  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.
