Results 241 to 250 of about 210,556 (289)

Interpretable local flow attention for multi-step traffic flow prediction

Neural Networks, 2023
Traffic flow prediction (TFP) has attracted increasing attention with the development of smart city. In the past few years, neural network-based methods have shown impressive performance for TFP. However, most of previous studies fail to explicitly and effectively model the relationship between inflows and outflows.
Xu Huang, Bowen Zhang, Shanshan Feng, Yunming Ye, Xutao Li   +4 more
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Short-term traffic flow prediction: From the perspective of traffic flow decomposition

Neurocomputing, 2020
Abstract Some researchers treat traffic flow as an entirety while predicting short-term traffic flow. Through analyzing real-world traffic flow, we have found that urban traffic shows a stable changing process along with random disturbs. An alternative way is to decompose traffic flow into two components: periodicity and volatility.
Li Chen, Linjiang Zheng, Jie Yang, Dong Xia, Weining Liu   +4 more
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Traffic Flow Prediction with Conv-SAE

Proceedings of the 2020 the 7th International Conference on Automation and Logistics (ICAL), 2020
As traffic jams become more serious, accurate traffic flow forecasting is essential to ease traffic pressure. In order to meet the needs of traffic forecasting, this paper proposes a combination model Conv-SAE based on convolution and SAE(stacked autoencoders), which roughly extracts the spatial features and temporal features by the SAE module, and ...
Shi Meng, Shulin Sun, Bailin Yang
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Traffic Flow Prediction with Vehicle Trajectories

Proceedings of the AAAI Conference on Artificial Intelligence, 2021
This paper proposes a spatiotemporal deep learning framework, Trajectory-based Graph Neural Network (TrGNN), that mines the underlying causality of flows from historical vehicle trajectories and incorporates that into road traffic prediction. The vehicle trajectory transition patterns are studied to explicitly model the spatial traffic demand via graph
Mingqian Li, Panrong Tong, Mo Li, Zhongming Jin, Jianqiang Huang, Xian-Sheng Hua   +5 more
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Adaptive Bayesian network for traffic flow prediction

2011 IEEE Statistical Signal Processing Workshop (SSP), 2011
Traffic control is essential for the achievement of a sustainable and safe mobility. Monitoring systems for traffic control collect a great amount of data that must be efficiently processed by estimation/prevision models to support operations of traffic management.
PASCALE, ALESSANDRA, NICOLI, MONICA BARBARA   +1 more
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Dynamic Traffic Prediction Based on Traffic Flow Mining

2006 6th World Congress on Intelligent Control and Automation, 2006
ITS technology collects a large of historical traffic flow data that may provide information for the support and improvement of traffic control. Data mining technique is appropriate to analysis the large amount of ITS data to acquire useful traffic pattern. We present a dynamic traffic prediction model, the model deals with traffic flow data to convert
null Yaqin Wang, null Yue Chen, null Minggui Qin, null Yangyong Zhu   +3 more
openaire   +1 more source

Traffic-flow-prediction systems based on upstream traffic

Proceedings of VNIS'94 - 1994 Vehicle Navigation and Information Systems Conference, 2002
Network-based model were developed to predict short term future traffic volume based on current traffic, historical average, and upstream traffic. It is presumed that upstream traffic volume can be used to predict the downstream traffic in a specific time period.
A.G. Hobeika, null Chang Kyun Kim
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Predicting information flows in network traffic

Journal of the American Society for Information Science and Technology, 2002
AbstractIn optimizing information flows in networks, it would be useful to predict aspects of the network traffic. Yet, the notion of predicting network traffic does not appear in the relevant literature reporting analysis of network traffic. This literature is both well developed and skeptical about the value of traditional time series analysis on ...
Melvin J. Hinich, Robert E. Molyneux
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Long Term Prediction of Traffic Flow

IFAC Proceedings Volumes, 1987
Abstract Prediction of traffic flow is now becoming a basic problem in designing variable information boards or route guidance (route telling) systems in road networks. Although, time series analysis is widely applied to this problem, it is known that the error of prediction increases rapidly as the prediction lead time increases and it is difficult ...
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Traffic Flow Prediction Based on BRNN

2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC), 2019
Accurate and real-time traffic flow prediction plays an important role in building intelligent transportation systems and traffic control and induction. As traffic flow data is mostly time series data, selecting a bidirectional recurrent neural network (BRNN) model in a recurrent neural network (RNN) that is good at processing time series data for ...
Huang Bohan, Bai Yun
openaire   +1 more source