Investigating the Relationship between Socioeconomic Status and Traffic Behaviors of Pedestrians in Marand

Extended Abstract

Background

Pedestrians are one of the most vulnerable groups in traffic accidents and are at a higher risk than other road users. Therefore, the death rate for pedestrians is 1.5 times higher than that of vehicle occupants. Traffic accidents among pedestrians are caused by the complex influence of multiple individual, vehicle, and environmental factors. Socioeconomic status is a known risk factor for traffic behaviors. Therefore, families and individuals living in more deprived socioeconomic areas are at a greater risk of traffic accidents compared to others. Considering the high number of pedestrian accidents in Marand and their subsequent physical, psychological, social, and economic damages on the individual, family, and society, the current study aimed to investigate the relationship between the socioeconomic status and traffic behavior of pedestrians in Marand.

Methods

The current study was a cross-sectional descriptive analytical one, which was conducted in the city of Marand, East Azerbaijan, from the fall of 2018 to the spring of 2019. The statistical population included all the pedestrians visiting health centers. The inclusion criteria were as follows: participants had to be pedestrians aged 18 and older, able to stand and walk independently, residents of Marand, and willing to participate in the study. People who had restrictive musculoskeletal disorders, neurological disorders (stroke, Parkinson's disease, paralysis, etc.), vision or hearing disorders, or did not wish to continue participating in the research were excluded from the study. Using Cochran's formula and assuming a Type I error of 0.05, a test power of 0.9, and an estimation error of 0.08, the sample size of 370 people was calculated. By considering health centers as clusters, sampling was done via the cluster sampling method. The final sample size was estimated to be 610 people considering the effect of cluster sampling and 10% attrition. Due to the spread of the COVID-19 disease, the number of people visiting health centers had decreased; a total of 515 questionnaires were completed during the data collection period. The data collection tools used in this study included a short version of the socioeconomic status questionnaire and the pedestrian traffic behavior questionnaire. The data were analyzed using SPSS software (version 23), which included descriptive statistics, independent t-test, and one-way analysis of variance (ANOVA).

Results

According to the results, most of the participants in the study were male (67.6%). Approximately 37% of the pedestrians studied were between the ages of 29 and 38. Almost half of the pedestrians had a literacy level equivalent to grades 7 to 12 (47.8%). Most of the pedestrians were married (82.7%). The mean (standard deviation) of the total pedestrian behavior score was 82.07 (7.80). Among the behavioral dimensions, the highest score was related to the dimension of aggressive behavior, i.e., 90.64 (15.87), and the dimension following traffic rules had the lowest score, i.e., 69.65 (12.94). The results of the independent t-test showed that among the different dimensions of traffic behavior, only the average score of aggressive behavior had a significant relationship with gender (P < 0.05). According to the results of one-way ANOVA, the average score of positive behaviors was significantly different in various age groups (P < 0.05). Married pedestrians had fewer distracted behaviors than single pedestrians. The results of the ANOVA showed that in all the subscales of traffic behavior, with the improvement of the socioeconomic status, the score of traffic behavior increased. However, this increase was significant only in the subscales of adherence to traffic rules, positive behaviors, and distraction (P < 0.05).

Conclusion

The results of the current study showed that pedestrians who had a lower socioeconomic status (jobs with lower income and lower education) showed more unsafe behaviors while crossing the road. Also, unsafe behaviors were more common among female pedestrians and young age groups. Considering the importance and severity of the consequences caused by the unsafe behaviors of pedestrians, measures such as implementing educational interventions through different media, holding educational campaigns, and environmental interventions should be taken to improve the knowledge and behavior of pedestrians. In designing these interventions, demographic characteristics such as gender, age, and socioeconomic status should be considered in order to improve traffic behaviors among high-risk pedestrians. Additionally, the results of the current study can be presented to relevant organizations such as municipalities, traffic organizations, health centers, and the police to design and implement interdepartmental cooperation in order to promote safe traffic behaviors. These measures may lead to reducing traffic accidents among pedestrians in Marand.