7,399 research outputs found
Wavelet-Inspired Multiscale Graph Convolutional Recurrent Network for Traffic Forecasting
Traffic forecasting is the foundation for intelligent transportation systems.
Spatiotemporal graph neural networks have demonstrated state-of-the-art
performance in traffic forecasting. However, these methods do not explicitly
model some of the natural characteristics in traffic data, such as the
multiscale structure that encompasses spatial and temporal variations at
different levels of granularity or scale. To that end, we propose a
Wavelet-Inspired Graph Convolutional Recurrent Network (WavGCRN) which combines
multiscale analysis (MSA)-based method with Deep Learning (DL)-based method. In
WavGCRN, the traffic data is decomposed into time-frequency components with
Discrete Wavelet Transformation (DWT), constructing a multi-stream input
structure; then Graph Convolutional Recurrent networks (GCRNs) are employed as
encoders for each stream, extracting spatiotemporal features in different
scales; and finally the learnable Inversed DWT and GCRN are combined as the
decoder, fusing the information from all streams for traffic metrics
reconstruction and prediction. Furthermore, road-network-informed graphs and
data-driven graph learning are combined to accurately capture spatial
correlation. The proposed method can offer well-defined interpretability,
powerful learning capability, and competitive forecasting performance on
real-world traffic data sets
Spatiotemporal Calibration of Atmospheric Nitrogen Dioxide Concentration Estimates From an Air Quality Model for Connecticut
A spatiotemporal calibration and resolution refinement model was fitted to
calibrate nitrogen dioxide (NO) concentration estimates from the Community
Multiscale Air Quality (CMAQ) model, using two sources of observed data on
NO that differed in their spatial and temporal resolutions. To refine the
spatial resolution of the CMAQ model estimates, we leveraged information using
additional local covariates including total traffic volume within 2 km,
population density, elevation, and land use characteristics. Predictions from
this model greatly improved the bias in the CMAQ estimates, as observed by the
much lower mean squared error (MSE) at the NO monitor sites. The final
model was used to predict the daily concentration of ambient NO over the
entire state of Connecticut on a grid with pixels of size 300 x 300 m. A
comparison of the prediction map with a similar map for the CMAQ estimates
showed marked improvement in the spatial resolution. The effect of local
covariates was evident in the finer spatial resolution map, where the
contribution of traffic on major highways to ambient NO concentration
stands out. An animation was also provided to show the change in the
concentration of ambient NO over space and time for 1994 and 1995.Comment: 23 pages, 8 figures, supplementary materia
Traveling Trends: Social Butterflies or Frequent Fliers?
Trending topics are the online conversations that grab collective attention
on social media. They are continually changing and often reflect exogenous
events that happen in the real world. Trends are localized in space and time as
they are driven by activity in specific geographic areas that act as sources of
traffic and information flow. Taken independently, trends and geography have
been discussed in recent literature on online social media; although, so far,
little has been done to characterize the relation between trends and geography.
Here we investigate more than eleven thousand topics that trended on Twitter in
63 main US locations during a period of 50 days in 2013. This data allows us to
study the origins and pathways of trends, how they compete for popularity at
the local level to emerge as winners at the country level, and what dynamics
underlie their production and consumption in different geographic areas. We
identify two main classes of trending topics: those that surface locally,
coinciding with three different geographic clusters (East coast, Midwest and
Southwest); and those that emerge globally from several metropolitan areas,
coinciding with the major air traffic hubs of the country. These hubs act as
trendsetters, generating topics that eventually trend at the country level, and
driving the conversation across the country. This poses an intriguing
conjecture, drawing a parallel between the spread of information and diseases:
Do trends travel faster by airplane than over the Internet?Comment: Proceedings of the first ACM conference on Online social networks,
pp. 213-222, 201
Multiscale mobility networks and the large scale spreading of infectious diseases
Among the realistic ingredients to be considered in the computational
modeling of infectious diseases, human mobility represents a crucial challenge
both on the theoretical side and in view of the limited availability of
empirical data. In order to study the interplay between small-scale commuting
flows and long-range airline traffic in shaping the spatio-temporal pattern of
a global epidemic we i) analyze mobility data from 29 countries around the
world and find a gravity model able to provide a global description of
commuting patterns up to 300 kms; ii) integrate in a worldwide structured
metapopulation epidemic model a time-scale separation technique for evaluating
the force of infection due to multiscale mobility processes in the disease
dynamics. Commuting flows are found, on average, to be one order of magnitude
larger than airline flows. However, their introduction into the worldwide model
shows that the large scale pattern of the simulated epidemic exhibits only
small variations with respect to the baseline case where only airline traffic
is considered. The presence of short range mobility increases however the
synchronization of subpopulations in close proximity and affects the epidemic
behavior at the periphery of the airline transportation infrastructure. The
present approach outlines the possibility for the definition of layered
computational approaches where different modeling assumptions and granularities
can be used consistently in a unifying multi-scale framework.Comment: 10 pages, 4 figures, 1 tabl
Multiscale identification of spatio-temporal dynamical systems using a wavelet multiresolution analysis
In this paper, a new algorithm for the multiscale
identification of spatio-temporal dynamical systems is derived. It is shown that the input and output observations can be represented in a multiscale manner based on a wavelet multiresolution analysis. The system dynamics at some specific scale of interest can then be identified using an orthogonal forward leastsquares algorithm. This model can then be converted between different scales to produce predictions of the system outputs at different scales. The method can be applied to both multiscale and conventional spatio-temporal dynamical systems. For multiscale systems, the method can generate a parsimonious and effective model at a coarser scale while considering the effects from finer scales. Additionally, the proposed method can be used to improve the performance of the identification when measurements are noisy. Numerical examples are provided to demonstrate the application of the proposed new approach
Interactive, multi-purpose traffic prediction platform using connected vehicles dataset
Traffic congestion is a perennial issue because of the increasing traffic demand yet limited budget for maintaining current transportation infrastructure; let alone expanding them. Many congestion management techniques require timely and accurate traffic estimation and prediction. Examples of such techniques include incident management, real-time routing, and providing accurate trip information based on historical data. In this dissertation, a speech-powered traffic prediction platform is proposed, which deploys a new deep learning algorithm for traffic prediction using Connected Vehicles (CV) data. To speed-up traffic forecasting, a Graph Convolution -- Gated Recurrent Unit (GC-GRU) architecture is proposed and analysis of its performance on tabular data is compared to state-of-the-art models. GC-GRU's Mean Absolute Percentage Error (MAPE) was very close to Transformer (3.16 vs 3.12) while achieving the fastest inference time and a six-fold faster training time than Transformer, although Long-Short-Term Memory (LSTM) was the fastest in training. Such improved performance in traffic prediction with a shorter inference time and competitive training time allows the proposed architecture to better cater to real-time applications. This is the first study to demonstrate the advantage of using multiscale approach by combining CV data with conventional sources such as Waze and probe data. CV data was better at detecting short duration, Jam and stand-still incidents and detected them earlier as compared to probe. CV data excelled at detecting minor incidents with a 90 percent detection rate versus 20 percent for probes and detecting them 3 minutes faster. To process the big CV data faster, a new algorithm is proposed to extract the spatial and temporal features from the CSV files into a Multiscale Data Analysis (MDA). The algorithm also leverages Graphics Processing Unit (GPU) using the Nvidia Rapids framework and Dask parallel cluster in Python. The results show a seventy-fold speedup in the data Extract, Transform, Load (ETL) of the CV data for the State of Missouri of an entire day for all the unique CV journeys (reducing the processing time from about 48 hours to 25 minutes). The processed data is then fed into a customized UNet model that learns highlevel traffic features from network-level images to predict large-scale, multi-route, speed and volume of CVs. The accuracy and robustness of the proposed model are evaluated by taking different road types, times of day and image snippets of the developed model and comparable benchmarks. To visually analyze the historical traffic data and the results of the prediction model, an interactive web application powered by speech queries is built to offer accurate and fast insights of traffic performance, and thus, allow for better positioning of traffic control strategies. The product of this dissertation can be seamlessly deployed by transportation authorities to understand and manage congestions in a timely manner.Includes bibliographical references
A Multiscale Pyramid Transform for Graph Signals
Multiscale transforms designed to process analog and discrete-time signals
and images cannot be directly applied to analyze high-dimensional data residing
on the vertices of a weighted graph, as they do not capture the intrinsic
geometric structure of the underlying graph data domain. In this paper, we
adapt the Laplacian pyramid transform for signals on Euclidean domains so that
it can be used to analyze high-dimensional data residing on the vertices of a
weighted graph. Our approach is to study existing methods and develop new
methods for the four fundamental operations of graph downsampling, graph
reduction, and filtering and interpolation of signals on graphs. Equipped with
appropriate notions of these operations, we leverage the basic multiscale
constructs and intuitions from classical signal processing to generate a
transform that yields both a multiresolution of graphs and an associated
multiresolution of a graph signal on the underlying sequence of graphs.Comment: 16 pages, 13 figure
Different goals in multiscale simulations and how to reach them
In this paper we sum up our works on multiscale programs, mainly simulations.
We first start with describing what multiscaling is about, how it helps
perceiving signal from a background noise in a ?ow of data for example, for a
direct perception by a user or for a further use by another program. We then
give three examples of multiscale techniques we used in the past, maintaining a
summary, using an environmental marker introducing an history in the data and
finally using a knowledge on the behavior of the different scales to really
handle them at the same time
- …