An Efficient Two-stage Gradient Boosting Framework for Short-term Traffic State Estimation
This work addresses real-time traffic state estimation for transportation systems, but it is incremental as it builds on existing gradient boosting methods for a specific benchmark.
The paper tackled short-term traffic state estimation for intelligent transportation systems by proposing an efficient two-stage gradient boosting framework, achieving third place in both core and extended challenges of the NeurIPS 2022 Traffic4cast challenge.
Real-time traffic state estimation is essential for intelligent transportation systems. The NeurIPS 2022 Traffic4cast challenge provides an excellent testbed for benchmarking short-term traffic state estimation approaches. This technical report describes our solution to this challenge. In particular, we present an efficient two-stage gradient boosting framework for short-term traffic state estimation. The first stage derives the month, day of the week, and time slot index based on the sparse loop counter data, and the second stage predicts the future traffic states based on the sparse loop counter data and the derived month, day of the week, and time slot index. Experimental results demonstrate that our two-stage gradient boosting framework achieves strong empirical performance, achieving third place in both the core and the extended challenges while remaining highly efficient. The source code for this technical report is available at \url{https://github.com/YichaoLu/Traffic4cast2022}.