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Abstract Sparse roadside infrastructure creates long monitoring gaps on desert expressways. This study develops a physics-guided machine-learning framework for downstream traffic-flow prediction and operating-regime attribution using sparse volume observations. For a 40.9-km section of the G30 Lianhuo Expressway in Xinjiang, a conservation-motivated boundary-flow imbalance feature was constructed from temporally aligned upstream and downstream flows.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
