105 research outputs found
Link travel time estimation in urban areas by detectors and probe vehicles fusion
International audienceThis paper presents an approach to estimate link travel time in urban areas. This approach consists of a data fusion from underground loop detectors and probe vehicles equipped with global positioning system (GPS). This method is expected to be more accurate, reliable and robust than using either of these data sources alone. In this approach, an algorithm is developed. This algorithm is based on the unscented Kalman filter using vehicle counts and flows from loop detectors located at the end of every link, and travel time from probe vehicles. From these counts the average travel time is calculated using the "cumulative plot" method. Furthermore, in order to incorporate the GPS data, a map-matching method is used to associate a travel time to the appropriate link
Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach
Traffic state forecasting is crucial for traffic management and control
strategies, as well as user- and system-level decision making in the
transportation network. While traffic forecasting has been approached with a
variety of techniques over the last couple of decades, most approaches simply
rely on endogenous traffic variables for state prediction, despite the evidence
that exogenous factors can significantly impact traffic conditions. This paper
proposes a multi-dimensional spatio-temporal graph attention-based traffic
prediction approach (M-STGAT), which predicts traffic based on past
observations of speed, along with lane closure events, temperature, and
visibility across the transportation network. The approach is based on a graph
attention network architecture, which also learns based on the structure of the
transportation network on which these variables are observed. Numerical
experiments are performed using traffic speed and lane closure data from the
California Department of Transportation (Caltrans) Performance Measurement
System (PeMS). The corresponding weather data were downloaded from the National
Oceanic and Atmospheric Administration (NOOA) Automated Surface Observing
Systems (ASOS). For comparison, the numerical experiments implement three
alternative models which do not allow for the multi-dimensional input. The
M-STGAT is shown to outperform the three alternative models, when performing
tests using our primary data set for prediction with a 30-, 45-, and 60-minute
prediction horizon, in terms of three error measures: Mean Absolute Error
(MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).
However, the model's transferability can vary for different transfer data sets
and this aspect may require further investigation.Comment: Transportation Research Board Annual Meeting 202
Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
Big data has been used widely in many areas including the transportation
industry. Using various data sources, traffic states can be well estimated and
further predicted for improving the overall operation efficiency. Combined with
this trend, this study presents an up-to-date survey of open data and big data
tools used for traffic estimation and prediction. Different data types are
categorized and the off-the-shelf tools are introduced. To further promote the
use of big data for traffic estimation and prediction tasks, challenges and
future directions are given for future studies
A vision transformer approach for traffic congestion prediction in urban areas
Traffic problems continue to deteriorate because of the increasing population in urban areas that rely on many modes of
transportation, the transportation infrastructure has achieved considerable strides in the last several decades. This has led to an
increase in congestion control difficulties, which directly affect citizens through air pollution, fuel consumption, traffic law breaches,
noise pollution, accidents, and loss of time. Traffic prediction is an essential aspect of an intelligent transportation system in smart cities
because it helps reduce traffic congestion. This article aims to design and enforce a traffic prediction scheme that is efficient and
accurate in forecasting traffic flow. Available traffic flow prediction methods are still unsuitable for real-world applications. This fact
motivated us to work on a traffic flow forecasting issue using Vision Transformers (VTs). In this work, VTs were used in conjunction with
Convolutional neural networks (CNNs) to predict traffic congestion in urban spaces on a city-wide scale. In our proposed architecture, a
traffic image is fed to the CNN, which generates feature maps. These feature maps are then fed to the VT, which employs the dual
techniques of tokenization and projection. Tokenization is used to convert features into tokens containing Vision information, which are
then sent to projection, where they are transformed into feature maps and ultimately delivered to LSTM. The experimental results
demonstrate that the vision transformer prediction method based on Spatio-temporal characteristics is an excellent way of predicting
traffic flow, particularly during anomalous traffic situations. The proposed technology surpasses traditional methods in terms of
precision, accuracy and recall and aids in energy conservation. Through rerouting, the proposed work will benefit travellers and reduce
fuel use
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