Optimization Method of Guide Signs for Ten-lane Freeway Off-ramps based on Driving Task Analysis

At exits of bidirectional 10-lane expressways, the increasing demand for continuous lane changes, coupled with insufficient adaptation between guide sign placement and driving task rhythm, tends to cause concentrated driving workload. This study proposes a guide sign optimization method based on driving task analysis. The continuous driving process from the mainline to the off-ramp is divided into five functional zones: mainline driving, first diversion, second diversion, deceleration adjustment, and ramp speed regulation. Five subtasks (visual scanning, lane control, sign identification, lane changing, and speed control) are decomposed and their workloads are quantified. Workload analysis reveals that the first and second diversion zones, with scores of 40 and 43 respectively, are the primary workload peaks during the exit process. Based on these findings, a "task workload-time-distance" mapping relationship is established to translate driving workload into spatial requirements for sign placement, enabling a transition from driving behavior analysis to the derivation of sign layout parameters. Accordingly, a six-level guide sign layout scheme featuring "cognitive advance and operational peak staggering" is proposed. A Unity driving simulation scenario was developed based on a real highway reconstruction and expansion project. Thirty-two drivers completed 160 valid trials across five guide sign schemes. By integrating drivers' information workload and vehicle control workload, and applying the entropy weight method for weight determination with subsequent scale calibration, a comprehensive Driver Workload (DWL) evaluation model was constructed. The results show that optimized Scheme E achieved a DWL of 0.331 ± 0.096, representing a 73.9% reduction compared to the actual engineering Scheme A (1.267±0.182), and was significantly lower than all other comparison schemes (p < 0.05), indicating that Scheme E effectively reduces overall driver workload and improves vehicle control stability. These findings demonstrate that zonal analysis based on driving task workload characteristics and optimization of sign spatial layout can enhance the matching between guide information and driving task rhythm, providing methodological support for the quantitative optimization of guide signs at exits of extra-wide expressways.