Multi-task Learning for Sparse Traffic Forecasting
Accurate traffic prediction is crucial to improve the performance of intelligent transportation systems. Previous traffic prediction tasks mainly focus on small and non-isolated traffic subsystems, while the Traffic4cast 2022 competition is dedicated to exploring the traffic state dynamics of entire cities. Given one hour of sparse loop count data only, the task is to predict the congestion classes for all road segments and the expected times of arrival along super-segments 15 minutes into the future. The sparsity of loop counter data and highly uncertain real-time traffic conditions make the competition challenging. For this reason, we propose a multi-task learning network that can simultaneously predict the congestion classes and the speed of each road segment. Specifically, we use clustering and neural network methods to learn the dynamic features of loop counter data. Then, we construct a graph with road segments as nodes and capture the spatial dependence between road segments based on a Graph Neural Network. Finally, we learn three measures, namely the congestion class, the speed value and the volume class, simultaneously through a multi-task learning module. For the extended competition, we use the predicted speeds to calculate the expected times of arrival along super-segments. Our method achieved excellent results on the dataset provided by the Traffic4cast Competition 2022, source code is available at https://github.com/OctopusLi/NeurIPS2022-traffic4cast.
Code (2)
Tasks
Graph Neural NetworkMulti-Task LearningTraffic PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Network-Scale Traffic Modeling and Forecasting with Graphical Lasso and Neural Networks
Traffic flow forecasting, especially the short-term case, is an important topic in intelligent transportation systems (ITS). This paper does a lot of research on network-scale modeling and forecasting of short-term traff…
GPRMulti-Task LearningregressionSimilarity-based Feature Extraction for Large-scale Sparse Traffic Forecasting
Short-term traffic forecasting is an extensively studied topic in the field of intelligent transportation system. However, most existing forecasting systems are limited by the requirement of real-time probe vehicle data …
Graph Neural NetworkTime SeriesTime Series AnalysisTime Series Forecasting+1CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecasting
Traffic forecasting is important in intelligent transportation systems of webs and beneficial to traffic safety, yet is very challenging because of the complex and dynamic spatio-temporal dependencies in real-world traff…
DecoderGraphSparseNet: a Novel Method for Large Scale Traffic Flow Prediction
Traffic flow forecasting is a critical spatio-temporal data mining task with wide-ranging applications in intelligent route planning and dynamic traffic management. Recent advancements in deep learning, particularly thro…
Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data
We present a generic framework for spatio-temporal (ST) data modeling, analysis, and forecasting, with a special focus on data that is sparse in both space and time. Our multi-scaled framework is a seamless coupling of t…