Short-Term Traffic Forecasting Using High-Resolution Traffic Data
- URL: http://arxiv.org/abs/2006.12292v1
- Date: Mon, 22 Jun 2020 14:26:19 GMT
- Title: Short-Term Traffic Forecasting Using High-Resolution Traffic Data
- Authors: Wenqing Li, Chuhan Yang, Saif Eddin Jabari
- Abstract summary: This paper develops a data-driven toolkit for traffic forecasting using high-resolution (a.k.a. event-based) traffic data.
The proposed methods are verified using high-resolution data obtained from a real-world traffic network in Abu Dhabi, UAE.
- Score: 2.0625936401496237
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper develops a data-driven toolkit for traffic forecasting using
high-resolution (a.k.a. event-based) traffic data. This is the raw data
obtained from fixed sensors in urban roads. Time series of such raw data
exhibit heavy fluctuations from one time step to the next (typically on the
order of 0.1-1 second). Short-term forecasts (10-30 seconds into the future) of
traffic conditions are critical for traffic operations applications (e.g.,
adaptive signal control). But traffic forecasting tools in the literature deal
predominantly with 3-5 minute aggregated data, where the typical signal cycle
is on the order of 2 minutes. This renders such forecasts useless at the
operations level. To this end, we model the traffic forecasting problem as a
matrix completion problem, where the forecasting inputs are mapped to a higher
dimensional space using kernels. The formulation allows us to capture both
nonlinear dependencies between forecasting inputs and outputs but also allows
us to capture dependencies among the inputs. These dependencies correspond to
correlations between different locations in the network. We further employ
adaptive boosting to enhance the training accuracy and capture historical
patterns in the data. The performance of the proposed methods is verified using
high-resolution data obtained from a real-world traffic network in Abu Dhabi,
UAE. Our experimental results show that the proposed method outperforms other
state-of-the-art algorithms.
Related papers
- An Application of Vector Autoregressive Model for Analyzing the Impact
of Weather And Nearby Traffic Flow On The Traffic Volume [0.0]
This paper aims to predict the traffic flow at one road segment based on nearby traffic volume and weather conditions.
Our team also discover the impact of weather conditions and nearby traffic volume on the traffic flow at a target point.
arXiv Detail & Related papers (2023-11-12T16:45:29Z) - Distil the informative essence of loop detector data set: Is
network-level traffic forecasting hungry for more data? [0.8002196839441036]
We propose an uncertainty-aware traffic forecasting framework to explore how many samples of loop data are truly effective for training forecasting models.
The proposed methodology proves valuable in evaluating large traffic datasets' true information content.
arXiv Detail & Related papers (2023-10-31T11:23:10Z) - Pre-training on Synthetic Driving Data for Trajectory Prediction [61.520225216107306]
We propose a pipeline-level solution to mitigate the issue of data scarcity in trajectory forecasting.
We adopt HD map augmentation and trajectory synthesis for generating driving data, and then we learn representations by pre-training on them.
We conduct extensive experiments to demonstrate the effectiveness of our data expansion and pre-training strategies.
arXiv Detail & Related papers (2023-09-18T19:49:22Z) - A Slow-Shifting Concerned Machine Learning Method for Short-term Traffic
Flow Forecasting [21.6456624219159]
A crucial challenge in traffic flow forecasting is the slow shifting in temporal peaks between daily and weekly cycles.
We propose a slow shifting concerned machine learning method for traffic flow forecasting, which includes two parts.
Our proposed method outperforms the state-of-art results by 14.55% and 62.56% using the metrics of root mean squared error and mean absolute percentage error, respectively.
arXiv Detail & Related papers (2023-03-31T03:07:53Z) - Traffic Prediction with Transfer Learning: A Mutual Information-based
Approach [11.444576186559487]
We propose TrafficTL, a cross-city traffic prediction approach that uses big data from other cities to aid data-scarce cities in traffic prediction.
TrafficTL is evaluated by comprehensive case studies on three real-world datasets and outperforms the state-of-the-art baseline by around 8 to 25 percent.
arXiv Detail & Related papers (2023-03-13T15:27:07Z) - PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for
Traffic Flow Prediction [78.05103666987655]
spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem.
We propose a novel propagation delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction.
Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
arXiv Detail & Related papers (2023-01-19T08:42:40Z) - Correlating sparse sensing for large-scale traffic speed estimation: A
Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging.
We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
arXiv Detail & Related papers (2022-10-21T07:25:57Z) - Predicting traffic signals on transportation networks using
spatio-temporal correlations on graphs [56.48498624951417]
This paper proposes a traffic propagation model that merges multiple heat diffusion kernels into a data-driven prediction model to forecast traffic signals.
We optimize the model parameters using Bayesian inference to minimize the prediction errors and, consequently, determine the mixing ratio of the two approaches.
The proposed model demonstrates prediction accuracy comparable to that of the state-of-the-art deep neural networks with lower computational effort.
arXiv Detail & Related papers (2021-04-27T18:17:42Z) - A Graph Convolutional Network with Signal Phasing Information for
Arterial Traffic Prediction [63.470149585093665]
arterial traffic prediction plays a crucial role in the development of modern intelligent transportation systems.
Many existing studies on arterial traffic prediction only consider temporal measurements of flow and occupancy from loop sensors and neglect the rich spatial relationships between upstream and downstream detectors.
We fill this gap by enhancing a deep learning approach, Diffusion Convolutional Recurrent Neural Network, with spatial information generated from signal timing plans at targeted intersections.
arXiv Detail & Related papers (2020-12-25T01:40:29Z) - Road Network Metric Learning for Estimated Time of Arrival [93.0759529610483]
In this paper, we propose the Road Network Metric Learning framework for Estimated Time of Arrival (ETA)
It consists of two components: (1) a main regression task to predict the travel time, and (2) an auxiliary metric learning task to improve the quality of link embedding vectors.
We show that our method outperforms the state-of-the-art model and the promotion concentrates on the cold links with few data.
arXiv Detail & Related papers (2020-06-24T04:45:14Z) - Nonlinear Traffic Prediction as a Matrix Completion Problem with
Ensemble Learning [1.8352113484137629]
This paper addresses the problem of short-term traffic prediction for signalized traffic operations management.
We focus on predicting sensor states in high-resolution (second-by-second)
Our contributions can be summarized as offering three insights.
arXiv Detail & Related papers (2020-01-08T13:10:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.