Fuzzy Lane-Changing Models with Socio-Demographics and Vehicle-to- Infrastructure System Based on a Simulator Test

Objective: We investigated the impacts of drivers’ socio-demographic factors on these lane-changing models, and developed Fuzzy logic-based lane-changing models associated the drivers’ demographic factors. Methods: Forty drivers were recruited for a driving simulator test to collect their driving behaviors of changing lane in a work zone with/without the aid of Driver’s Smart Advisory System (DSAS), a Vehicle-to-Infrastructure (V2I) communication system. Fuzzy table look-up scheme was selected to model Lane-Changing Reaction Time (LCRT) and Lane-Changing Reaction Distance (LCRD) incorporated with drivers' socio-demographic information and the collected driving behaviors. Findings: Without DSAS messages, the elders slowly responded to a static traffic guide of lane-change, but they didn’t change lane at the last minute. The highly educated and young drivers changed lane the earliest. When DSAS messages were provided, all drivers’ LCRT became shorter while their LCRD longer. Conclusion: Drivers’ age and the level of education are essential socio-demographic factors in lane-changing process. The DSAS is able to instruct all drivers to take earlier lane-changing actions. The developed models can predict drivers’ LCRT and LCRD accurately.