6,824 research outputs found
Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction
Traffic prediction plays an important role in the realization of traffic
control and scheduling tasks in intelligent transportation systems. With the
diversification of data sources, reasonably using rich traffic data to model
the complex spatial-temporal dependence and nonlinear characteristics in
traffic flow are the key challenge for intelligent transportation system. In
addition, clearly evaluating the importance of spatial-temporal features
extracted from different data becomes a challenge. A Double Layer - Spatial
Temporal Feature Extraction and Evaluation (DL-STFEE) model is proposed. The
lower layer of DL-STFEE is spatial-temporal feature extraction layer. The
spatial and temporal features in traffic data are extracted by multi-graph
graph convolution and attention mechanism, and different combinations of
spatial and temporal features are generated. The upper layer of DL-STFEE is the
spatial-temporal feature evaluation layer. Through the attention score matrix
generated by the high-dimensional self-attention mechanism, the
spatial-temporal features combinations are fused and evaluated, so as to get
the impact of different combinations on prediction effect. Three sets of
experiments are performed on actual traffic datasets to show that DL-STFEE can
effectively capture the spatial-temporal features and evaluate the importance
of different spatial-temporal feature combinations.Comment: 39 pages, 14 figures, 5 table
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
Annual Average Daily Traffic (AADT) Estimation with Regression Using Centrality and Roadway Characteristic Variables
Accurate estimation of annual average daily traffic (AADT) is critical in nearly every roadway decision, such as allocations of funding for roadway improvements and maintenance. While some roadway locations have permanent count stations capable of counting vehicles 24-hours a day throughout the entire year, they are typically only installed at selected locations on major roadways (i.e., freeways and major arterials) with high traffic volumes. On lower functional class roads and roadway segments on higher functional class roads without permanent count stations, short-term coverage counts are collected and adjusted with data from permanent count stations to estimate AADT. Short-term coverage counts are essential because they provide data from roadways of all functional classes and lane configurations, accounting for varying volumes on all roads maintained by an agency. Although necessary, coverage counts can be expensive and can exhaust resources such as investment in data collection workforce, equipment and data analysis. This study develops a strategy for estimating AADT on every roadway within a given jurisdiction using permanent count stations and short term coverage counts, while limiting the number of coverage counts needed. The goal of this thesis is to illustrate a noteworthy time and cost savings using a new centrality based AADT estimation method. A set of new deterministic variables, based on the theory of centrality, are introduced. This study revealed that estimated root mean square error (RMSE) for the new centrality based AADT method is half of the estimated RMSE in the travel demand based AADT model for the same area. Additionally, it was found that using centrality based AADT estimation model, the number of coverage count stations necessary can be reduced by more than 60% compared to the standard factor method for AADT estimation without compromising the AADT estimation accuracy
FCG-ASpredictor: An Approach for the Prediction of Average Speed of Road Segments with Floating Car GPS Data
The average speed (AS) of a road segment is an important factor for predicting traffic congestion, because the accuracy of AS can directly affect the implementation of traffic management. The traffic environment, spatiotemporal information, and the dynamic interaction between these two factors impact the predictive accuracy of AS in the existing literature, and floating car data comprehensively reflect the operation of urban road vehicles. In this paper, we proposed a novel road segment AS predictive model, which is based on floating car data. First, the impact of historical AS, weather, and date attributes on AS prediction has been analyzed. Then, through spatiotemporal correlations calculation based on the data from Global Positioning System (GPS), the predictive method utilizes the recursive least squares method to fuse the historical AS with other factors (such as weather, date attributes, etc.) and adopts an extended Kalman filter algorithm to accurately predict the AS of the target segment. Finally, we applied our approach on the traffic congestion prediction on four road segments in Chengdu, China. The results showed that the proposed predictive model is highly feasible and accurate.
Document type: Articl
Prediction of Traffic Flow via Connected Vehicles
We propose a Short-term Traffic flow Prediction (STP) framework so that
transportation authorities take early actions to control flow and prevent
congestion. We anticipate flow at future time frames on a target road segment
based on historical flow data and innovative features such as real time feeds
and trajectory data provided by Connected Vehicles (CV) technology. To cope
with the fact that existing approaches do not adapt to variation in traffic, we
show how this novel approach allows advanced modelling by integrating into the
forecasting of flow, the impact of the various events that CV realistically
encountered on segments along their trajectory. We solve the STP problem with a
Deep Neural Networks (DNN) in a multitask learning setting augmented by input
from CV. Results show that our approach, namely MTL-CV, with an average
Root-Mean-Square Error (RMSE) of 0.052, outperforms state-of-the-art ARIMA time
series (RMSE of 0.255) and baseline classifiers (RMSE of 0.122). Compared to
single task learning with Artificial Neural Network (ANN), ANN had a lower
performance, 0.113 for RMSE, than MTL-CV. MTL-CV learned historical
similarities between segments, in contrast to using direct historical trends in
the measure, because trends may not exist in the measure but do in the
similarities
- …