Assignment-Informed Link-Flow Forecasting: A Case Study in Wuxi

Road restrictions and changing travel demand can alter link flows before recent detector counts describe the new pattern. This study develops a selective assignment correction for traffic flow forecasting in Wuxi. A relative cost Logit model translates OD demand and road costs into changes in route use. Link specific calibration adjusts the response scale, and an assignment state rule directs the correction toward substantial departures from recurring conditions. The study combines observed detector counts, OD demand and the Wuxi road network. Comparisons cover supplied road cost changes, several forecasting backbones, prediction horizons and persistent flow drops. Assignment improves forecasts in strong path states and adds a useful correction to models that already use recent detector histories. The benefit increases with assigned state strength and remains visible across test dates. Calibrating service ratios also improves forecasts during persistent drops. These findings indicate how route information can supplement an established detector forecast. Broader observation periods, independent demand sources and documented disruptions would support assessment across a wider range of operating conditions.