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Partitioned Graph Convolution Using Adversarial and Regression Networks for Road Travel Speed Prediction [article]

Jakob Meldgaard Kjær, Lasse Kristensen, Mads Alberg Christensen
<span title="2021-02-26">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To this end, we propose a framework for predicting road segment travel speed histograms for dataless edges, based on a latent representation generated by an adversarially regularized convolutional network  ...  We apply a partitioning algorithm to divide the graph into dense subgraphs, and then train a model for each subgraph to predict speed histograms for the nodes.  ...  Figure 1 . 1 Framework architecture for Partitioned Graph Convolution Using Adversarial and Regression Networks for Road Travel Speed Prediction.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.00067v1">arXiv:2103.00067v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/o3urn7sovzhmdet3yirbn7otwy">fatcat:o3urn7sovzhmdet3yirbn7otwy</a> </span>
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Graph Neural Network for Traffic Forecasting: A Survey [article]

Weiwei Jiang, Jiayun Luo
<span title="2022-02-22">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this survey, we review the rapidly growing body of research using different graph neural networks, e.g. graph convolutional and graph attention networks, in various traffic forecasting problems, e.g  ...  . road traffic flow and speed forecasting, passenger flow forecasting in urban rail transit systems, and demand forecasting in ride-hailing platforms.  ...  The traffic speed and flow of the entire PeMS dataset with 11,160 traffic sensor locations are predicted simultaneously in [28] , using a graph-partitioning method that decomposes a large highway network  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2101.11174v4">arXiv:2101.11174v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/txrrk6yia5dcvcamabhqahsrni">fatcat:txrrk6yia5dcvcamabhqahsrni</a> </span>
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Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities [article]

Yihong Tang, Ao Qu, Andy H.F. Chow, William H.K. Lam, S.C. Wong, Wei Ma
<span title="2022-02-08">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To the best of our knowledge, we are the first to employ adversarial multi-domain adaptation for network-wide traffic forecasting problems.  ...  Specifically, we leverage the graph representation learning and adversarial domain adaptation techniques to learn the domain-invariant node embeddings, which are further incorporated to model the temporal  ...  ACKNOWLEDGMENTS This study was supported by the Research Impact Fund for "Reliabilitybased Intelligent Transportation Systems in Urban Road Network with Uncertainty" from the Research Grants Council of  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.03630v1">arXiv:2202.03630v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wao6svsnyzfnlijzraphy62rie">fatcat:wao6svsnyzfnlijzraphy62rie</a> </span>
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How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey [article]

Jiexia Ye, Juanjuan Zhao, Kejiang Ye, Chengzhong Xu
<span title="2020-10-11">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Traditionally, convolution neural networks (CNNs) are utilized to model spatial dependency by decomposing the traffic network as grids. However, many traffic networks are graph-structured in nature.  ...  More and more works combine GNNs with other deep learning techniques to construct an architecture dealing with various challenges in a complex traffic task, where GNNs are responsible for extracting spatial  ...  ACKNOWLEDGMENT The authors would like to thank anonymous reviewers for their valuable comments. This work is supported in part by the National Key  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.11691v6">arXiv:2005.11691v6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uiso5cg6cvhvnfmtisvuxapfqi">fatcat:uiso5cg6cvhvnfmtisvuxapfqi</a> </span>
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Short-term Traffic Prediction with Deep Neural Networks: A Survey [article]

Kyungeun Lee, Moonjung Eo, Euna Jung, Yoonjin Yoon, Wonjong Rhee
<span title="2020-08-28">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
involved. 2) We briefly explain a wide range of DNN techniques from the earliest networks, including Restricted Boltzmann Machines, to the most recent, including graph-based and meta-learning networks  ...  public traffic datasets that are popular and can be used as the standard benchmarks.  ...  The graph weights are also modified using simple partition filters.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2009.00712v1">arXiv:2009.00712v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rvcz235ugbahhjglkjgvwgxks4">fatcat:rvcz235ugbahhjglkjgvwgxks4</a> </span>
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Short-term Traffic Prediction with Deep Neural Networks: A Survey

Kyungeun Lee, Moonjung Eo, Euna Jung, Yoonjin Yoon, Wonjong Rhee
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
The graph weights are also modified using simple partition filters.  ...  GRAPH CONVOLUTIONAL NETWORKS (FOURTH GENERATION)(a) 2D convolution.(b) Graph convolution. FIGURE 10 . 10 Typical 2D convolution and a graph convolution. Best viewed in color. (a) 2D convolution.  ...  Her most recent efforts involved modeling short-term traffic prediction problem with graph neural network, as well as spatial contiguity extraction of urban 3D airspace for autonomous flight routing for  ... 
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Deep Learning for Spatio-Temporal Data Mining: A Survey [article]

Senzhang Wang, Jiannong Cao, Philip S. Yu
<span title="2019-06-24">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Recently, with the advances of deep learning techniques, deep leaning models such as convolutional neural network (CNN) and recurrent neural network (RNN) have enjoyed considerable success in various machine  ...  predictive learning, representation learning, anomaly detection and classification.  ...  RNN and LSTM are widely used for time series ST data prediction. [82] integrated LSTM and sequence to sequence model to predict the traffic speed of a road segment.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1906.04928v2">arXiv:1906.04928v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4zrdtgkvirfuniq3rb2gl7ohpy">fatcat:4zrdtgkvirfuniq3rb2gl7ohpy</a> </span>
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A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Haitao Yuan, Guoliang Li
<span title="2021-01-23">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4pfqfq76vvcxljfl7mvdl37n5q" style="color: black;">Data Science and Engineering</a> </i> &nbsp;
., intelligent traffic light) makes our travel more convenient and efficient.  ...  Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/forecasting).  ...  ., intersection flows, road speed and travel time) are related to road networks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s41019-020-00151-z">doi:10.1007/s41019-020-00151-z</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nnnnxnpo3bgk3l4hpr7kk2n4xa">fatcat:nnnnxnpo3bgk3l4hpr7kk2n4xa</a> </span>
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AI and Deep Learning for Urban Computing [chapter]

Senzhang Wang, Jiannong Cao
<span title="">2021</span> <i title="Springer Singapore"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b4phtjt3hvdyfb45mr5maz4mlq" style="color: black;">The Urban Book Series</a> </i> &nbsp;
For each application, we summarize major research challenges and review previous work that uses AI techniques to address them.  ...  In this chapter, we introduce the challenges, methodologies, and applications of AI techniques for urban computing.  ...  To model the spatial correlations among the road links of a transportation network, a graph convolution network (GCN) is used in both the generator and the discriminator for feature learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-981-15-8983-6_43">doi:10.1007/978-981-15-8983-6_43</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uq7j3hvsvzfl5lq33omx64un3i">fatcat:uq7j3hvsvzfl5lq33omx64un3i</a> </span>
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A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

Florin Leon, Marius Gavrilescu
<span title="2021-03-19">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ye33srllvnanjouxn4tmrfgjsq" style="color: black;">Mathematics</a> </i> &nbsp;
Approaches based on deep neural networks and others, especially stochastic techniques, are reported.  ...  This paper provides a literature review of some of the most important concepts, techniques, and methodologies used within autonomous car systems.  ...  Asachi" Technical University of Iaşi and Continental AG.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/math9060660">doi:10.3390/math9060660</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qvikrr32tzd7fnjzs22u3ago4m">fatcat:qvikrr32tzd7fnjzs22u3ago4m</a> </span>
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Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method

Sujae Kim, Sangho Choo, Gyeongjae Lee, Sanghun Kim
<span title="2022-02-23">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/oglosmy3gbhuzobyjit4qalakq" style="color: black;">Sustainability</a> </i> &nbsp;
To improve the level of service, relocation strategies for shared e-scooters are needed, and it is important to predict the demand for their use within a given area.  ...  Therefore, this study aimed to develop a demand prediction model for the use of shared e-scooters.  ...  Acknowledgments: The authors are thankful to the four reviewers and academic editor for their valuable comments on this paper. Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/su14052564">doi:10.3390/su14052564</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5rlyip5tq5aofbemdubunxpb7u">fatcat:5rlyip5tq5aofbemdubunxpb7u</a> </span>
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A Comprehensive Survey on Graph Neural Networks [article]

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu
<span title="2019-12-04">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and  ...  We further discuss the applications of graph neural networks across various domains and summarize the open source codes, benchmark data sets, and model evaluation of graph neural networks.  ...  For example, in a traffic network, the travel time between two roads may depend on their current traffic conditions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.00596v4">arXiv:1901.00596v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xxuchvawonhczay2sgjgzw5wgu">fatcat:xxuchvawonhczay2sgjgzw5wgu</a> </span>
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2019 Index IEEE Transactions on Intelligent Transportation Systems Vol. 20

<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/in6o6x6to5e2dls4y2ff52dy6u" style="color: black;">IEEE transactions on intelligent transportation systems (Print)</a> </i> &nbsp;
., and Ma, J  ...  ., +, TITS May 2019 1739-1748 Real-Time Traffic Speed Estimation With Graph Convolutional Generative Autoencoder.  ...  ., +, TITS May 2019 1669-1682 Taxi-Based Mobility Demand Formulation and Prediction Using Condi- tional Generative Adversarial Network-Driven Learning Approaches.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tits.2020.2966388">doi:10.1109/tits.2020.2966388</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xkvww7uabzhlzgfz3yiecyb75y">fatcat:xkvww7uabzhlzgfz3yiecyb75y</a> </span>
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Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data

Sina Dabiri, Chang-Tien Lu, Kevin Heaslip, Chandan K Reddy
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ht3yl6qfebhwrg7vrxkz4gxv3q" style="color: black;">IEEE Transactions on Knowledge and Data Engineering</a> </i> &nbsp;
The SECA integrates a convolutional-deconvolutional autoencoder and a convolutional neural network into a unified framework to concurrently perform supervised and unsupervised learning.  ...  The two components are simultaneously trained using both labeled and unlabeled GPS segments, which have already been converted into an efficient representation for the convolutional operation.  ...  Simultaneously, useful properties of the underlying road network can be also learned using unsupervised graph embedding approaches.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tkde.2019.2896985">doi:10.1109/tkde.2019.2896985</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/32wehszwsvhd3fp3oqji4g4enu">fatcat:32wehszwsvhd3fp3oqji4g4enu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190501225955/https://vtechworks.lib.vt.edu/bitstream/handle/10919/86845/Dabiri_S_T_2018.pdf;jsessionid=AF55BDC328665AEB312C16DAB707FC79?sequence=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/63/2d/632d7a72f473050b3ea93dffebd10ead69b58f56.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tkde.2019.2896985"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Artificial Intelligence [chapter]

Eric Guérin, Orhun Aydin, Ali Mahdavi-Amiri
<span title="2019-11-20">2019</span> <i title="Springer Singapore"> Manual of Digital Earth </i> &nbsp;
We introduce statistical ML methods that are frequently used in spatial problems and their applications.  ...  We discuss generative models, one of the hottest topics in ML, to illustrate the possibility of generating new data sets that can be used to train data analysis methods or to create new possibilities for  ...  The regression model can be used to predict the average temperature for the entire lower 48 states.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-981-32-9915-3_10">doi:10.1007/978-981-32-9915-3_10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bhm5j3k74naatinb5dpkxag5ky">fatcat:bhm5j3k74naatinb5dpkxag5ky</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200311050140/https://link.springer.com/content/pdf/10.1007%2F978-981-32-9915-3_10.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/da/02/da025ca27b7ee406397a4fb23d44136c26c04f23.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-981-32-9915-3_10"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>
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