Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting

Traffic forecasting is a challenging problem due to complex road networks and sudden speed changes caused by various events on roads. Several models have been proposed to solve this challenging problem, with a focus on learning the spatio-temporal dependencies of roads. In this work, we propose a new perspective for converting the forecasting problem into a pattern-matching task, assuming that large traffic data can be represented by a set of patterns. To evaluate the validity of this new perspective, we design a novel traffic forecasting model called Pattern-Matching Memory Networks (PM-MemNet), which learns to match input data to representative patterns with a key-value memory structure. We first extract and cluster representative traffic patterns that serve as keys in the memory. Then, by matching the extracted keys and inputs, PM-MemNet acquires the necessary information on existing traffic patterns from the memory and uses it for forecasting. To model the spatio-temporal correlation of traffic, we proposed a novel memory architecture, GCMem, which integrates attention and graph convolution. The experimental results indicate that PM-MemNet is more accurate than state-of-the-art models, such as Graph WaveNet, with higher responsiveness. We also present a qualitative analysis describing how PM-MemNet works and achieves higher accuracy when road speed changes rapidly.