Most traffic authorities across the US usually collect high-resolution (10 Hz) loop detector and signal state data and video data. The multiple modalities of data that are readily available can be utilized for better ...
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ISBN:
(纸本)9789897586521
Most traffic authorities across the US usually collect high-resolution (10 Hz) loop detector and signal state data and video data. The multiple modalities of data that are readily available can be utilized for better traffic operations management and improving safety. In this work, we show that the fusion of widely deployed loop detector data with trajectory information collected through video cameras can augment intersection safety and operational efficiency analysis. The additional information that can be extracted from the object's (vehicle and pedestrian) trajectory derived from video data when fused with signal state data leads to several interesting safety analyses. dataanalysis shows a significant variance in turn-movement counts, pedestrian behaviors, vehicle composition, etc., temporally (hour-of-day, day-of-week, etc.) and spatially (approach-wise). We present a simulation-based approach for customizing signal timing plans based on the traffic behavior at the intersections at various times. When used to drive simulations in demand generation, we show that the fused data calibrating the simulation parameters can lead to potential improvements in existing signal timing plans that match reality and can greatly help improve intersection safety and operational efficiency by providing planners with data-driven insights.
Stroke patients discharge planning is a complex task that could be carried out by the use of a suitable decision support system. Such a platform should be based on unsupervised machine learning algorithms to reach the...
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ISBN:
(纸本)9789897584909
Stroke patients discharge planning is a complex task that could be carried out by the use of a suitable decision support system. Such a platform should be based on unsupervised machine learning algorithms to reach the best results. More specifically, in this kind of prediction task clustering learning algorithms seem to perform better than the other unsupervised models. These algorithms are able to independently subdivide the treated clinical cases into groups, and they can serve to discover interesting correlations among the clinical variables taken into account and to improve the prediction accuracy of the treatment outcome. This work aims to compare the prediction accuracy of a particular clustering learning algorithm, the Growing Neural Gas, with the prediction accuracy of other supervised and unsupervised algorithms used in stroke patients discharge planning. This machine learning model is also able to accurately identify the input space topology. In other words it is characterized by the ability to independently select a subset of attributes to be taken into consideration in order to correctly perform any predictive task.
Air cargo transportation is an essential mode of cargo transportation. How to distribute air cargo into flights better is an important operational problem. In this paper, air cargo data collected by CAAC (Civil Aviati...
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Air cargo transportation is an essential mode of cargo transportation. How to distribute air cargo into flights better is an important operational problem. In this paper, air cargo data collected by CAAC (Civil Aviation Administration of China) during the first three months of 2018 are analyzed. We find that the available capacity for cargo transportation shows great variations, and the cargo compartment utilization rates of flights are heterogeneously distributed. Next, a data-driven air cargo redistribution model is developed based on multiple programming (MP). The proposed model can effectively transport high-priority goods and balance cargo compartment utilization rates of flights. In addition, the proposed model framework can provide a new solution to multi-objective or multi-stage optimization problems.
Traffic signal control is an effective way of solving urban traffic problems by providing appropriate signal control plans for various intersections. Essentially, the aim of Traffic Signal Control is to find the best ...
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ISBN:
(纸本)9781467365963
Traffic signal control is an effective way of solving urban traffic problems by providing appropriate signal control plans for various intersections. Essentially, the aim of Traffic Signal Control is to find the best matching timing plans to current traffic conditions. Inspired by recommendation technology, we regard traffic conditions as users, timing plans as items, and traffic indicators like delay time are regarded as the ratings that users give to items. By means of Content-based Recommendation technology and k-Nearest Neighbor method in Recommendation Systems, we first find the similar traffic conditions according to the characteristics of traffic conditions. Then the matching degree between current traffic conditions and various timing plans can be predicted by analyzing the history data of selected similar traffic conditions. What's more, Artificial Transportation Systems method was applied to recommend and sort the timing plans for various traffic conditions in this paper. With normalized Discounted Cumulative Gain, which is a measure of ranking quality, was chosen as the performance indicator, we conducted the experiments in Paramics. The results showed that the strategies based on our method outperform the classic Webster method.
Traffic signal control is an effective way of solving urban traffic problems by providing appropriate signal control plans for various intersections. Essentially, the aim of Traffic Signal Control is to find the best ...
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ISBN:
(纸本)9781467365970
Traffic signal control is an effective way of solving urban traffic problems by providing appropriate signal control plans for various intersections. Essentially, the aim of Traffic Signal Control is to find the best matching timing plans to current traffie conditions. Inspired by recommendation technology, we regard traffic conditions as users, timing plans as items, and traffic indicators like delay time are regarded as the ratings that users give to items. By means of Content-based Recommendation technology and k-Nearest Neighbor method in Recommendation Systems, we first find the similar traffic conditions according to the characteristics of traffic conditions. Then the matching degree between current traffic conditions and various timing plans can be predicted by analyzing the history data of selected similar traffic conditions. What's more, Artificial Transportation Systems method was applied to recommend and sort the timing plans for various traffic conditions in this paper. With normalized Discounted Cumulative Gain, which is a measure of ranking quality, was chosen as the performance indicator, we conducted the experiments in Paramics. The results showed that the strategies based on our method outperform the classic Webster method.
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