A Bi-Level Optimization Framework for Coordinated Control of Variable Directional Lanes and Traffic Signals Considering Route Choice Behavior

The efficient use of existing road infrastructure has become increasingly important in densely developed urban areas where large-scale roadway expansion is constrained. Variable directional lanes (VDLs) and traffic signal control can reallocate roadway capacity and improve network performance. However, most existing studies optimize lane configurations and signal timing under a fixed route-flow distribution and therefore do not capture the feedback between control decisions and travelers’ route choices. To address this limitation, this study proposes a bi-level framework for coordinating VDLs and traffic signals at multiple intersections. The upper-level model determines the VDL functions and signal-control parameters to minimize total system travel time, while the lower-level static Logit-based stochastic user equilibrium model endogenously redistributes fixed origin–destination (OD) demand among candidate paths. Thus, OD demand remains fixed within each analysis period, whereas the route-flow distribution responds endogenously to the interaction between traffic control and aggregate route-choice responses. A hybrid solution procedure combining the Non-dominated Sorting Genetic Algorithm II and the Method of Successive Averages is used to solve the coupled control–assignment problem. Numerical experiments on a hypothetical network showed that incorporating route-choice feedback improved coordinated VDL–signal control under the tested conditions. In a supplementary comparison with the pre-optimization BPR-based reference scenario, the average route travel time decreased by 7.67–12.84% across the five representative demand periods, including reductions of 12.52% and 12.84% during the morning and evening peak periods, respectively. Microscopic simulation provided an additional numerical consistency check, with average discrepancies of 4.66% before optimization and 4.38% after optimization between the analytical and simulation results. These findings indicate that incorporating aggregate route-choice feedback can support sustainable urban traffic management by reducing travel time and congestion and improving the utilization of existing transportation infrastructure. However, further validation using real-world data and larger-scale networks is required, and environmental benefits should be evaluated explicitly using energy-consumption and emission indicators.