Abstract
Background:
Although the number of pedestrian traffic accidents is decreasing worldwide, more than one-third of traffic fatalities in Iran are related to pedestrians. High-risk behaviors of pedestrians increase their vulnerability to road traffic injuries.Objectives:
This study aimed to investigate gender differences in adopting safe pedestrian behaviors in Tabriz, Iran.Methods:
This cross-sectional study was carried out among pedestrians aged 18 years and over (n = 508) living in the second municipal district of Tabriz, Iran. The stratified random sampling method was used to recruit a representative sample of adults from 23 July to 21 November 2019. A standardized, structured questionnaire was used for evaluating traffic behaviors in five domains (i.e., adhering to traffic rules, pedestrians’ traffic violations, positive pedestrian behaviors, pedestrians’ distraction, and pedestrians’ aggressive behaviors). The data was analyzed by SPSS version 22 using independent t-test, regression, chi-square, and ANOVA tests.Results:
Overall, 58.5% of the participants were female. Women had significantly higher scores in three domains of pedestrian traffic behaviors (i.e., no traffic violations, not being distracted, and not having aggressive behavior) than men (P < 0.001). Additionally, married women and women with higher education levels earned significantly higher scores than others in total pedestrian traffic behaviors. There were significant differences between men and women regarding the use of a personal car and walking daily more than an hour (P < 0.05).Conclusions:
Our findings suggest gender differences in the five dimensions of pedestrian traffic behaviors in Iran. Gender-specific risk reduction strategies in the Iranian pedestrians’ safety intervention programs may promote safe traffic behaviors of pedestrians.Keywords
1. Background
Traffic accident is the eighth leading cause of death worldwide, and it is the most common cause of mortality in people aged 12 - 29 years. Traffic accidents represent the greatest economic burden of disease across the world. According to the World Health Organization (WHO), annually 1.24 million people are killed on the world's roads, 10 million are disabled, and over 50 million are injured due to traffic accidents (1-4).
In Iran, traffic accident is the leading cause of years of life lost (13.5%) and premature death. It is the second leading cause of death, as it is estimated that 2.5% of traffic accidents in the world occur in Iran (5-8). Although the number of pedestrian traffic accidents is declining worldwide, Iranian pedestrians are 1.5 times more likely to die due to traffic accidents (9, 10).
Findings show that the rate of road traffic deaths among pedestrians in Iran is over 30% (11). Pedestrians are often vulnerable roadway users because of poor decision making or high-risk road crossing behaviors (12). Robust evidence shows that pedestrians not only are the most vulnerable victims, but they are also at fault for the accident (13).
For the prevention and control of pedestrian crashes, it is important to identify and understand the factors contributing to pedestrians’ high-risk road-crossing behaviors (14). Various studies have shown that when pedestrians observe other pedestrians crossing the road in an improper manner, they tend to consecutively cross the street by following them (15-17). Some studies suggest that using mobile phones while cross walking increases the rate of injuries among pedestrians (12, 18, 19).
Also, alcohol consumption and inadequate visibility of pedestrians on the roads have been mentioned as the key risk behaviors (20, 21). Personality traits, age, and gender were additionally found to be influential factors in pedestrian behaviors (22, 23). Studies have revealed that female pedestrians had less death rates and fatal injuries than men did (24, 25). Male pedestrians are more likely to cross carelessly (60%) and on a red light (64%) compared to women (40% and 36%, respectively) (26). Since traffic behaviors depend on social and cultural factors (27), in different societies and cultures, women and men tend to present different traffic behaviors.
2. Objectives
This study aimed to investigate gender differences in adopting safe pedestrian behaviors. There has also been no study in Iran regarding the role of gender in safe pedestrian behaviors.
3. Methods
3.1. Participants
This study was carried out among pedestrians aged 18 years and over (n = 508) living in the second municipal district of Tabriz city of Iran, which is a candidate safe community. District 2 was chosen for the study because of its distinctive traffic characteristics compared to all other municipal districts of Tabriz. In this district, large shopping centers, hospitals, and universities are located, causing heavy traffic congestion in this area. The inclusion criteria were age of 18 years and over and living in District 2 for at least 10 months. People with mental disabilities were excluded from the study.
3.2. Procedure
The cluster sampling method was used to select the sample of adults from 23 July to 21 November 2019. For this purpose, the district was divided into four sections, namely north, south, east, and west. Then, the researcher attended the health centers of each section and randomly selected the households according to their health records in the centers. From each household, people aged 18 years and over were enrolled in the study. The sample size was calculated at 250 according to the study of Jalilian et al. (25) and the sample size calculation formula .
The sample size was calculated based on the probability of pedestrian risk behavior of 46% (25) with a 95% confidence, an error of 5%, and considering 15% attrition rate (n = 500). Written informed consent was obtained from all the participants.
3.3. Data Collection and Questionnaire
We used a standardized, structured questionnaire for evaluating traffic behaviors in the past month (29). The questionnaire consisted of six questions related to demographic characteristics (i.e., age, sex, marital status, level of education, walking, and transportation) and 29 items addressing five domains of pedestrians' traffic behaviors, which were rated on a five-point Likert scale (i.e., never, rarely, sometimes, most often, and always). The first domain included seven items on adherence to traffic rules (when crossing the street, after complete stopping of cars and turning the pedestrian light into green), the second domain included 10 items on pedestrians’ traffic violations (Most people do not use the pedestrian bridge, rather they prefer to cross the road in risky conditions.) , the third domain with seven items measures positive pedestrian behaviors (e.g., I let the car pass, even if I have the priority to pass), the fourth domain with four items measures pedestrians’ distraction (e.g., I use hands-free), and the fifth domain contains two items on pedestrians’ aggressive behaviors (e.g., If I'm angry with the behavior of a motorcyclist, I hit his vehicle with my hands or feet). Walking rate per day and form of transportation (e.g., personal car, public transportation, and walking) were also asked from the participants.
The average content validity coefficient regarding relevancy, clarity, and overall average were 0.86, 0.88, and 0.87, respectively. To establish the reliability of the questionnaire, the intra-class correlation coefficient and Kendall's tau-b and kappa10 were eliminated, and the questionnaire’s Cronbach's alpha coefficient was 0.84.
3.4. Analysis
Statistical analyses were performed using SPSS, version 22. The normality of the data was analyzed by the Kolmogorov-Smirnov test. Continuous and discrete variables are presented as mean and standard deviation (SD) and number and percentage, respectively (Table 1). In this study, independent samples t-test, multivariate regression, One-way analysis of variance (ANOVA), and chi-square test were used.
Demographic Characteristics of the Participantsa
| Parameters | Men | Women | P-Value |
|---|---|---|---|
| Age | 0.027 | ||
| 18 - 27 | 44 (44.9) | 54 (55.1) | |
| 28 - 35 | 57 (37.5) | 95 (62.5) | |
| 36 - 43 | 51 (35.2) | 94 (64.8) | |
| ≤ 44 | 59 (52.2) | 54 (47.8) | |
| Marital status | |||
| Single | 54 (58.7) | 38 (41.3) | 0.001 |
| Married | 157 (37.7) | 259 (62.3) | |
| Level of education | 0.013 | ||
| Illiteracy & elementary | 31 (14.7) | 34 (11.4) | |
| Secondary | 84 (39.8) | 97 (32.7) | |
| Associate degree | 19 (9.0) | 42 (14.1) | |
| Bsc and over | 77 (36.5) | 124 (41.8) | |
| Walking rate per day | 0.001 | ||
| Less than 30 minutes a day | 70 (32.0) | 149 (68.0) | |
| 30 minutes to an hour | 89 (42.4) | 121 (57.6) | |
| Above an hour a day | 52 (65.8) | 27 (34.2) | |
| Transportation | 0.006 | ||
| Personal car | 140 (44.9) | 172 (55.1) | |
| Public transportation | 44 (30.8) | 99 (69.2) | |
| Walking | 27 (50.9) | 26 (49.1) |
4. Results
The mean age of the participants was 36.55 ± 10.44 years. Most of the participants (58.5%) were women, and 80.1% of them were married. More than half of the women had a higher level of education (63.4%). Nearly half of the participants (42.9%) stated that they walked for less than 30 minutes every day, and 57.1% stated that they used a personal car to move around the city. There were significant differences between men and women for using personal car and walking daily more than an hour (P < 0.05). About 44% of men stated that they used their personal car for transportation, and 65.8% walked more than an hour a day.
Table 1 shows the demographic characteristics of the participants by gender and statistical differences between them. As shown, more men had postgraduate education than women, and single women were more than single men.
Table 2 shows pedestrian traffic behaviors for men and women. There were significant differences in the mean scores of pedestrian traffic behaviors between men and women (P < 0.001). Women had significantly higher scores in three domains of pedestrian traffic behaviors (i.e., no traffic violation, not being distracted, and not having aggressive behavior) than men did. Additionally, married women and women with higher levels of education earned significantly higher scores than others in total pedestrian traffic behaviors and for no traffic violation, not being distracted, and not having aggressive behavior. Women in all age groups had significantly higher scores than men for not having aggressive behaviors.
| Characteristics | Number of questions (Min to Max score) | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total Score:29 (29 - 145) | Adherence to Traffic Rules:7 (7 - 35) | No Traffic Violation: 10 (10 - 50) | Positive Behavior: 6 (5 - 30) | No Traffic Distraction: 4 (4 - 16) | Not having Aggressive Behavior: 2 (2 - 10) | |||||||||||||
| Men | Women | P-Value | Men | Women | P-Value | Men | Women | P-Value | Men | Women | P-Value | Men | Women | P-Value | Men | Women | P-Value | |
| Age | ||||||||||||||||||
| 18 - 27 | 100 (14.77) | 109.57 (14.79) | 0.002 | 22.80 (4.43) | 24.56 (4.46) | 0.054 | 34.80 (8.15) | 38.76 (7.29) | 0.013 | 19.73 (4.25) | 21.36 (4.19) | 0.061 | 14.37 (3.31) | 15.83 (3.20) | 0.092 | 7.95 (2.30) | 9.06 (1.67) | 0.07 |
| 28 - 35 | 109.25 (15.60) | 112.76 (13.82) | 0.151 | 23.81 (4.18) | 24.63 (4.55) | 0.267 | 38.67 (6.99) | 40.20 (6.27) | 0.089 | 22.37 (4.56) | 21.74 (4.20) | 0.397 | 15.96 (3.01) | 16.63 (3.39) | 0.224 | 8.44 (2.19) | 9.16 (1.63) | 0.025 |
| 36 - 43 | 112.18 (14.80) | 116.04 (11.85) | 0.08 | 24.86 (5.15) | 24.95 (4.93) | 0.923 | 39.41 (6.33) | 40.25 (5.79) | 0.237 | 22.92 (4.66) | 23.50 (3.84) | 0.424 | 16.14 (3.14) | 17.49 (2.81) | 0.009 | 8.84 (1.92) | 9.46 (1.08) | 0.015 |
| ≤ 44 | 106.64 (19.03) | 113.69 (15.51) | 0.034 | 24.66 (4.62) | 24.43 (5.92) | 0.814 | 37.12 (9.17) | 40.33 (7.02) | 0.040 | 21.88 (4.17) | 21.89 (4.07) | 0.992 | 15.20 (4.32) | 17.61 (3.45) | 0.002 | 7.78 (2.84) | 9.43 (1.92) | 0.001 |
| Marital status | ||||||||||||||||||
| Single | 102.26 (16.35) | 108.05 (16.23) | 0.09 | 22.96 (4.94) | 23.37 (5.60) | 0.715 | 35.63 (8.82) | 38.39 (7.43) | 0.118 | 20.13 (4.58) | 21.13 (5.25) | 0.334 | 15.41 (3.09) | 16.24 (4.27) | 0.283 | 8.13 (2.48) | 8.29 (2.04) | 0.110 |
| Married | 109.03 (16.56) | 114.17 (13.33) | 0.001 | 24.48 (4.48) | 24.87 (4.78) | 0.404 | 38.29 (7.45) | 40.50 (6.29) | 0.001 | 22.39 (4.38) | 22.45 (3.93) | 0.958 | 15.58 (3.68) | 17.04 (3.06) | 0.001 | 8.29 (2.34) | 9.34 (1.46) | 0.001 |
| Educational level | ||||||||||||||||||
| Illiteracy & elementary | 96.10 (19.67) | 102.82 (13.01) | 0.106 | 22.29 (5.40) | 21.74 (4.90) | 0.66 | 33.26 (9.55) | 36.06 (5.57) | 014 | 20.03 (4.70) | 20.09 (4.56) | 0.961 | 13.74 (4.79) | 16.15 (3.93) | 0.03 | 6.77 (3.05) | 8.79 (2.26) | 0.003 |
| Secondary | 106.89 (16.24) | 113.47 (13.13) | 0.003 | 24.44 (4.45) | 23.82 (5.15) | 0.39 | 36.63 (8.13) | 40.56 (6.02) | 0.001 | 21.92 (4.16) | 22.44 (4.58) | 0.42 | 15.75 (3.52) | 17.23 (3.36) | 0.004 | 8.15 (2.47) | 9.45 (1.37) | 0.001 |
| Associate degree | 106.47 (17.23) | 110.14 (15.61) | 0.41 | 24 (4.13) | 25.1 (5.23) | 0.42 | 36.74 (8.23) | 37.69 (8.01) | 0.67 | 21.37 (4.41) | 22.07 (4.39) | 0.56 | 16 (2.44) | 16.36 (3.48) | 0.68 | 8.37 (2.06) | 8.93 (1.77) | 0.28 |
| Bsc and over | 112.45 (13.55) | 117.31 (12.28) | 0.009 | 24.45 (4.54) | 26.02 (4.1) | 0.013 | 40.64 (5.43) | 41.99 (5.70) | 0.09 | 22.53 (4.75) | 22.76 (3.35) | 0.69 | 15.91 (2.98) | 17.13 (2.81) | 0.004 | 8.92 (1.70) | 9.43 (1.33) | 0.02 |
| Walking rate per day | ||||||||||||||||||
| Less than 30 minutes a day | 108.21 (18.62) | 115.14 (13.50) | 0.002 | 24.41 (4.53) | 25.29 (4.71) | 0.197 | 37.97 (8.41) | 40.77 (6.60) | 0.008 | 22.63 (4.81) | 22.81 (3.90) | 0.772 | 15.21 (3.88) | 17.01 (3.08) | 0.001 | 7.99 (2.84) | 9.27 (1.64) | 0.001 |
| 30 minutes to an hour | 109.24 (14.99) | 111.50 (13.82) | 0.258 | 24.20 (4.82) | 24.17 (5.08) | 0.967 | 38.64 (6.74) | 39.43 (6.27) | 0.384 | 21.73 (4.63) | 21.74 (4.35) | 0.983 | 16.01 (3.26) | 16.91 (3.44) | 0.058 | 8.65 (1.96) | 9.25 (1.50)) | 0.014 |
| Above an hour a day | 102.75 (16.33) | 112.15 (15.22) | 0.015 | 23.46 (4.48) | 23.59 (4.92) | 0.905 | 35.35 (8.65) | 40.85 (6.50) | 0.005 | 20.87 (3.79) | 21.48 (4.14) | 0.510 | 15.15 (3.44) | 16.70 (3.34) | 0.059 | 7.92 (2.28) | 9.52 (1.25) | 0.001 |
| Transportation | ||||||||||||||||||
| Personal car | 107.96 (16.21) | 115.34 (13.11) | 0.001 | 24.04 (4.67) | 25.30 (5.01) | 0.024 | 37.88 (7.71) | 41.01 (5.92) | 0.001 | 21.99 (4.61) | 22.54 (4.12) | 0.270 | 15.45 (3.43) | 16.98 (3.02) | 0.001 | 8.35 (2.26) | 9.37 (1.38) | 0.001 |
| Public transportation | 106.14 (18.27) | 110.51 (15.46) | 0.143 | 24.04 (4.67) | 25.30 (5.01) | 0.994 | 37.27 (8.25) | 38.95 (7.30) | 0.224 | 21.27 (4.27) | 22.01 (4.17) | 0.333 | 15.68 (3.98) | 16.66 (3.69) | 0.153 | 7.86 (2.81) | 9.08 (1.86) | 0.003 |
| Walking | 105.78 (17.21) | 111.42 (9.94) | 0.151 | 24.41 (4.58) | 22.96 (4.52) | 0263 | 36.74 (8.40) | 40.08 (5.93) | 0.111 | 21.78 (4.58) | 21.21 (4.03) | 0.642 | 15.74 (3.39) | 17.79 (2.70) | 0.022 | 8.37 (2.18) | 9.54 (1.21) | 0.24 |
According to Table 3, gender was a significant predictor for the behaviors in all the domains, except for adherence to traffic rules and positive behaviors. Also, educational status predicted the behaviors in all the domains.
Predictors of Pedestrian Behaviors According to Demographic Characteristics and Behavioral Dimensions
| Total Score | Adherence to Traffic Rules | No Traffic Violation | Positive Behavior | No Traffic Distraction | Not Having Aggressive Behavior | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No. (%) | B | P-Value | B | P-Value | B | P-Value | B | P-Value | B | P-Value | B | P-Value | |
| Sex (ref : ≤ women) | |||||||||||||
| men | 211 (41.5) | -5.01 | 0.001 | -0.24 | 0.58 | -2.31 | 0.001 | -0.01 | 0.96 | -1.39 | 0.001 | -1.03 | 0.001 |
| Age (ref : ≤ 44) | |||||||||||||
| 18 – 27 | 98 (19.3) | -8.13 | 0.001 | -1.11 | 0.13 | -3.04 | 0.005 | -1.58 | 0.01 | -1.98 | 0.001 | -0.41 | 0.17 |
| 28 – 35 | 152 (29.9) | -3.59 | 0.05 | -1.28 | 0.037 | -0.80 | 0.36 | -0.64 | 0.23 | -0.68 | 0.11 | -0.17 | 0.47 |
| 36 – 43 | 145 (28.5) | -0.39 | 0.83 | -0.65 | 0.29 | -0.43 | 0.62 | 0.62 | 0.25 | -0.10 | 0.80 | 0.18 | 0.48 |
| Marital status (ref : Married) | |||||||||||||
| Single | 92 (18.1) | -2.1 | 0.27 | -1.12 | 0.07 | -0.58 | 0.52 | -0.85 | 0.13 | 0.44 | 0.31 | 0.01 | 0.97 |
| Level of education (ref : BSc and over) | |||||||||||||
| Illiteracy & Elementary | 65 (12.8) | -16.43 | 0.001 | -3.62 | 0.001 | -6.77 | 0.001 | -2.51 | 0.001 | -2.10 | 0.001 | -1.40 | 0.001 |
| Secondary | 181 (35.6) | -4.12 | 0.005 | -1.21 | 0.01 | -2.30 | 0.001 | -0.28 | 0.50 | 0.006 | 0.98 | -0.32 | 0.10 |
| Associate degree | 61 (12) | -5.98 | 0.002 | -0.51 | 0.45 | -4.14 | 0.001 | -0.69 | 0.25 | -0.57 | 0.23 | -0.54 | 0.05 |
| Walking (min/day) (ref : >60) | |||||||||||||
| 30 > | 219 (43.1) | 3.54 | 0.07 | 0.97 | 0.14 | 1.04 | 0.27 | 1.23 | 0.03 | 0.38 | 0.41 | -0.09 | 0.71 |
| 31-60 | 210 (41.3) | 2.86 | 1.39 | 0.42 | 0.50 | 1.10 | 0.23 | 0.41 | 0.47 | 0.64 | 0.15 | 0.27 | 0.29 |
| Transportation mode (ref : Walking or Bicycle) | |||||||||||||
| Personal car | 312 (4.61) | -2.83 | 0.21 | -0.51 | 0.49 | -0.44 | 0.68 | -0.34 | 0.60 | -1.25 | 0.01 | -0.27 | 0.36 |
| Public transportation | 143 (28.1) | -3.98 | 0.09 | -0.77 | 0.32 | -1.12 | 0.31 | -0.38 | 0.58 | -1.17 | 0.03 | -0.52 | 0.10 |
5. Discussion
This cross-sectional study was conducted to investigate gender differences in pedestrians’ traffic behaviors. Our findings indicated that gender differences exist in pedestrian traffic behaviors in Iran, just as in other countries (22, 30, 31). According to our findings, gender can be a predictor of pedestrian behaviors, such that women had safer traffic behaviors than men. Women had significantly higher safe pedestrian traffic behaviors scores than men in all age groups, except the 28 - 35 years’ age group. Gender has been identified to play a role in decision-making in road crossing in that men were more likely than women to cross the road in risky conditions (14). It appears that pedestrian crossing behavior is a predictable factor; thus, pedestrian accidents and deaths can be predicted and prevented (30).
The results showed a significant gender difference in not having aggressive traffic behavior in all the age groups. However, the gender difference for no traffic distraction was observed among pedestrians < 35 years of age, where female pedestrians older than 35 years of age being less distracted than others. Additionally, in line with Deb et al. study, female pedestrians aged 44 years and older had significantly higher scores than male pedestrians and other age groups for no traffic violation. Regarding the mean scores of men and women in the dimensions of violation, aggressive, and distraction, men had more unsafe behaviors than women (32). According to Koh et al., female pedestrians are less likely to violate the traffic rules (31), which can be explained by the tendency of women to be more conservative and more precise, while men tend to be more competitive and controlling (28).
According to our findings, with increasing age (both in women and in men), pedestrians had more safe behaviors and were more frequently assigned to the group of safe behaviors. Similarly, in the study of Antić et al. (33), age had a significant positive correlation with safe traffic behaviors and a negative correlation with violations. Torquato and Bianchi (34) also reported that young pedestrians in the age group of 17-25 years showed more violations than older pedestrians (25 - 49 years), while Granie et al. found that pedestrians aged 35 - 45 years had the most positive behaviors (35). It seems that although pedestrians have different behavioral patterns in both developing and developed countries, they show similar types of gender differences in traffic behaviors (28, 31-35). Other existing evidence confirms that advancing age leads to a reduction in violations in pedestrians and drivers. Age has been reported as a factor for decision making in crossing the road among pedestrians (14). Additionally, adolescents and college students use technology more than others, which may lead to distracted walking (36).
According to the findings of our study, with increasing the level of education, safe behaviors in pedestrians was significantly increased (both in women and in men), which was consistent with Zheng et al. results (37). In another research (38), pedestrians with higher levels of education were more likely to use pedestrian bridges because those with higher education were more concerned about their personal safety while crossing the street. The education level of pedestrians may determine their behaviors and decisions when crossing the road.
In our study, people who walked less than 30 minutes exhibited safe behaviors more than other people. Pedestrians who walked more frequently as a means of transport were more likely to display unsafe behaviors than private car drivers. The results of Granie et al. (35) are consistent with these findings. Pedestrians who walk to school, office, and shops almost every day because of their obligations, they are in a hurry and choose the shortest route and focus on reaching the destination. This causes certain violations and leads to the display of aggressive behaviors to other road users.
Understanding which groups of pedestrians exhibit the most unsafe behaviors may help planners and policymakers to account for them. Since female pedestrians showed safe traffic behaviors, it is necessary to design interventions to promote safe pedestrian traffic behaviors for men.
5.1. Limitations of the Study
The study has some limitations; first, it was performed in only one district of Tabriz. Second, given the fact that the data collection tool was a questionnaire and the information was collected as self-report, there is the potential of overestimating/underestimating the data. Third, children were not included in this study.
5.2. Conclusions
Our findings showed gender differences in the five dimensions of pedestrian traffic behaviors in Iran. Gender-specific risk reduction strategies (for example, increasing risk perception among male pedestrians in the national driving licensing curriculum) in Iranian pedestrians’ safety intervention programs may promote the safe traffic behaviors of pedestrians.
Acknowledgements
References
-
1.
Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):2095-128. [PubMed ID: 23245604]. https://doi.org/10.1016/S0140-6736(12)61728-0.
-
2.
World Health Organization. Global tuberculosis control: surveillance, planning, financing. World Health Organization,; 2008. Available from: https://apps.who.int/iris/handle/10665/43831.
-
3.
World Health Organization. Injuries and violence: The facts. Geneva:: WHO; 2010. Available from: http://apps.who.int/iris/bitstream/handle/10665/44288/9789241599375_eng.pdf?sequence=1.
-
4.
Pfortmueller CA, Marti M, Kunz M, Lindner G, Exadaktylos AK. Injury severity and mortality of adult zebra crosswalk and non-zebra crosswalk road crossing accidents: a cross-sectional analysis. PLoS One. 2014;9(3). e90835. [PubMed ID: 24595100]. [PubMed Central ID: PMC3940932]. https://doi.org/10.1371/journal.pone.0090835.
-
5.
Naghavi M, Abolhassani F, Pourmalek F, Lakeh M, Jafari N, Vaseghi S, et al. The burden of disease and injury in Iran 2003. Popul Health Metr. 2009;7:9. [PubMed ID: 19527516]. [PubMed Central ID: PMC2711041]. https://doi.org/10.1186/1478-7954-7-9.
-
6.
Hatamabadi H, Vafaee R, Hadadi M, Abdalvand A, Esnaashari H, Soori H. Epidemiologic study of road traffic injuries by road user type characteristics and road environment in Iran: a community-based approach. Traffic Inj Prev. 2012;13(1):61-4. [PubMed ID: 22239145]. https://doi.org/10.1080/15389588.2011.623201.
-
7.
Karbakhsh M, Zandi NS, Rouzrokh M, Zarei MR. Injury epidemiology in Kermanshah: the National Trauma Project in Islamic Republic of Iran. East Mediterr Health J. 2009;15(1):57-64. [PubMed ID: 19469427].
-
8.
Motamedi MH, Dadgar E, Ebrahimi A. Curbing road traffic accidents--the major cause of facial fractures. Int J Emerg Ment Health. 2014;16(2):326-7. [PubMed ID: 25585486]. https://doi.org/10.4172/1522-4821.1000113.
-
9.
Beck LF, Dellinger AM, O'Neil ME. Motor vehicle crash injury rates by mode of travel, United States: using exposure-based methods to quantify differences. Am J Epidemiol. 2007;166(2):212-8. [PubMed ID: 17449891]. https://doi.org/10.1093/aje/kwm064.
-
10.
World Health Organization. Pedestrian safety: a road safety manual for decision-makers and practitioners. WHO; 2013. Available from: https://www.who.int/roadsafety/projects/manuals/pedestrian_manual.pdf.
-
11.
Rahimi AM, Kazemi M, Movaghari H. An accident prediction model for fatalities in rural areas. Traffic Management Studies. 2012;6(23):29-46.
-
12.
Zhou R, Horrey WJ, Yu R. The effect of conformity tendency on pedestrians' road-crossing intentions in China: An application of the theory of planned behavior. Accid Anal Prev. 2009;41(3):491-7. [PubMed ID: 19393798]. https://doi.org/10.1016/j.aap.2009.01.007.
-
13.
Ulfarsson GF, Kim S, Booth KM. Analyzing fault in pedestrian-motor vehicle crashes in North Carolina. Accid Anal Prev. 2010;42(6):1805-13. [PubMed ID: 20728631]. https://doi.org/10.1016/j.aap.2010.05.001.
-
14.
Rosenbloom T. Crossing at a red light: Behaviour of individuals and groups. Transp Res Part F Traffic Psychol Behav. 2009;12(5):389-94. https://doi.org/10.1016/j.trf.2009.05.002.
-
15.
Hughes RL. A continuum theory for the flow of pedestrians. Transport Res B-Meth. 2002;36(6):507-35. https://doi.org/10.1016/s0191-2615(01)00015-7.
-
16.
Meng X, Liao M, Wang K, Tan W. Group behaviors in pedestrian traffic simulation model. International Conference on Transportation Engineering 2009. Chengdu , China. 2009. p. 1086-91.
-
17.
Yagil D. Beliefs, motives and situational factors related to pedestrians’ self-reported behavior at signal-controlled crossings. Transp Res Part F Traffic Psychol Behav. 2000;3(1):1-13. https://doi.org/10.1016/s1369-8478(00)00004-8.
-
18.
Nasar J, Hecht P, Wener R. Mobile telephones, distracted attention, and pedestrian safety. Accid Anal Prev. 2008;40(1):69-75. [PubMed ID: 18215534]. https://doi.org/10.1016/j.aap.2007.04.005.
-
19.
Granié M. Effects of gender, sex-stereotype conformity, age and internalization on risk-taking among adolescent pedestrians. Saf Sci. 2009;47(9):1277-83. https://doi.org/10.1016/j.ssci.2009.03.010.
-
20.
Kwan I, Mapstone J. Visibility aids for pedestrians and cyclists: A systematic review of randomised controlled trials. Accid Anal Prev. 2004;36(3):305-12. https://doi.org/10.1016/s0001-4575(03)00008-3.
-
21.
Miles-Doan R. Alcohol use among pedestrians and the odds of surviving an injury: Evidence from Florida law enforcement data. Accid Anal Prev. 1996;28(1):23-31. https://doi.org/10.1016/0001-4575(95)00030-5.
-
22.
Herrero-Fernández D, Macía-Guerrero P, Silvano-Chaparro L, Merino L, Jenchura EC. Risky behavior in young adult pedestrians: Personality determinants, correlates with risk perception, and gender differences. Transp Res Part F Traffic Psychol Behav. 2016;36:14-24. https://doi.org/10.1016/j.trf.2015.11.007.
-
23.
Brosseau M, Zangenehpour S, Saunier N, Miranda-Moreno L. The impact of waiting time and other factors on dangerous pedestrian crossings and violations at signalized intersections: A case study in Montreal. Transp Res Part F Traffic Psychol Behav. 2013;21:159-72. https://doi.org/10.1016/j.trf.2013.09.010.
-
24.
Holland C, Hill R. The effect of age, gender and driver status on pedestrians' intentions to cross the road in risky situations. Accid Anal Prev. 2007;39(2):224-37. [PubMed ID: 16979132]. https://doi.org/10.1016/j.aap.2006.07.003.
-
25.
Jalilian M, Mostafavi F, Mahaki B, Delpisheh A, Rad GS. An application of a theory of planned behaviour to determine the association between behavioural intentions and safe road-crossing in college students: perspective from Isfahan, Iran. J Pak Med Assoc. 2015;65(7):742-6. [PubMed ID: 26160084].
-
26.
Holland C, Hill R. Gender differences in factors predicting unsafe crossing decisions in adult pedestrians across the lifespan: a simulation study. Accid Anal Prev. 2010;42(4):1097-106. [PubMed ID: 20441818]. https://doi.org/10.1016/j.aap.2009.12.023.
-
27.
Nordfjærn T, Şimşekoğlu Ö. The role of cultural factors and attitudes for pedestrian behaviour in an urban Turkish sample. Transp Res Part F Traffic Psychol Behav. 2013;21:181-93. https://doi.org/10.1016/j.trf.2013.09.015.
-
28.
Gneezy U, Pietrasz A. When half of the men are more competitive than all women. Unpublished manuscript. 2013.
-
29.
Sadeghi Bazargan H, Haghighi M, Heydari ST, Soori H, Rezapur Shahkolai F, Motevalian SA, et al. Developing and validating a measurement tool to self-report pedestrian safety-related behavior: The Pedestrian Behavior Questionnaire (PBQ). Bull Emerg Trauma. 2020;8(4):229-35. [PubMed ID: 33426138]. [PubMed Central ID: PMC7783302]. https://doi.org/10.30476/beat.2020.86488.
-
30.
Tom A, Granie MA. Gender differences in pedestrian rule compliance and visual search at signalized and unsignalized crossroads. Accid Anal Prev. 2011;43(5):1794-801. [PubMed ID: 21658507]. https://doi.org/10.1016/j.aap.2011.04.012.
-
31.
Koh PP, Wong YD, Chandrasekar P. Safety evaluation of pedestrian behaviour and violations at signalised pedestrian crossings. Saf Sci. 2014;70:143-52. https://doi.org/10.1016/j.ssci.2014.05.010.
-
32.
Deb S, Strawderman L, DuBien J, Smith B, Carruth DW, Garrison TM. Evaluating pedestrian behavior at crosswalks: Validation of a pedestrian behavior questionnaire for the U.S. population. Accid Anal Prev. 2017;106:191-201. [PubMed ID: 28628811]. https://doi.org/10.1016/j.aap.2017.05.020.
-
33.
Antić B, Pešić D, Milutinović N, Maslać M. Pedestrian behaviours: Validation of the Serbian version of the pedestrian behaviour scale. Transp Res Part F Traffic Psychol Behav. 2016;41:170-8. https://doi.org/10.1016/j.trf.2016.02.004.
-
34.
Torquato RJ, Bianchi ASA. Comportamento de Risco do Pedestre ao Atraversaar a Rua: Um Estudo com Universitarios. Transporte: Teoria e aplicação. 2010;2(1):19-41. Brazilian.
-
35.
Granie MA, Pannetier M, Gueho L. Developing a self-reporting method to measure pedestrian behaviors at all ages. Accid Anal Prev. 2013;50:830-9. [PubMed ID: 22836115]. https://doi.org/10.1016/j.aap.2012.07.009.
-
36.
Wells HL, McClure LA, Porter BE, Schwebel DC. Distracted pedestrian behavior on two urban college campuses. J Community Health. 2018;43(1):96-102. [PubMed ID: 28695427]. [PubMed Central ID: PMC5762430]. https://doi.org/10.1007/s10900-017-0392-x.
-
37.
Zheng T, Qu W, Ge Y, Sun X, Zhang K. The joint effect of personality traits and perceived stress on pedestrian behavior in a Chinese sample. PLoS One. 2017;12(11). e0188153. [PubMed ID: 29190750]. [PubMed Central ID: PMC5708679]. https://doi.org/10.1371/journal.pone.0188153.
-
38.
Wu Y, Lu J, Chen H, Wu L. Identification of contributing factors to pedestrian overpass selection. J Traffic Transp Eng. 2014;1(6):415-23. https://doi.org/10.1016/s2095-7564(15)30291-9.