A parallel NAW-DBLSTM algorithm on Spark for traffic flow forecasting

Xia, Dawen; Yang, Nan; Jiang, Shunying; Hu, Yang; Li, Yantao; Li, Huaqing; Wang, Lin

doi:10.1007/s00521-021-06409-5

Cited by 14 publications

(3 citation statements)

References 47 publications

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“…proposed mixed CNN and LSTM models to predict flow and speed, and Li et al (2020) to predict congestion. Aiming for increased accuracy and scalability using big data, Xia et al (2021) proposed a NAW-DBLSTM model on Spark that uses a bidirectional LSTM with an attention mechanism to perform traffic flow forecasting.…”

Section: Traffic Breakdown Forecastingmentioning

confidence: 99%

Probabilistic traffic breakdown forecasting through Bayesian approximation using variational LSTMs

Zechin

Cybis

2023

Transportmetrica B: Transport Dynamics

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Primeiramente agradeço o apoio conferido por toda minha família em minha jornada de estudos desde a infância, o qual culminoue espero não esteja limitadoa esta tese. Merecem agradecimentos especiais meus pais João Roque e Magda, pela dedicação incondicional em me incentivar e apoiar nos estudos, e de minha namorada Bruna, pela companhia diária em todos os momentos deste trabalho, pela inspiração, pelas palavras de carinho proferidas nos momentos mais necessários, pelas ideias sensatas e pelo amor expresso em cada auxílio.Agradeço também a todos os amigos que ao seu modo participaram desta jornada sem citar nomes para não cometer injustiças. Abro exceção para o amigo João, com quem tive a despretensiosa conversa que deu origem ao tema desta tese.Agradeço a todos meus professores pelo conhecimento transmitido e que, consolidado, resultou neste trabalho. Agradeço especialmente à minha oritentadora Helena Cybis, que me acompanha desde a graduação e que tem desempenhado papel crucial em direcionar os rumos de meus trabalhos. Agradeço a meus colegas de laboratório Felipe e Maurício, que me acompanharam nos primeiros estudos envolvendo machine learning, e aos colegas Fagner, Yan,

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Section: Traffic Breakdown Forecastingmentioning

confidence: 99%

Probabilistic traffic breakdown forecasting through Bayesian approximation using variational LSTMs

Zechin

Cybis

2023

Transportmetrica B: Transport Dynamics

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“…The research in [27,28] examines data foundation frameworks and data models based on Spark technology for business needs in smart city construction and urban management, introducing intelligence and refinement in urban management to realize realtime detection, analysis, and data processing in various areas of a city. In [29][30][31], Spark technology is used to address the traffic flow prediction problem in order to optimize traffic flow and improve road utilization and traffic efficiency. By monitoring and analyzing traffic data in real time, traffic congestion and anomalies are identified, and future traffic flows are predicted and planned.…”

Section: Plos Onementioning

confidence: 99%

A vehicle license plate data access model based on the jump hash consistency algorithm

Wang,

Cheng,

Chen

et al. 2023

PLoS ONE

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The massive amount of vehicle plate data generated by intelligent transportation systems is widely used in the field of urban transportation information system construction and has a high scientific research and application value. The adoption of big data platforms to properly preserve, process, and exploit these valuable data resources has become a hot research area in recent years. To address the problems of implementing complex multi-conditional comprehensive query functions and flexible data applications in the key–value database storage environment of a big data platform, this paper proposes a data access model based on the jump hash consistency algorithm. Algorithms such as data slice storage and multi-threaded sliding window parallel reading are used to realize evenly distributed storage and fast reading of massive time-series data on clustered data nodes. A comparative analysis of data distribution uniformity and retrieval efficiency shows that the model can effectively avoid generating the cluster hotspot problem, support comprehensive analysis queries with various complex conditions, and maintain high query efficiency by precisely positioning the data storage range and utilizing parallel scan reading.

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“…To forecast trafc fow, Xia et al in [33] suggested a bidirectional LSTM network with attention and a normal distribution module. Te attention mechanism is used to identify the high-impact attention weight values that have an impact on the targeted road segment, and it employs a fve-second time window for the road segment.…”

Section: Forecasting Trafc Under Regular Road Conditionsmentioning

confidence: 99%

BiLSTM- and GNN-Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data

Alourani,

Ashfaq,

Jhanjhi

et al. 2023

Journal of Advanced Transportation

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The timely and accurate forecasting of urban road traffic is crucial for smart city traffic management and control. It can assist both drivers and traffic controllers in selecting efficient routes and diverting traffic to less congested roads. However, estimating traffic volume while taking into account external factors such as weather and accidents is still a challenge. In this research, we propose a hybrid deep learning framework, double attention graph neural network BiLSTM (DAGNBL), that utilizes a graph neural network to represent spatial characteristics and bidirectional LSTM units to capture temporal dependencies between features. Attention modules are added to the GNN and BLSTM to find high-impact attention weight values for the chosen road section. Our model offers the best prediction accuracy with a mean absolute percentage error of 5.21% and a root mean squared error of 4. It can be utilized as a useful tool for predicting traffic flow on certain stretches of road.

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A parallel NAW-DBLSTM algorithm on Spark for traffic flow forecasting

Cited by 14 publications

References 47 publications

Probabilistic traffic breakdown forecasting through Bayesian approximation using variational LSTMs

Probabilistic traffic breakdown forecasting through Bayesian approximation using variational LSTMs

A vehicle license plate data access model based on the jump hash consistency algorithm

BiLSTM- and GNN-Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data

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