The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Zeqiang Chen - One of the best experts on this subject based on the ideXlab platform.
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an efficient method of sharing mass spatio temporal trajectory data based on cloudera impala for Traffic distribution mapping in an urban city
Sensors, 2016Co-Authors: Lianjie Zhou, Nengcheng Chen, Sai Yuan, Zeqiang ChenAbstract:The efficient sharing of spatio-temporal trajectory data is important to understand Traffic congestion in mass data. However, the data volumes of bus networks in urban cities are growing rapidly, reaching daily volumes of one hundred million datapoints. Accessing and retrieving mass spatio-temporal trajectory data in any field is hard and inefficient due to limited computational capabilities and incomplete data organization mechanisms. Therefore, we propose an optimized and efficient spatio-temporal trajectory data retrieval method based on the Cloudera Impala query engine, called ESTRI, to enhance the efficiency of mass data sharing. As an excellent query tool for mass data, Impala can be applied for mass spatio-temporal trajectory data sharing. In ESTRI we extend the spatio-temporal trajectory data retrieval function of Impala and design a suitable data partitioning method. In our experiments, the Taiyuan BeiDou (BD) bus network is selected, containing 2300 buses with BD positioning sensors, producing 20 million records every day, resulting in two difficulties as described in the Introduction section. In addition, ESTRI and MongoDB are applied in experiments. The experiments show that ESTRI achieves the most efficient data retrieval compared to retrieval using MongoDB for data volumes of fifty million, one hundred million, one hundred and fifty million, and two hundred million. The performance of ESTRI is approximately seven times higher than that of MongoDB. The experiments show that ESTRI is an effective method for retrieving mass spatio-temporal trajectory data. Finally, bus distribution mapping in Taiyuan city is achieved, describing the buses density in different regions at different times throughout the day, which can be applied in future studies of transport, such as Traffic scheduling, Traffic Planning and Traffic behavior management in intelligent public transportation systems.
Lily Elefteriadou - One of the best experts on this subject based on the ideXlab platform.
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multiclass Traffic assignment problem with flow dependent passenger car equivalent value of trucks
Transportation Research Record, 2017Co-Authors: Gustavo Riente De Andrade, Zhibin Chen, Lily ElefteriadouAbstract:This paper develops and analyzes a multiclass Traffic assignment model considering the flow-dependent passenger car equivalent (PCE) value of trucks based on the latest Highway Capacity Manual (HCM, 6th edition) and explores the properties of the proposed model to provide guidance on related Planning applications. HCM discrete values of truck PCEs are fitted by power functions for combinations of link grades and lengths, which have been found to produce high coefficients of determination (R2) in all cases. With the established fitting functions, the multiclass Traffic assignment problem is formulated as a variational inequality problem and solved by an efficient method. The equilibrium link flow distribution is proved to exist but may not be unique. Numerical examples and discussions are presented to demonstrate the variance of the link flow distributions and the effect of such nonuniqueness on Traffic Planning applications. Several approaches are then provided to obtain the best range of solutions accord...
Houbing Song - One of the best experts on this subject based on the ideXlab platform.
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optimization of real time Traffic network assignment based on iot data using dbn and clustering model in smart city
Future Generation Computer Systems, 2017Co-Authors: Jiachen Yang, Yafang Wang, Bin Jiang, Zhihan Lv, Houbing SongAbstract:Abstract With the rapid development of the information age, smart city has gradually become the mainstream of urban construction. Dynamic transportation assignment has attracted more interest in the smart city construction under the new era of the Internet of things (IoT) because the urban road Traffic is the heart of many problems in many fields, such as in the case of city congestion and processing center Planning system. In this paper, we analyzed the processing center’s economic indexes and optimized the dynamic transportation network assignment based on continuous big IoT input database, and a high performance computing model is proposed for the dynamic Traffic Planning. Specifically, while the previous methods exploited the geographical information system (GIS) or K -means separately, the proposed transportation Planning is based on the real-time IoT and GIS data, which is processed by DBN and K -means to make the final solution close to the practice and meet the requirements of high performance computing and economic cost. which is regarded as the key target index. Moreover, considering the large data characteristic of real-time online stream, the deep belief network (DBN) model is built to preprocess the data to improve the clustering effect of the K -means. This study works on the example case of hotel service centers problem in Tianjin to evaluate the optimal dynamic Traffic network Planning result. The experiment test has proved that based on the performance of super high computing, the model is precisely helpful for the optimal Planning of Traffic network under real time mass data situation and low cost, and promoting the construction and development of the smart city.
Lianjie Zhou - One of the best experts on this subject based on the ideXlab platform.
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an efficient method of sharing mass spatio temporal trajectory data based on cloudera impala for Traffic distribution mapping in an urban city
Sensors, 2016Co-Authors: Lianjie Zhou, Nengcheng Chen, Sai Yuan, Zeqiang ChenAbstract:The efficient sharing of spatio-temporal trajectory data is important to understand Traffic congestion in mass data. However, the data volumes of bus networks in urban cities are growing rapidly, reaching daily volumes of one hundred million datapoints. Accessing and retrieving mass spatio-temporal trajectory data in any field is hard and inefficient due to limited computational capabilities and incomplete data organization mechanisms. Therefore, we propose an optimized and efficient spatio-temporal trajectory data retrieval method based on the Cloudera Impala query engine, called ESTRI, to enhance the efficiency of mass data sharing. As an excellent query tool for mass data, Impala can be applied for mass spatio-temporal trajectory data sharing. In ESTRI we extend the spatio-temporal trajectory data retrieval function of Impala and design a suitable data partitioning method. In our experiments, the Taiyuan BeiDou (BD) bus network is selected, containing 2300 buses with BD positioning sensors, producing 20 million records every day, resulting in two difficulties as described in the Introduction section. In addition, ESTRI and MongoDB are applied in experiments. The experiments show that ESTRI achieves the most efficient data retrieval compared to retrieval using MongoDB for data volumes of fifty million, one hundred million, one hundred and fifty million, and two hundred million. The performance of ESTRI is approximately seven times higher than that of MongoDB. The experiments show that ESTRI is an effective method for retrieving mass spatio-temporal trajectory data. Finally, bus distribution mapping in Taiyuan city is achieved, describing the buses density in different regions at different times throughout the day, which can be applied in future studies of transport, such as Traffic scheduling, Traffic Planning and Traffic behavior management in intelligent public transportation systems.
Senzhang Wang - One of the best experts on this subject based on the ideXlab platform.
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spatial temporal incidence dynamic graph neural networks for Traffic flow forecasting
Information Sciences, 2020Co-Authors: Hao Peng, Hongfei Wang, Zakirul Alam Bhuiyan, Jianwei Liu, Lihong Wang, Zeyu Yang, Senzhang WangAbstract:Abstract Accurate and real-time Traffic passenger flows forecasting at transportation hubs, such as subway/bus stations, is a practical application and of great significance for urban Traffic Planning, control, guidance, etc. Recently deep learning based methods are promised to learn the spatial-temporal features from high non-linearity and complexity of Traffic flows. However, it is still very challenging to handle so much complex factors including the urban transportation network topological structures and the laws of Traffic flows with spatial and temporal dependencies. Considering both the static hybrid urban transportation network structures and dynamic spatial-temporal relationships among stations from historical Traffic passenger flows, a more effective and fine-grained spatial-temporal features learning framework is necessary. In this paper, we propose a novel spatial-temporal incidence dynamic graph neural networks framework for urban Traffic passenger flows prediction. We first model dynamic Traffic station relationships over time as spatial-temporal incidence dynamic graph structures based on historically Traffic passenger flows. Then we design a novel dynamic graph recurrent convolutional neural network, namely Dynamic-GRCNN, to learn the spatial-temporal features representation for urban transportation network topological structures and transportation hubs. To fully utilize the historical passenger flows, we sample the short-term, medium-term and long-term historical Traffic data in training, which can capture the periodicity and trend of the Traffic passenger flows at different stations. We conduct extensive experiments on different types of Traffic passenger flows datasets including subway, taxi and bus flows in Beijing. The results show that the proposed Dynamic-GRCNN effectively captures comprehensive spatial-temporal correlations significantly and outperforms both traditional and deep learning based urban Traffic passenger flows prediction methods.