One of the major challenges of autonomous vehicles (AV) is their interaction with pedestrians. Unofficial interactions such as gestures, eye contact, waving, and flashing lights are very common behavioral patterns for drivers to express their intent to give priority. In our research we composed a virtual reality experiment for a pedestrian crossing in an urban environment in order to test pedestrians’ reactions on an LED light display mounted on a virtual AV. Our main research interest was to investigate whether communication patterns influence the decision making of pedestrians when crossing the road. In a VR environment, four scenarios were created with a vehicle approaching a pedestrian crossing with different speeds and displaying a special red/green sign to pedestrians. Here, 51 persons participating in the experiment had to decide when crossing is safe. Results show that the majority of people indicated they would cross in the time windows when it was actually safe to cross. Male subjects made their decision to cross slightly faster but no significant differences were found in the decision making by gender. It was found that age is not an influencing factor, either. Overall, a quick learning process was experienced proving that explicit communication patterns are self-explaining.

This study explores road users’ behaviors and accident analyses on different travel modes in the Bangkok Metropolitan Region (BMR). The questionnaire survey was adopted and designed based on contributing factors related to risk behavior perceptions in different travel modes. A total of 3000 participants submitted questionnaires that provided data for a multiple regression model analysis. The results indicated that different travel modes have different risk behavior perceptions. Road users of vulnerable modes of travel, such as walking and cycling, were more aware of risky behaviors than users of others. Risky, violent behavior may occur due to fatigue, driving while taking drugs, or aggressive tactics where the driver may lose control and cause an accident. However, driver negligence, especially violating traffic rules, may sometimes cause risky behavior. The results show that age, gender, education level, income level, marital status, driving experience, accident experience, and attitude toward road safety affect risk behavior perception. In conclusion, the differentiation of vehicles plays a vital role as a critical issue that should be understood for effectively mitigating risks in different travel patterns.

Pedestrian safety at signalized intersections is a key component of sustainable urban mobility, as safer walking environments support active transportation, reduce crash risk, and improve the inclusiveness of urban transport systems. This study presents a structured review of pedestrian–vehicle conflicts based on a systematic PRISMA-guided literature search, synthesizing 60 studies with emphasis on operational conditions, behavioral factors, infrastructural characteristics, and surrogate safety measures. The review examines the application of surrogate safety measures (SSMs), including Time-to-Collision (TTC), Post-Encroachment Time (PET), Pedestrian Path Deviation (PPD), and Deceleration-to-Safety Time (DST). The findings reveal significant variability in threshold definitions and methodological approaches, which limits the comparability and transferability of results across different traffic contexts. Building on this synthesis, the paper proposes an integrated conceptual framework linking behavioral, operational, and infrastructural determinants to conflict occurrence and severity. The analysis shows that existing studies often treat these factors in isolation, reducing the generalizability of current models. Overall, this review identifies key methodological inconsistencies in surrogate safety indicators and emphasizes the need for standardized yet context-sensitive thresholds and locally validated conflict models to improve the comparability and transferability of pedestrian–vehicle conflict assessments.