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A GTFS data acquisition and processing framework and its application t by Jianqing Wu, Bo Du et al

With advanced artificial intelligence and deep learning techniques, a growing number of data sources are playing more and more critical roles in planning and operating transportation services. The General Transit Feed Specification (GTFS), with standard open-source data in both static and real-time formats, is being widely used in public transport planning and operation management. However, compared to other extensively studied data sources such as smart card data and GPS trajectory data, the GTFS data lacks proper investigation yet. Utilization of the GTFS data is challenging for both transport planners and researchers due to its difficulty and complexity of understanding, processing, and leveraging the raw data. In this paper, a GTFS data acquisition and processing framework is proposed to offer an efficient and effective benchmark tool for converting and fusing the GTFS data to a ready-to-use format. To validate and test the proposed framework, a multivariate multistep Long Short-Te

New south walesGeneral transit feed specificationLong short term memoryAdata fusionDelay predictionGeneral transit feed specificationLong short term memoryRain delay

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Ini Lelaki yang Akan Menikahi Amanda Manopo, Nasibnya Dengan Arya Saloka di Ikatan Cinta Disorot
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Predicting British railway delays using artificial intelligence

Credit: Debra Larson, The Grainger College of Engineering Over the past 20 years, the number of passengers traveling on British train networks has almost doubled to 1.7 billion annually. With numbers like that it s clear how much people rely on rail service in Great Britain, and how many disgruntled patrons there would be when delays occur. A recent study used real British Railway data and an artificial intelligence model to improve the ability to predict delays in railway networks. We wanted to explore this problem using our experience with graph neural networks, said Huy Tran, an aerospace engineering faculty member at the University of Illinois Urbana-Champaign. These are a specific class of artificial intelligence models that focus on data modeled as a graph, where a set of nodes are connected by edges.

United statesUnited kingdomUniversity of illinoisGreat britainJacobsw heglundHuy tranThuyt tranPanukorn taleongpongTemporal graph convolutional networksTemporal graph convolutional networkRailway delay predictionBritish railwayInternational conference on intelligent transportation systemsUniversity of illinois urbana champaign instituteZhejiang universityUniversity of illinois urbana champaign