Drivers should react quickly in dilemma zones at signalized intersections, where ill-timed decisions may result in rear-end or angular collisions with other vehicles. Recent advancements in connected vehicle (CV) technologies, particularly cellular vehicle-to-everything (C-V2X), are expected to enhance driver decision-making by providing real-time traffic information. Despite this, most previous studies have not considered the latest C-V2X specifications, leaving critical questions unanswered about how drivers interact with and benefit from this technology in dilemma-zone scenarios. To address this gap, this study builds a co-simulation platform that integrates Unity and VISSIM to simulate four communication conditions: (1) no communication (baseline), (2) perfect communication (green-light countdown), (3) interrupted communication (green-light countdown with loading delays), and (4) communication loss due to the absence of smart infrastructure (out of service information). Sixty-two licensed drivers participated in four randomized trials, each with multiple unpredictable green-to-yellow transitions designed to capture dilemma-zone responses. Driving performance was assessed in terms of stop-or-go decisions and red-light running outcomes. Results of the random parameters binary logit model for stop-or-go decisions indicate that, compared to no communication, drivers are more inclined to proceed through the intersection when communication is lost. In contrast, perfect communication and communication interruption generally reduce this tendency. Furthermore, significant interaction effects revealed the observed heterogeneity, indicating that drivers with specific driving histories respond differently under communication interruption and loss conditions. For the red-light running outcomes, the descriptive analysis shows that under the perfect communication condition, the proportion of red-light running decreases by 3.44% among drivers. Interestingly, even interrupted communication leads to a 2.19% decrease in the proportion of red-light running outcomes. These findings demonstrate the complex ways in which C-V2X-based information can influence driver decisions, emphasizing the need for robust implementation strategies that are context-aware. This study sheds light on how drivers interact with emerging C-V2X systems and provides insights for road authorities and policymakers seeking to enhance safety and reduce crash risks at signalized intersections.

Reducing risky behaviors and traffic violations is crucial for preventing road trauma among private and commercial motorcyclists. While legal interventions such as road rules and police enforcement have been somewhat effective, there is a gap in understanding how motorcyclists perceive these deterrents and the psycho-social factors influencing their effectiveness. This research aims to explore the interplay between motorcyclists’ psycho-social predispositions and their responses to legal interventions. It also compares two distinct groups of riders - private and commercial motorcyclists - within the urban environment of Da Nang, Vietnam, providing a nuanced consideration of the influence of work-related impacts on risky behavior, which can enable targeted interventions. We propose a theoretical model integrating the Theory of Planned Behavior and Deterrence Theory to analyze how psycho-social and punitive factors influence riders’ traffic violations. A total of 423 delivery riders and 411 private riders in Da Nang participated in the study. The results show that attitude, perceived behavioral control, and perceptions of deterrence significantly impact riders’ intentions to engage in risky behaviors, with delivery riders being particularly responsive to law enforcement and penalties. To enhance policy effectiveness, interventions should employ tailored enforcement strategies that account for the unique motivations and perceptions of each rider group. For example, increasing the visibility and consistency of law enforcement could be particularly impactful for delivery riders, who are more sensitive to immediate deterrents. Policy adjustments should also focus on reshaping riders’ attitudes toward safety and adjusting their perceived control over riding situations. By tailoring interventions to effectively influence each group's behavior, we can ultimately reduce traffic incidents.