An Incremental Learning-based Framework for Non-stationary Traffic Representations Clustering and Forecasting.

To curb the growth of COVID-19, many rules, including a work-from-home policy, were issued in 2020. While these limits successfully prevented the virus's transmission, they completely altered original mobility patterns, resulting in considerable reductions in travel time and vehicle miles traveled. Under this non-stationary data stream, the US Department of Transportation struggled to anticipate future traffic conditions. Obviously, two essential challenges need to be addressed immediately: 1) it is challenging for transportation agencies to learn representative traffic patterns from the continually changing traffic circumstances. And 2) when and how should the forecasting model be updated to learn new patterns without forgetting previous tasks? We proposed an incremental learning-based framework for non-stationary data clustering and forecasting in transportation scenarios to tackle the issues mentioned above. It is a dual-module architecture that includes a Temporal Neighborhood Clustering module and an Incremental Learning module. The objective of the first component is to dynamically detect the optimal boundary for clustering statistically similar neighbors by enlarging both the in-group similarity and between-group dissimilarity. The second module applies the online-EWC approach, which is commonly used in image classification tasks but rarely in regression models, to learn new tasks and avoid catastrophic forgetting, which is a typical occurrence while training neural networks with multiple tasks. Experiments on the Greater Seattle Area employed loop detector data collected in 2020 yielded reliable prediction performance in both robustness and accuracy. The dual-module framework can generate promising results from pre-COVID-19 to post-COVID-19 time frames. This framework would aid government agencies and the general public in developing long-term policies and strategies for post-pandemic intelligent transportation systems.