摘要 :
With the large-scale implementation of high-speed rail (HSR) network, China has entered in a new era
features with high-speed rail transportation. The launching of this new transportation infrastructure not
only offers a new opt...
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With the large-scale implementation of high-speed rail (HSR) network, China has entered in a new era
features with high-speed rail transportation. The launching of this new transportation infrastructure not
only offers a new option for travelers’ mode choice, but also influence, or even generate, the
redistribution of demographic and economic activities. As has been observed over the past several
years, the impact of HSR is huge. However, few quantitative studies have been conducted to measure
this impact. As a new attempt for understanding the HSR network, this paper describes an
accessibility analysis to evaluate the impacts of China’s HSR network. Weighted average travel times
and travel costs, contour measure, and potential accessibility are used as indicators of accessibility at
the macroscopic level. Forty nine major cities in the HSR network are chosen for the accessibility
analysis. Accessibility quantification and spatial distribution analysis for the study cities are performed
on the Geographical Information System (GIS) platform. Accessibilities associated with varying
availabilities of HSR, conventional rail, and airline are estimated and compared. The selected
indicators and computational methods are found effective in evaluating the accessibility impacts of
HSR from different conceptualizations and perspectives. They also offer complementary information
on accessibility capacity to the study cities via HSR network.
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摘要 :
With the large-scale implementation of high-speed rail (HSR) network, China has entered in a new era
features with high-speed rail transportation. The launching of this new transportation infrastructure not
only offers a new optio...
展开
With the large-scale implementation of high-speed rail (HSR) network, China has entered in a new era
features with high-speed rail transportation. The launching of this new transportation infrastructure not
only offers a new option for travelers’ mode choice, but also influence, or even generate, the
redistribution of demographic and economic activities. As has been observed over the past several
years, the impact of HSR is huge. However, few quantitative studies have been conducted to measure
this impact. As a new attempt for understanding the HSR network, this paper describes an
accessibility analysis to evaluate the impacts of China’s HSR network. Weighted average travel times
and travel costs, contour measure, and potential accessibility are used as indicators of accessibility at
the macroscopic level. Forty nine major cities in the HSR network are chosen for the accessibility
analysis. Accessibility quantification and spatial distribution analysis for the study cities are performed
on the Geographical Information System (GIS) platform. Accessibilities associated with varying
availabilities of HSR, conventional rail, and airline are estimated and compared. The selected
indicators and computational methods are found effective in evaluating the accessibility impacts of
HSR from different conceptualizations and perspectives. They also offer complementary information
on accessibility capacity to the study cities via HSR network.
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摘要 :
This paper introduces a novel vehicle detection method combined with probability voting based hypothesis generation (HG) and SVM based hypothesis verification (HV) specialized for the complex background airborne traffic video, hi ...
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This paper introduces a novel vehicle detection method combined with probability voting based hypothesis generation (HG) and SVM based hypothesis verification (HV) specialized for the complex background airborne traffic video, hi HG stage, a statistic based road area extraction method is applied and the lane marks are eliminated. Remained areas are clustered, and then the canny algorithm is performed to detect edges in clustered areas. A voting strategy is designed to detect rectangle objects in the scene. In HV stage, every possible vehicle area is rotated to align the vehicle along the vertical direction, and the vertical and horizontal gradients of them are calculated. SVM is adopted to classify vehicle and non-vehicle. The proposed method has been applied to several traffic scenes, and the experiment results show it's effective and veracious for the vehicle detection.
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摘要 :
This paper introduces a novel vehicle detection method combined with probability voting based hypothesis generation (HG) and SVM based hypothesis verification (HV) specialized for the complex background airborne traffic video, hi ...
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This paper introduces a novel vehicle detection method combined with probability voting based hypothesis generation (HG) and SVM based hypothesis verification (HV) specialized for the complex background airborne traffic video, hi HG stage, a statistic based road area extraction method is applied and the lane marks are eliminated. Remained areas are clustered, and then the canny algorithm is performed to detect edges in clustered areas. A voting strategy is designed to detect rectangle objects in the scene. In HV stage, every possible vehicle area is rotated to align the vehicle along the vertical direction, and the vertical and horizontal gradients of them are calculated. SVM is adopted to classify vehicle and non-vehicle. The proposed method has been applied to several traffic scenes, and the experiment results show it's effective and veracious for the vehicle detection.
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摘要 :
Smart IC-card has been widely used in fare payment systems of public transport, which produces a large number of ticket checking records and spa-tiotemporal trajectory information. Accurately predicting passengers' travel stations...
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Smart IC-card has been widely used in fare payment systems of public transport, which produces a large number of ticket checking records and spa-tiotemporal trajectory information. Accurately predicting passengers' travel stations based on IC-card data plays an important role in intelligent transportation. However, incomplete IC-Card transaction records are widely existing. The IC-card not only does not record the actual boarding stations but also lacks the information of alighting stations because passengers do not need to swipe card when they get off. Therefore, it is difficult to construct the actual passenger travel link, which makes it challenging to predict alighting stations accurately. Targeting on this challenge, we propose a "Boarding Cluster to Alighting Station" alighting station prediction model (BCTAS) by condition hypothesis. First, the model analyzes the travel characteristics of passengers' public transport. Second, the smart IC-card transaction records and map-matching algorithm are used to construct the mixed boarding station link. Third, the model performs the station clustering and cluster expansion to merge the same name station and the nearest station into a cluster, and further constructs the mixed boarding cluster link. Fourth, a Variable Order Markov Model that named Prediction by Partial Match (PPM) is adopted to predict the mixed boarding cluster link and then predict the boarding station. Fifth, the model infers the prediction precision of the alighting cluster and alighting station based on the condition hypothesis. Finally, our approach was evaluated by using the public transport data obtained in Shenzhen city, China. The results show that (a) with the increase of training data, the precision of the model is gradually enhanced, (b) by using the mixed boarding cluster link, the prediction precision of the boarding cluster and boarding station could reach 88.05% and 84.52% respectively, (c) Based on the condition hypothesis, it can be inferred that the lower limit of the prediction precision of the alighting cluster and alighting station is 78.09% and 74.96%, respectively.
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摘要 :
Smart IC-card has been widely used in fare payment systems of public transport, which produces a large number of ticket checking records and spa-tiotemporal trajectory information. Accurately predicting passengers' travel stations...
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Smart IC-card has been widely used in fare payment systems of public transport, which produces a large number of ticket checking records and spa-tiotemporal trajectory information. Accurately predicting passengers' travel stations based on IC-card data plays an important role in intelligent transportation. However, incomplete IC-Card transaction records are widely existing. The IC-card not only does not record the actual boarding stations but also lacks the information of alighting stations because passengers do not need to swipe card when they get off. Therefore, it is difficult to construct the actual passenger travel link, which makes it challenging to predict alighting stations accurately. Targeting on this challenge, we propose a "Boarding Cluster to Alighting Station" alighting station prediction model (BCTAS) by condition hypothesis. First, the model analyzes the travel characteristics of passengers' public transport. Second, the smart IC-card transaction records and map-matching algorithm are used to construct the mixed boarding station link. Third, the model performs the station clustering and cluster expansion to merge the same name station and the nearest station into a cluster, and further constructs the mixed boarding cluster link. Fourth, a Variable Order Markov Model that named Prediction by Partial Match (PPM) is adopted to predict the mixed boarding cluster link and then predict the boarding station. Fifth, the model infers the prediction precision of the alighting cluster and alighting station based on the condition hypothesis. Finally, our approach was evaluated by using the public transport data obtained in Shenzhen city, China. The results show that (a) with the increase of training data, the precision of the model is gradually enhanced, (b) by using the mixed boarding cluster link, the prediction precision of the boarding cluster and boarding station could reach 88.05% and 84.52% respectively, (c) Based on the condition hypothesis, it can be inferred that the lower limit of the prediction precision of the alighting cluster and alighting station is 78.09% and 74.96%, respectively.
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摘要 :
Inspired by the outstanding performance of sparse coding in applications of image denoising, restoration, classification, etc., we propose an adaptive sparse coding method for painting style analysis that is traditionally carried ...
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Inspired by the outstanding performance of sparse coding in applications of image denoising, restoration, classification, etc., we propose an adaptive sparse coding method for painting style analysis that is traditionally carried out by art connoisseurs and experts. Significantly improved over previous sparse coding methods, which heavily rely on the comparison of query paintings, our method is able to determine the authenticity of a single query painting based on estimated decision boundary. Firstly, discriminative patches containing the most representative characteristics of the given authentic samples are extracted via exploiting the statistical information of their representation on the DCT basis. Subsequently, the strategy of adaptive sparsity constraint which assigns higher sparsity weight to the patch with higher discriminative level is enforced to make the dictionary trained on such patches more exclusively adaptive to the authentic samples than via previous sparse coding algorithms. Relying on the learnt dictionary, the query painting can be authenticated if both better denoising performance and higher sparse representation are obtained, otherwise it should be denied. Extensive experiments on impressionist style paintings demonstrate efficiency and effectiveness of our method.
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摘要 :
Inspired by the outstanding performance of sparse coding in applications of image denoising, restoration, classification, etc., we propose an adaptive sparse coding method for painting style analysis that is traditionally carried ...
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Inspired by the outstanding performance of sparse coding in applications of image denoising, restoration, classification, etc., we propose an adaptive sparse coding method for painting style analysis that is traditionally carried out by art connoisseurs and experts. Significantly improved over previous sparse coding methods, which heavily rely on the comparison of query paintings, our method is able to determine the authenticity of a single query painting based on estimated decision boundary. Firstly, discriminative patches containing the most representative characteristics of the given authentic samples are extracted via exploiting the statistical information of their representation on the DCT basis. Subsequently, the strategy of adaptive sparsity constraint which assigns higher sparsity weight to the patch with higher discriminative level is enforced to make the dictionary trained on such patches more exclusively adaptive to the authentic samples than via previous sparse coding algorithms. Relying on the learnt dictionary, the query painting can be authenticated if both better denoising performance and higher sparse representation are obtained, otherwise it should be denied. Extensive experiments on impressionist style paintings demonstrate efficiency and effectiveness of our method.
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摘要 :
Inspired by the outstanding performance of sparse coding in applications of image denoising, restoration, classification, etc., we propose an adaptive sparse coding method for painting style analysis that is traditionally carried ...
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Inspired by the outstanding performance of sparse coding in applications of image denoising, restoration, classification, etc., we propose an adaptive sparse coding method for painting style analysis that is traditionally carried out by art connoisseurs and experts. Significantly improved over previous sparse coding methods, which heavily rely on the comparison of query paintings, our method is able to determine the authenticity of a single query painting based on estimated decision boundary. Firstly, discriminative patches containing the most representative characteristics of the given authentic samples are extracted via exploiting the statistical information of their representation on the DCT basis. Subsequently, the strategy of adaptive sparsity constraint which assigns higher sparsity weight to the patch with higher discriminative level is enforced to make the dictionary trained on such patches more exclusively adaptive to the authentic samples than via previous sparse coding algorithms. Relying on the learnt dictionary, the query painting can be authenticated if both better denoising performance and higher sparse representation are obtained, otherwise it should be denied. Extensive experiments on impressionist style paintings demonstrate efficiency and effectiveness of our method.
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摘要 :
In this paper, we presented a ROad SEgment-based emission model (ROSE) for transportation Green House Gas
(GHG) emissions estimation. The objective of this study is to provide a framework for quickly estimating
traffic-related G...
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In this paper, we presented a ROad SEgment-based emission model (ROSE) for transportation Green House Gas
(GHG) emissions estimation. The objective of this study is to provide a framework for quickly estimating
traffic-related GHG emissions and analyzing its spatiotemporal distribution and variation based on real-time
traffic data. The model is carried out a combination of Intelligent Transport System (ITS) technology, Geographic
Information System (GIS) technology, and the International Vehicle Emission Model (IVE). In the ROSE model,
the ITS' floating car data (FCD) and loop detector data (LDD) are used as the model input. The IVE model is used
for providing microscopic vehicle emission rates; and GIS is not only used as a database exchanger, but also used
as a computation and a visualization tool in ROSE model. This paper will discuss two fundamental works
conducted in our ROSE model research project: 1) ITS real-time traffic data collection and geographic-related
data unification; 2) vehicle driving activity generation & road-segment based CO_2 emission computation. To
demonstrate the effectiveness of the ROSE model, we applied this model in a case study for estimating the daily
CO_2 emissions generated from the highway transportation of Beijing, China during the year of 2008. The result
shows that the ROSE model can provide micro level highly accurate and real-time GHG emission for the whole
urban area such as Beijing city.
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