A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation
Haitao Yuan,Guoliang Li +1 more
TLDR
Wang et al. as discussed by the authors provided a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer, and split the whole research scope into four parts from bottom to up, where the four parts are, respectively, spatiotemporal data, preprocessing, traffic prediction and traffic application.Abstract:
Intelligent transportation (e.g., intelligent traffic light) makes our travel more convenient and efficient. With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here, traffic prediction plays an important role. In this paper, we provide a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer. At first, we split the whole research scope into four parts from bottom to up, where the four parts are, respectively, spatio-temporal data, preprocessing, traffic prediction and traffic application. Later, we review existing work on the four parts. First, we summarize traffic data into five types according to their difference on spatial and temporal dimensions. Second, we focus on four significant data preprocessing techniques: map-matching, data cleaning, data storage and data compression. Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/forecasting). In particular, we summarize the challenges and discuss how existing methods address these challenges. Fourth, we list five typical traffic applications. Lastly, we provide emerging research challenges and opportunities. We believe that the survey can help the partitioners to understand existing traffic prediction problems and methods, which can further encourage them to solve their intelligent transportation applications.read more
Citations
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Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction
TL;DR: Wang et al. as discussed by the authors proposed a unified dynamic deep spatio-temporal neural network model based on convolutional neural networks and long short-term memory, termed as (DHSTNet) to simultaneously predict crowd flows in every region of a city.
Journal ArticleDOI
Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction
TL;DR: Wang et al. as discussed by the authors proposed a unified dynamic deep spatio-temporal neural network model based on convolutional neural networks and long short-term memory, termed as (DHSTNet) to simultaneously predict crowd flows in every region of a city.
Journal ArticleDOI
Graph Neural Network-Driven Traffic Forecasting for the Connected Internet of Vehicles
TL;DR: In this article , a graph neural network-driven traffic forecasting model for connected Internet of vehicles (CIoVs) is proposed, which is denoted as Gra-TF, which regards the dynamics of traffic data as a temporal evolution scenario.
Journal ArticleDOI
A Deep Gravity model for mobility flows generation.
TL;DR: In this paper, the authors proposed Deep Gravity, an effective model to generate flow probabilities that exploits many features (e.g., land use, road network, transport, food, health facilities) extracted from voluntary geographic data, and uses deep neural networks to discover nonlinear relationships between those features and mobility flows.
Journal ArticleDOI
Traffic Flow Prediction for Smart Traffic Lights Using Machine Learning Algorithms
TL;DR: In this paper , machine learning and deep learning algorithms are proposed for predicting traffic flow at an intersection, thus laying the groundwork for adaptive traffic control, either by remote control of traffic lights or by applying an algorithm that adjusts the timing according to the predicted flow.
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