How conservative driving behavior increases crash risk: Understanding the systemic safety impacts of older drivers in...

Despite the cautious driving behavior of older adults, their over-representation in traffic accidents presents a significant safety concern. Their conservative driving patterns, such as slower reaction times and longer headways, may disrupt heterogeneous traffic flow and increase crash risks. To investigate this phenomenon, this study proposes a data-driven risk assessment framework that links micro-level car-following behavior with macro-level traffic safety. Using driving simulator data from 16 older and 19 young drivers, a Time Series Lightweight Adaptive Network (TSLANet) is employed to train longitudinal acceleration prediction models for both groups, which significantly outperformed mainstream models such as Intelligent Driver Model (IDM) and Transformer in terms of RMSE, MAE, and R². The framework then enables flexible configurations of older driver proportions and various spatial layouts. It simulates five car-following scenarios and outputs multidimensional indicators, including acceleration noise, speed coefficient of variation (SCV), deceleration rate to avoid a crash (DRAC), and potential index for collision with urgent deceleration (PICUD). Simulation results reveal that increasing the proportion of older drivers amplifies speed fluctuations and reduces safety margins. Front-concentrated and random distributions of older drivers exacerbate risks, whereas rear-concentrated and alternating distributions effectively mitigate adverse effects. The methodology and findings of this study provide robust evidence for refined traffic risk assessment, safety interventions for older drivers, and mixed-traffic optimization in aging societies.