2,157 research outputs found
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
Timely accurate traffic forecast is crucial for urban traffic control and
guidance. Due to the high nonlinearity and complexity of traffic flow,
traditional methods cannot satisfy the requirements of mid-and-long term
prediction tasks and often neglect spatial and temporal dependencies. In this
paper, we propose a novel deep learning framework, Spatio-Temporal Graph
Convolutional Networks (STGCN), to tackle the time series prediction problem in
traffic domain. Instead of applying regular convolutional and recurrent units,
we formulate the problem on graphs and build the model with complete
convolutional structures, which enable much faster training speed with fewer
parameters. Experiments show that our model STGCN effectively captures
comprehensive spatio-temporal correlations through modeling multi-scale traffic
networks and consistently outperforms state-of-the-art baselines on various
real-world traffic datasets.Comment: Proceedings of the 27th International Joint Conference on Artificial
Intelligenc
Patterns of mobility in a smart city
Transportation data in smart cities is becoming increasingly available. This data allows building meaningful, intelligent solutions for city residents and city management authorities, the so-called Intelligent Transportation Systems. Our research focused on Lisbon mobility data, provided by Lisbon municipality. The main research objective was to address mobility problems, interdependence, and cascading effects solutions for the city of Lisbon. We developed a data-driven approach based on historical data with a strong focus on visualization methods and dashboard creation. Also, we applied a method based on time series to do prediction based on the traffic congestion data provided. A CRISP-DM approach was applied, integrating different data sources, using Python. Hence, understand traffic patterns, and help the city authorities in the decision-making process, namely more preparedness, adaptability, responsiveness to events.Os dados de transporte, no âmbito das cidades inteligentes, estão cada vez mais disponÃveis. Estes dados permitem a construção de soluções inteligentes com impacto significativo na vida dos residentes e nos mecanismos das autoridades de gestão da cidade, os chamados Sistemas de Transporte Inteligentes. A nossa investigação incidiu sobre os dados de mobilidade urbana da cidade de Lisboa, disponibilizados pelo municÃpio. O principal objetivo da pesquisa foi abordar os problemas de mobilidade, interdependência e soluções de efeitos em cascata para a cidade de Lisboa. Para alcançar este objetivo foi desenvolvida uma metodologia baseada nos dados históricos do transito no centro urbano da cidade e principais acessos, com uma forte componente de visualização. Foi também aplicado um método baseado em series temporais para fazer a previsão das ocorrências de transito na cidade de Lisboa. Foi aplicada uma abordagem CRISP-DM, integrando diferentes fontes de dados, utilizando Python.
Esta tese tem como objetivo identificar padrões de mobilidade urbana com análise e visualização de dados, de forma a auxiliar as autoridades municipais no processo de tomada de decisão, nomeadamente estar mais preparada, adaptada e responsiva
Structural recurrent neural network for traffic speed prediction
Deep neural networks have recently demonstrated the
traffic prediction capability with the time series data obtained
by sensors mounted on road segments. However, capturing
spatio-temporal features of the traffic data often requires a
significant number of parameters to train, increasing compu-
tational burden. In this work we demonstrate that embedding
topological information of the road network improves the
process of learning traffic features. We use a graph of a ve-
hicular road network with recurrent neural networks (RNNs)
to infer the interaction between adjacent road segments as
well as the temporal dynamics. The topology of the road
network is converted into a spatio-temporal graph to form a
structural RNN (SRNN). The proposed approach is validated
over traffic speed data from the road network of the city of
Santander in Spain. The experiment shows that the graph-
based method outperforms the state-of-the-art methods based
on spatio-temporal images, requiring much fewer parameters
to trai
Multi-Spatio-temporal Fusion Graph Recurrent Network for Traffic forecasting
Traffic forecasting is essential for the traffic construction of smart cities
in the new era. However, traffic data's complex spatial and temporal
dependencies make traffic forecasting extremely challenging. Most existing
traffic forecasting methods rely on the predefined adjacency matrix to model
the Spatio-temporal dependencies. Nevertheless, the road traffic state is
highly real-time, so the adjacency matrix should change dynamically with time.
This article presents a new Multi-Spatio-temporal Fusion Graph Recurrent
Network (MSTFGRN) to address the issues above. The network proposes a
data-driven weighted adjacency matrix generation method to compensate for
real-time spatial dependencies not reflected by the predefined adjacency
matrix. It also efficiently learns hidden Spatio-temporal dependencies by
performing a new two-way Spatio-temporal fusion operation on parallel
Spatio-temporal relations at different moments. Finally, global Spatio-temporal
dependencies are captured simultaneously by integrating a global attention
mechanism into the Spatio-temporal fusion module. Extensive trials on four
large-scale, real-world traffic datasets demonstrate that our method achieves
state-of-the-art performance compared to alternative baselines
Scalable learning with a structural recurrent neural network for short-term traffic prediction
This paper presents a scalable deep learning approach for short-term traffic prediction based on historical traffic data in a vehicular road network. Capturing the spatio-temporal relationship of the big data often requires a significant amount of computational burden or an ad-hoc design aiming for a specific type of road network. To tackle the problem, we combine a road network graph with recurrent neural networks (RNNs)
to construct a structural RNN (SRNN). The SRNN employs a spatio-temporal graph to infer the interaction between adjacent road segments as well as the temporal dynamics of the time series data. The model is scalable thanks to two key aspects. First, the proposed SRNN architecture is built by using the semantic similarity of the spatio-temporal dynamic interactions of all segments. Second, we design the architecture to deal with fixed-length tensors regardless of the graph topology. With the real traffic speed data measured in the city of Santander, we demonstrate the proposed SRNN outperforms the image-based approaches using the capsule network (CapsNet) by 14.1% and the convolutional neural network (CNN) by 5.87%, respectively, in terms of root mean squared error (RMSE). Moreover, we show that the proposed model is scalable. The SRNN model trained with data of a road network is able to predict traffic data of different road networks, with the fixed number of parameters to train
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