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Real-time traffic state estimation is essential for intelligenttransportation systems. The NeurIPS 2022 Traffic4cast challenge provides anexcellent testbed for benchmarking short-term traffic state estimationapproaches. This technical report describes our solution to this challenge. Inparticular, we present an efficient two-stage gradient boosting framework forshort-term traffic state estimation. The first stage derives the month, day ofthe week, and time slot index based on the sparse loop counter data, and thesecond stage predicts the future traffic states based on the sparse loopcounter data and the derived month, day of the week, and time slot index.Experimental results demonstrate that our two-stage gradient boosting frameworkachieves strong empirical performance, achieving third place in both the coreand the extended challenges while remaining highly efficient. The source codefor this technical report is available at\url{https://github.com/YichaoLu/Traffic4cast2022}.

An Efficient Two-stage Gradient Boosting Framework for Short-term Traffic State Estimation