original data. This verifies the usefulness of the incorporation of the energy-consuming patterns. The proposed approach can also be extended and applied to some other similar prediction problems that have periodicity patterns, e.g., the traffic flow forecasting and the electricity consumption prediction.

ACS Style

Chengdong Li; Zixiang Ding; Jianqiang Yi; Yisheng Lv; Guiqing Zhang. Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction. Energies 2018, 11, 242 .

AMA Style

Chengdong Li, Zixiang Ding, Jianqiang Yi, Yisheng Lv, Guiqing Zhang. Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction. Energies. 2018; 11 (1):242.

Chicago/Turabian Style

Chengdong Li; Zixiang Ding; Jianqiang Yi; Yisheng Lv; Guiqing Zhang. 2018. "Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction." Energies 11, no. 1: 242.

Journal article
Published: 20 February 2015 in IEEE Transactions on Intelligent Transportation Systems
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Natural or man-made disasters can cause huge losses of human life and property. One of the effective and widely used response and mitigation strategies for these disasters is traffic evacuation. Evacuation destination choice is critical in evacuation traffic planning and management. In this paper, we propose a partially random destination allocation strategy for evacuation management. We present a metamodel-based simulation optimization method to design the strategy. The proposed method uses a quadratic polynomial as a metamodel, within which a degree-free trust region algorithm is developed to solve the proposed model. The performance of the proposed method is evaluated based on a subnetwork of Beijing with two different traffic demands. Computational experiments demonstrate that the proposed method can yield a well-performed strategy, leading to reduced network clearance times.

ACS Style

Yisheng Lv; Xiqiao Zhang; Wenwen Kang; Yanjie Duan; 生吕 宜. Managing Emergency Traffic Evacuation With a Partially Random Destination Allocation Strategy: A Computational-Experiment-Based Optimization Approach. IEEE Transactions on Intelligent Transportation Systems 2015, 16, 1 -10.

AMA Style

Yisheng Lv, Xiqiao Zhang, Wenwen Kang, Yanjie Duan, 生吕 宜. Managing Emergency Traffic Evacuation With a Partially Random Destination Allocation Strategy: A Computational-Experiment-Based Optimization Approach. IEEE Transactions on Intelligent Transportation Systems. 2015; 16 (4):1-10.

Chicago/Turabian Style

Yisheng Lv; Xiqiao Zhang; Wenwen Kang; Yanjie Duan; 生吕 宜. 2015. "Managing Emergency Traffic Evacuation With a Partially Random Destination Allocation Strategy: A Computational-Experiment-Based Optimization Approach." IEEE Transactions on Intelligent Transportation Systems 16, no. 4: 1-10.

Conference paper
Published: 01 July 2013 in Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics
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With the acceleration of China's urbanization, more and more unexpected disasters in big cities make a severe challenge to city emergency traffic management. Under this background, we present a heuristic implementation of urban emergency traffic evacuation in this paper. Firstly, we refer to a popular evacuation demand generation model to generate the evacuation demand. When solving the path selection problem, the heuristic search method is used. We take Dijkstra shortest path and current road condition as two parts of the evaluation function to evaluate different choices and choose the best one. To simulate the dynamic process of evacuation, we developed a position update algorithm to update the positions of traffic participants. The mathematical analysis method and computer simulation are combined to determine the final evacuation route of a traffic participant. This combination is effective since it takes advantages of both methods and avoids the shortcomings at the same time.

ACS Style

Wenwen Kang; Fenghua Zhu; Yisheng Lv; Gang Xiong; Li Xie; Bin Xi; 生吕 宜. A heuristic implementation of emergency traffic evacuation in urban areas. Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics 2013, 40 -44.

AMA Style

Wenwen Kang, Fenghua Zhu, Yisheng Lv, Gang Xiong, Li Xie, Bin Xi, 生吕 宜. A heuristic implementation of emergency traffic evacuation in urban areas. Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics. 2013; ():40-44.

Chicago/Turabian Style

Wenwen Kang; Fenghua Zhu; Yisheng Lv; Gang Xiong; Li Xie; Bin Xi; 生吕 宜. 2013. "A heuristic implementation of emergency traffic evacuation in urban areas." Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics , no. : 40-44.

Conference paper
Published: 01 July 2013 in Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics
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Information feedback is very important in traffic systems. Real-time information feedback can improve traffic flow with existing facilities. This paper proposes a real-time information feedback strategy named improved mean number feedback strategy. Based on a two-route scenario, simulation results show that the strategy is much more effective in different length of roads or in different percentage of dynamic vehicles than the old strategies, i.e., congestion coefficient feedback strategy and mean velocity feedback strategy.

ACS Style

Duan Yanjie; Zhu Fenghua; Xiong Gang; Li Yuantao; Lv Yisheng; 生吕 宜. Improved information feedback in symmetric dualchannel traffic. Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics 2013, 29 -34.

AMA Style

Duan Yanjie, Zhu Fenghua, Xiong Gang, Li Yuantao, Lv Yisheng, 生吕 宜. Improved information feedback in symmetric dualchannel traffic. Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics. 2013; ():29-34.

Chicago/Turabian Style

Duan Yanjie; Zhu Fenghua; Xiong Gang; Li Yuantao; Lv Yisheng; 生吕 宜. 2013. "Improved information feedback in symmetric dualchannel traffic." Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics , no. : 29-34.

Conference paper
Published: 01 July 2013 in Proceedings of 2013 IEEE International Conference on Vehicular Electronics and Safety
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An Emergency Traffic Evacuation Management Platform is developed in this paper to support evacuation planning and decision making for city managers. The platform is based on Eclipse RCP, a programming framework kept updating by the Eclipse RCP open source project. Architecture design and implementation of the platform are mainly discussed. Modularization is the basic idea when building the platform. At the last of the paper, we suppose that a sudden disaster has happened somewhere in Hangzhou and the platform is used to manage the evacuation planning. Experiments show that evacuation time increases linearly with the amount of evacuation demand in a certain range, and we draw the conclusion that balanced improvements on the whole road network should be made instead of only on the “bottlenecks”.

ACS Style

Wenwen Kang; Li Xie; Fenghua Zhu; Yisheng Lv; Gang Xiong; Bin Xi; 生吕 宜. Design and implementation of an Emergency Traffic Evacuation Management Platform for urban areas based on Eclipse RCP. Proceedings of 2013 IEEE International Conference on Vehicular Electronics and Safety 2013, 84 -88.

AMA Style

Wenwen Kang, Li Xie, Fenghua Zhu, Yisheng Lv, Gang Xiong, Bin Xi, 生吕 宜. Design and implementation of an Emergency Traffic Evacuation Management Platform for urban areas based on Eclipse RCP. Proceedings of 2013 IEEE International Conference on Vehicular Electronics and Safety. 2013; ():84-88.

Chicago/Turabian Style

Wenwen Kang; Li Xie; Fenghua Zhu; Yisheng Lv; Gang Xiong; Bin Xi; 生吕 宜. 2013. "Design and implementation of an Emergency Traffic Evacuation Management Platform for urban areas based on Eclipse RCP." Proceedings of 2013 IEEE International Conference on Vehicular Electronics and Safety , no. : 84-88.

Conference paper
Published: 01 July 2012 in Proceedings of the 10th World Congress on Intelligent Control and Automation
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Evacuation is an effective strategy to mitigate damage of man-made or natural disasters. Evacuation clearance time is one of the key indicators in evacuation planning and management. Evacuees' destination choice and route choice behavior are two crucial factors used to estimate evacuation clearance time. However, these two factors are viewed as constants in previous research, and the quantification of the impact of these two factors is lacking. In this paper, the authors report impact of variation of evacuees' destination choice and route choice behavior on evacuation clearance time. The impact analysis is done based on a case study by using an artificial transportation system platform called TransWorld. And the best values of evacuees' destination choice and route choice behavior are given, respectively. The computational experimental results illustrate that if evacuation managers adopt reasonable strategies to guide evacuees' destination choice and route choice, it can significantly reduce evacuation clearance time. The simulation methodology, computational results and discussion can be used for future emergency evacuation planning. This study also provides potentials of new emergency evacuation management and control strategies from the perspective of evacuee behavior.

ACS Style

Yisheng Lv; Fenghua Zhu; Gang Xiong; Qingming Yao; Songhang Chen; Peijun Ye; 生吕 宜; Zhu Fenghua; Xiong Gang; Yao Qingming; Chen Songhang; Ye Peijun. Impact of evacuee behavior on evacuation clearance time. Proceedings of the 10th World Congress on Intelligent Control and Automation 2012, 3520 -3525.

AMA Style

Yisheng Lv, Fenghua Zhu, Gang Xiong, Qingming Yao, Songhang Chen, Peijun Ye, 生吕 宜, Zhu Fenghua, Xiong Gang, Yao Qingming, Chen Songhang, Ye Peijun. Impact of evacuee behavior on evacuation clearance time. Proceedings of the 10th World Congress on Intelligent Control and Automation. 2012; ():3520-3525.

Chicago/Turabian Style

Yisheng Lv; Fenghua Zhu; Gang Xiong; Qingming Yao; Songhang Chen; Peijun Ye; 生吕 宜; Zhu Fenghua; Xiong Gang; Yao Qingming; Chen Songhang; Ye Peijun. 2012. "Impact of evacuee behavior on evacuation clearance time." Proceedings of the 10th World Congress on Intelligent Control and Automation , no. : 3520-3525.

Conference paper
Published: 01 April 2012 in Proceedings of 2012 9th IEEE International Conference on Networking, Sensing and Control
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Public traffic is required for the convenient travel, reducing traffic congestion and accidents, low-carbon, environmental protection, sustainable development, and traffic demand of giant sports and large business activities, etc. For public transport demand of the 16th Asian Games and the 2010 Asian Para Games held in Guangzhou in 2010, based on ACP approach, Parallel Public Transport Management And Control System (PPTMS) for Guangzhou Asian Games had been developed to support management decision of public transport. This system can help public transport managers to improve and enhance significantly the level of public transport management in Guangzhou, from experience-based formulation and manual implementation to scientific computation-based formulation and automatic implementation with intelligent systems, to guarantee the traffic demand effectively during the two Games.

ACS Style

Xisong Dong; Gang Xiong; Dong Fan; Fenghua Zhu; Yisheng Lv; 生吕 宜. Parallel public transport management and control system for Guangzhou Asian Games. Proceedings of 2012 9th IEEE International Conference on Networking, Sensing and Control 2012, 16 -21.

AMA Style

Xisong Dong, Gang Xiong, Dong Fan, Fenghua Zhu, Yisheng Lv, 生吕 宜. Parallel public transport management and control system for Guangzhou Asian Games. Proceedings of 2012 9th IEEE International Conference on Networking, Sensing and Control. 2012; ():16-21.

Chicago/Turabian Style

Xisong Dong; Gang Xiong; Dong Fan; Fenghua Zhu; Yisheng Lv; 生吕 宜. 2012. "Parallel public transport management and control system for Guangzhou Asian Games." Proceedings of 2012 9th IEEE International Conference on Networking, Sensing and Control , no. : 16-21.

Book chapter
Published: 01 January 2012 in Service Science, Management, and Engineering:
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ACS Style

Fenghua Zhu; Zhenjiang Li; Yisheng Lv; 生吕 宜. Evaluating Traffic Signal Control Systems Based on Artificial Transportation Systems. Service Science, Management, and Engineering: 2012, 117 -140.

AMA Style

Fenghua Zhu, Zhenjiang Li, Yisheng Lv, 生吕 宜. Evaluating Traffic Signal Control Systems Based on Artificial Transportation Systems. Service Science, Management, and Engineering:. 2012; ():117-140.

Chicago/Turabian Style

Fenghua Zhu; Zhenjiang Li; Yisheng Lv; 生吕 宜. 2012. "Evaluating Traffic Signal Control Systems Based on Artificial Transportation Systems." Service Science, Management, and Engineering: , no. : 117-140.

Conference paper
Published: 01 October 2011 in 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)
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The ACP (Artificial societies, Computational experiments and Parallel execution) approach has provided us an opportunity to look into new methods in addressing transportation problems from new perspectives. In this paper, we present our works and results of applying ACP approach in modeling and analyzing transportation system, especially carrying out computational experiments based on artificial transportation systems. Two aspects in the modeling process are analyzed. The first is growing artificial transportation system from bottom up using agent-based technologies. The second is modeling environment impacts in simple-is-consistent principle. Finally, two computational experiments are carried out on one specific ATS, Jinan ATS, and numerical results are presented to illustrate the applications of our method.

ACS Style

Fenghua Zhu; Fei-Yue Wang; Runmei Li; Yisheng Lv; Songhang Chen; 生吕 宜. Modeling and analyzing transportation systems based on ACP approach. 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC) 2011, 2136 -2141.

AMA Style

Fenghua Zhu, Fei-Yue Wang, Runmei Li, Yisheng Lv, Songhang Chen, 生吕 宜. Modeling and analyzing transportation systems based on ACP approach. 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC). 2011; ():2136-2141.

Chicago/Turabian Style

Fenghua Zhu; Fei-Yue Wang; Runmei Li; Yisheng Lv; Songhang Chen; 生吕 宜. 2011. "Modeling and analyzing transportation systems based on ACP approach." 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC) , no. : 2136-2141.