Rear-end crash prevention depends strongly on warning timing, yet most connected warning studies still rely on fixed trigger rules that are only loosely aligned with drivers' perceptual urgency and are rarely evaluated across different car-following conditions under degraded visibility. This study evaluated a vehicle-to-vehicle-enabled connected collision warning (CCW) strategy triggered by the visual-looming-based tau-1 metric, which provides a perception-consistent indicator of time-critical risk. A driving simulator experiment was conducted under rainy conditions, in which 37 licensed drivers were exposed to 12 lead vehicle deceleration events each. Behavioral responses and surrogate safety outcomes were analyzed using linear mixed-effects and mediation models. Relative to the no-warning condition, the tau-1-triggered CCW reduced collision occurrence and shortened brake reaction time by 10.1% at far headway and 7.5% at near headway, while producing limited changes in brake transition time, maximum deceleration, and the proportion of uncomfortable deceleration. The safety effects of the warning were jointly moderated by headway and lead vehicle deceleration, with stronger benefits observed under moderate urgency and attenuated effects under extreme urgency. Mediation analysis further indicated that 39% of the reduction in collision severity was transmitted through brake reaction time, whereas 61% was attributable to direct warning effects on post-braking control. These findings support perception-consistent and scenario-adaptive trigger calibration for connected warning systems, in which warning thresholds should be adjusted according to headway condition, lead vehicle deceleration intensity, and degraded-visibility risk rather than relying on a single fixed time-to-collision (TTC) or distance threshold.
Child car seat laws vary widely across U.S. states, creating potential safety risks for children and penalties for drivers during interstate travel. This study examined child restraint laws in all 50 states and the DC to assess their strength and uniformity. Statutes were collected using Nexis Uni and evaluated across seven parameters: rear-facing requirements, forward-facing requirements, booster-seat requirements, seat-belt requirements, fines for violations, license-point penalties, and enforcement type. Each parameter was scored on a 0-10 scale, with states meeting the stringent requirements receiving 10 points and others evaluated using secondary criteria. Results show substantial variation across states. Only Alabama, Alaska, Michigan, New Jersey, Washington, Wisconsin, and the District of Columbia received B grades, indicating relatively strong laws. Four states received F grades and twelve received E grades, reflecting weak and less comprehensive child restraint laws. These findings highlight the need for stronger and more uniform child restraint laws nationwide.
To uncover the causal mechanism underlying e-bike violations during weekday commuting peak hours, this study develops an e-bike violation-behavior dataset using video recordings collected at signalized intersections. A composite violation risk indicator is then proposed. Subsequently, an interpretable Bayesian-optimized light gradient boosting machine (BO-LightGBM) model integrated with bivariate partial dependence plots (PDP) is developed to identify key influencing factors and reveal the interaction effects. The results indicate that violation risk is primarily driven by behavioral characteristics, particularly two-stage crossing behavior, observable looking behavior, and following behavior. Interaction analyses further reveal complex nonlinear patterns across factors. First, the occurrence of two-stage crossing, observable looking, and following behaviors markedly increases violation risk, and their co-occurrence exhibits clear cumulative effects. Second, violation risk is highest for riders traveling in groups of 7 to 10 and under moderate motor vehicle volumes (60 to 80 vehicles/5 min). In contrast, low (0 to 1 m/s) and moderate (2.5 to 3.5 m/s) riding speeds correspond to distinct decision patterns that are consistent with opportunistic waiting to violate and accelerated rushing-through, respectively. Finally, nonmotorized lane width shows a safety threshold of approximately 4-m widths below this threshold increase the risk of violation by 12%.
Accurately assessing the causal effects of speed limit policies on traffic operational risks is critical for enhancing freeway safety. While existing studies have relied on theoretical models or driving simulations, their associational analyses often suffer from model bias and fail to provide robust causal interpretations. This study introduces a dual machine learning (DML) causal inference framework to address this gap. First, integrate aggressive driving behavior and speed fluctuations into a Traffic Order Index (TOI). Second, use the conditional Shapley value index for confounding factor screening. Finally, construct a DML causal forest model to precisely quantify the conditional average treatment effects of speed limit signs on traffic operational risks. Empirical analysis of multi-source traffic data in Shandong Province shows superior estimation accuracy of the proposed method compared to baseline models. On average, the Lane-specific and vehicle type-specific Speed Limit Signs(La_SLS) (ATE = 0.190, p < 0.01) are more effective in improving traffic order than the Traditional Speed Limit sign (Tra_SLS) (ATE = 0.168, p < 0.01). This effect, however, is highly heterogeneous. The perceived safety benefit of La_SLS is most pronounced at interchanges and under adverse weather conditions. Counterfactual analysis reveals La_SLS in Section 34 boosts TOI by 3.09%, while Tra_SLS in Section 33 improves it by 2.37%.
The rapid expansion of photovoltaic installations along expressways has raised concerns regarding glare exposure and its potential impact on driving safety. This study employed a controlled driving simulation to examine the effects of glare intensity and incident angle on driver performance. Illumination intensity was set at three levels (A > B > C), with five incident angles (-60 degrees, -30 degrees, 0 degrees, +30 degrees, +60 degrees). A total of 36 participants completed the experiments, yielding 540 valid samples. A two-stage analytical framework was adopted, combining analysis of variance to quantify main and interaction effects with gradient boosting regression and SHapley Additive exPlanations(SHAP) to capture nonlinear responses and identify key contributors.Results reveal a dual-mechanism impairment pattern. Speed regulation and braking were most affected under high-intensity frontal glare, while steering stability and visual comfort were more sensitive to large incident angles under lower illumination. Frontal glare (0 degrees) posed the highest risk across all intensities. Warning prompts significantly mitigated impairment, reducing speed-stability impact by up to 86.6% under critical conditions. Vehicle speed further moderated these effects, with substantial amplification above 110 km/h.These findings provide quantitative insights for developing engineering countermeasures to enhance safety in photovoltaic-integrated expressway environments.
