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How to Identify Patterns of Citywide Dynamic Traffic at a Low Cost? An In-Depth Neural Network Approach with Digital Maps

Li Zhang, Ke Gong, Maozeng Xu, Aixing Li, Yuanxiang Dong, Yong Wang, Guang Li
2021 Complexity  
The identification and analysis of the spatiotemporal dynamic traffic patterns in citywide road networks constitute a crucial process for complex traffic management and control.  ...  The distance captured by our method can represent the evolution of different traffic conditions during the morning and evening peak hours.  ...  We study those traffic condition maps to reveal the pattern evolution in the citywide traffic network.  ... 
doi:10.1155/2021/6648116 fatcat:eti5u5ljrfdqrajejouaqtaqfa

Short-Term Traffic State Prediction Based on the Spatiotemporal Features of Critical Road Sections

Gang Yang, Yunpeng Wang, Haiyang Yu, Yilong Ren, Jindong Xie
2018 Sensors  
road sections (CRS-ConvLSTM NN) to predict the traffic evolution of global networks.  ...  Subsequently, the traffic speed of the critical road sections is used as the input to the ConvLSTM to predict the future traffic states of the entire network.  ...  especially suitable for RNNs to capture the temporal evolution of traffic flow.  ... 
doi:10.3390/s18072287 pmid:30011942 pmcid:PMC6068706 fatcat:72bos3dgxvbrthsuvb43nededy

A Spatiotemporal Apriori Approach to Capture Dynamic Associations of Regional Traffic Congestion

Dong-Fan Xie, Mei-Hong Wang, Xiao-Mei Zhao
2019 IEEE Access  
Due to the interactions among adjacent roads in urban road networks, traffic congestion gradually propagates to neigboring roads, resulting in regional congestion.  ...  Case studies are carried out for the urban road network in Tianjin, China, based on empirical data.  ...  INTER-TRANSACTION SPATIOTEMPORAL APRIORI ALGORITHM The dynamic evolution of congestion is a typical characteristic of network traffic flow, which may seriously impact on the efficiency of road networks  ... 
doi:10.1109/access.2019.2962619 fatcat:c5ewciaf5ffhxiqh4uyhnrd4k4

Dynamic Spatiotemporal Causality Analysis for Network Traffic Flow Based on Transfer Entropy and Sliding Window Approach

Senyan Yang, Lianju Ning, Xilong Cai, Mingyu Liu, Ronghui Zhang
2021 Journal of Advanced Transportation  
This study proposes a dynamic spatiotemporal causality modeling approach to analyze traffic causal relationships for the large-scale road network.  ...  critical roads and potential bottlenecks of the existing road network.  ...  for the large-scale road network. e above spatiotemporal traffic causality modeling methods are either too simple to fully capture the potential spatiotemporal causality and nonlinearity characteristic  ... 
doi:10.1155/2021/6616800 fatcat:pt6xcxplyzbhfodxsgwb5y7mwa

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks [article]

Haiyang Yu, Zhihai Wu, Shuqin Wang, Yunpeng Wang, Xiaolei Ma
2017 arXiv   pre-print
The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs.  ...  Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
arXiv:1705.02699v1 fatcat:2dkv77fmqvgcvmseajxyzcqj3y

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

Haiyang Yu, Zhihai Wu, Shuqin Wang, Yunpeng Wang, Xiaolei Ma
2017 Sensors  
The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs.  ...  Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s17071501 pmid:28672867 pmcid:PMC5539509 fatcat:et6ohnfz45c5jouhd3p7zkphsu

Short-term Traffic Speed Prediction of Urban Road with Multi-source Data

Xun Yang, Yu Yuan, Zhiyuan Liu
2020 IEEE Access  
In recent years, the convolutional and recurrent neural networks are widely applied in traffic prediction tasks.  ...  Under different time steps, the prediction error of our model is lower than any other methods in urban expressway, primaryarterial, secondary-arterial, and branch-road.  ...  LONG SHORT-TERM MEMORY NEURAL NETWORK In this paper, the LSTM neural network is used to capture the temporal evolution of traffic speed.  ... 
doi:10.1109/access.2020.2992507 fatcat:sqarwq5tqbcf5lxfkpmiy4vu3m

AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting [article]

Jiawei Zhu, Chao Tao, Hanhan Deng, Ling Zhao, Pu Wang, Tao Lin, Haifeng Li
2020 arXiv   pre-print
Therefore, based on the assumption that introducing external factors can enhance the spatiotemporal accuracy in predicting traffic and improving interpretability, we propose an attribute-augmented spatiotemporal  ...  Traffic forecasting is a fundamental and challenging task in the field of intelligent transportation.  ...  ACKNOWLEDGMENTS This work was supported by the National Science Foundation of China [grant numbers ].  ... 
arXiv:2011.11004v1 fatcat:yacqvnklqzhkjnw6h25zbbjmha

Multi-fold Correlation Attention Network for Predicting Traffic Speeds with Heterogeneous Frequency [article]

Yidan Sun, Guiyuan Jiang, Siew-Kei Lam, Peilan He, Fangxin Ning
2021 arXiv   pre-print
In addition, the existing works assume that all road segments can employ the same sampling frequency of traffic states, which is impractical.  ...  We propose a Heterogeneous Spatial Correlation (HSC) model to capture the spatial correlation based on a specific measurement, where the traffic data of varying road segments can be heterogeneous (i.e.  ...  Acknowledgments This research project is supported in part by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme with the  ... 
arXiv:2104.09083v1 fatcat:3nchqv526vbjxj3rmmvph6nxzm

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

Xiaolei Ma, Zhuang Dai, Zhengbing He, Jihui Ma, Yong Wang, Yunpeng Wang
2017 Sensors  
The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing  ...  Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix.  ...  Understanding traffic evolution for the entire road network rather than on a single road is of great interest and importance to help people with complete traffic information in make better route choices  ... 
doi:10.3390/s17040818 pmid:28394270 pmcid:PMC5422179 fatcat:ps4vh3l34rcv3oie5fbmba4gdq

A Temporal Directed Graph Convolution Network for Traffic Forecasting Using Taxi Trajectory Data

Kaiqi Chen, Min Deng, Yan Shi
2021 ISPRS International Journal of Geo-Information  
Traffic forecasting plays a vital role in intelligent transportation systems and is of great significance for traffic management.  ...  To address these issues, a temporal directed graph convolution network (T-DGCN) is proposed.  ...  Acknowledgments: The authors would like to thank the reviewers for their useful comments and suggestions for this paper. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijgi10090624 fatcat:ht3237glmvbkjefr345g7hfk5m

AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting

Jiawei Zhu, Qiongjie Wang, Chao Tao, Hanhan Deng, Ling Zhao, Haifeng Li
2021 IEEE Access  
Therefore, based on the assumption that introducing external factors can enhance the spatiotemporal accuracy in predicting traffic and improving interpretability, we propose an attribute-augmented spatiotemporal  ...  Traffic forecasting is a fundamental and challenging task in the field of intelligent transportation.  ...  in the road network.  ... 
doi:10.1109/access.2021.3062114 fatcat:27sptaw2xbgfnb6wt6dj4te2gu

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction [article]

Xiaolei Ma, Zhuang Dai, Zhengbing He, Jihui Na, Yong Wang, Yunpeng Wang
2017 arXiv   pre-print
The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing  ...  Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix.  ...  Conflicts of Interest: The authors declare no conflict of interest. References  ... 
arXiv:1701.04245v4 fatcat:sgrpclijungkbht7ggcdeldcca

Spatiotemporal Traffic State Prediction Based on Discriminatively Pre-trained Deep Neural Networks

Mohammed Elhenawy, Hesham Rakha
2017 Advances in Science, Technology and Engineering Systems  
The availability of traffic data and computational advances now make it possible to build data-driven models that capture the evolution of the state of traffic along modeled stretches of road.  ...  In this paper, we adopted a state-of-the-art machine learning deep neural network and the divide-andconquer approach to model large road stretches.  ...  This is not an easy task, since the evolution of traffic states is a complex spatiotemporal process.  ... 
doi:10.25046/aj020387 fatcat:fl5y7xycqnf55lcyqsbtlagcla

Urban Fine Management of Multisource Spatial Data Fusion Based on Smart City Construction

Yuanpeng Long, Xuena Zhang, Feng Gao, Sang-Bing Tsai
2021 Mathematical Problems in Engineering  
Aiming at the traffic problems in urban fine management, this paper proposes a deep network architecture based on multisource data fusion.  ...  Then, the convolution neural network technology is explored in the data fusion technology strategy.  ...  Acknowledgments is study was supported by the "Science and Technology Project of China Railway Corporation, China (Grant no. 1341324011)" and by Chengdu Technological University.  ... 
doi:10.1155/2021/5058791 fatcat:fzalehc7indb7mp4wovqlsms74
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