Traffic Injury Risk Based on Mobility Patterns by Gender, Age, Mode of Transport and Type of Road
Abstract
:1. Introduction
2. Background
2.1. Influence of Mobility Patterns to the Exposure of Risk of RTI
2.2. Risk Factors in Road Accidents
3. Materials and Methods
3.1. Study Area
3.2. Study Design
3.3. Data Sources and Selection of Variables
- Gender: Men, women.
- Age group: 16–29 years old, 30–39 years old, 40–49 years old, 50–64 years old, over 64 years old.
- Mode of transport: Pedestrian, car (drivers and passengers of cars with or without trailers and disabled cars), motorcycle (drivers and passengers on motorcycle/moped), bicycle, public transport (passengers of public service vehicle up to 9 seats, regular bus, school bus and other bus).
- Type of road: Urban road, interurban rural road (hereafter rural road).
- Injury severity: Minor (victims hospitalised less than 24 h or not hospitalised), serious (victims hospitalised more than 24 h), fatal (death occurring within 24 h after the accident).
3.4. Statistical Analysis
4. Results
5. Discussion
6. Conclusions
- For pedestrian mode and on rural roads, males are more likely to suffer injuries of any degree of severity.
- For motorcycles and regardless of road type, males have a higher risk of serious or fatal injuries, and females have a higher risk of minor injuries.
- In cars, males have a higher risk of serious or fatal injuries regardless of road type, while the risk of minor injuries is higher for females on rural roads.
Author Contributions
Funding
Conflicts of Interest
Appendix A
Author and Location | Methodology | Mobility Exposure Measure | Mode of Transport | Details | |
---|---|---|---|---|---|
[10] Chen et al., 2016 New Mexico (USA) | Hierarchical ordered logit model | No | Drivers | Objective | To examine significant factors in predicting driver injury severities in rural non-interstate crashes |
Accident Data Road Type Year | Rural non-interstate crash dataset extracted from traffic crash records in New Mexico Rural 2010–2011 | ||||
Injury Severity | No injury, complaint of injury/possible injury, visible injury, incapacitating injury and death | ||||
Results | Road segments far from intersection, wet road surface condition, collision with animals, heavy vehicle drivers, male drivers and driver seatbelt used tend to induce less severe driver injury outcomes than the factors such as multiple-vehicle crashes, severe vehicle damage in a crash, motorcyclists, females, senior drivers, drivers with alcohol or drug impairment and other major collision types. | ||||
[11] Chen et al., 2016 New Mexico (USA) | Hierarchical Bayesian logistic model | No | Drivers | Objective | To examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes |
Accident Data Road Type Year | Rural interstate crash data extracted from two-year New Mexico crash records from 2010 to 2015 | ||||
Injury Severity | Low injury severity level (including no injury and complaint of injury) and high injury severity level (including visible injury, incapacitating injury and fatality) | ||||
Results | Road curve, functional and disabled vehicle damage in crash, single-vehicle crashes, female drivers, senior drivers, motorcycles and driver alcohol or drug involvement tend to increase the odds of drivers being incapably injured or killed in rural interstate crashes, while wet road surface, male drivers and driver seatbelt use are more likely to decrease the probability of severe driver injuries. | ||||
[12] Wu et al., 2016 New Mexico (USA) | Nested logit models and mixed logit models | No | Drivers | Objective | To analyse driver injury severities for single-vehicle crashes occurring in rural and urban areas |
Accident Data Road Type Year | Data on Single-vehicle Crashes Rural and Urban Areas 2010–2011 | ||||
Injury Severity | No injury, Possible injury, Visible injury, Incapacitating injury and fatality | ||||
Results | Significant differences exist between factors contributing to driver injury severity in single-vehicle crashes in rural and urban areas. However, what is common in both rural and urban areas is that the drivers, who are female, senior, alcohol impaired or involved in overturn or fixed-object crashes are more likely to be injured severely. | ||||
[13] Islam et al., 2017 Alabama (USA) | Random parameter logit models of injury severity | No | Motorcycle | Objective | To explore the factors contributing to the injury severity resulting from the motorcycle at-fault accidents in rural and urban areas |
Accident Data Road Type Year | Data on motorcycle at-fault crashes Rural and urban areas in Alabama 2010–2014 | ||||
Injury Severity | Fatal, major, minor and possible or no injury | ||||
Results | Clear weather, young motorcyclists and roadways without light were found significant only in the rural model. On the other hand, older female motorcyclists, horizontal curves and at intersections were found significant only in the urban model. Some variables (such as, motorcyclists under influence of alcohol, non-usage of helmet, high speed roadways, etc.) were found significant in both models. Men were less likely to be seriously or fatally injured on urban and rural roads. Specifically, on urban roads, young men were more likely to suffer minor injuries and older women serious and fatal injuries. | ||||
[14] Islam et al., 2014 Alabama (USA) | Random parameter logit models of injury severity | No | Pedestrians | Objective | To explore the factors contributing to the injury severity resulting from pedestrian at-fault crashes in rural and urban locations in Alabama |
Accident Data Road Type Year | Pedestrian at-fault crash data Rural and urban locations 2006 to 2010 | ||||
Injury Severity | Major injury (included fatal and incapacitating injuries), minor injury (non-incapacitating injuries) and possible/no injury (included no visible injuries, no injuries and property damage only crashes) | ||||
Results | There are differences between the influences of a variety of variables on the injury severities resulting from urban versus rural pedestrian at-fault accidents. Females and the 24–64 age group are more likely to be seriously injured in urban areas. | ||||
[15] Zwerling et al., 2005 USA | Decomposition method Yes (vehicle miles travelled) | Drivers | Objective | To explore the factors associated with increased fatal crash involvement rates in rural compared with urban communities | |
Accident Data Road Type Year | Data on the number of fatal crashes in rural and urban areas from 2001 | ||||
Injury Severity | Fatal | ||||
Results | The fatal crash incidence density was more than two times higher in rural than in urban areas. This was driven primarily by the injury fatality rate, which was almost three times higher in rural areas. The risk of fatal crashes was higher for younger men on all type of roads, while for older people, it was higher for men on urban roads and for women on rural ones. | ||||
[16] Pulido et al., 2016 Spain | Crude (CDRR) and adjusted (ADRR) death rate ratios | Yes (quasi-induced exposure) | Drivers | Objective | To compare the age and gender differences in death rates with and without adjustment by exposure |
Accident Data Road Type Year | Data of driver deaths Urban and non-urban roads 2004–2012 | ||||
Injury Severity | Fatal | ||||
Results | There are differences between CDRR and ADRR estimates. CDRR: highest traffic mortality among the youngest drivers, except for females in non-urban roads. ADRR: highest mortality among the oldest groups, especially in females, peaking among drivers > 74 years in all types of roads. Regarding differences by gender, both estimates revealed higher traffic mortality in males, although the differences were much smaller when using ADRR. CDRR and ADRR for males tended to converge as age increased. | ||||
[30] Vorko et al., 2006 Zagreb (Croatia) | Simple and bivariate analysis using χ2, odds ratio, and confidence interval of 95% | No | Not specified | Objective | To analyse the risks of urban traffic accidents |
Accident Data Road Type Year | Data of injured and killed persons in road traffic accidents 1999–2000 | ||||
Injury Severity | Killed, severely and mildly injured | ||||
Results | More fatal accidents occurred during night hours, on urban road links and at exceeding speed limit. More people were injured than killed on urban junctions. The highest combined risk of dying or being severely injured was found in males, driving at excessive speed, on urban links and with bad visibility. |
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Urban Roads | |||||||
---|---|---|---|---|---|---|---|
Minor Victims in Traffic Accidents (2008–2013) | |||||||
Men | Women | ||||||
Age (Years Old) | Hours Travel-People 2011 (Mill) | Victims | Rate 1 (95% CI) 2 | Hours Travel-People 2011 (Mill) | Victims | Rate 1 (95% CI) 2 | Rate Ratio Men/Women (95% CI) |
Pedestrian | |||||||
16–29 | 30.41 | 366 | 20.06 (18.01–22.12) | 36.14 | 424 | 19.55 (17.69–21.41) | 1.03 (0.89–1.18) |
30–39 | 18.74 | 238 | 21.17 (18.48–23.86) | 36.55 | 253 | 11.54 (10.12–12.96) | 1.84 (1.54–2.19) |
40–49 | 20.43 | 237 | 19.33 (16.87–21.79) | 36.50 | 309 | 14.11 (12.54–15.68) | 1.37 (1.16–1.62) |
50–64 | 41.53 | 338 | 13.57 (12.12–15.01) | 55.96 | 446 | 13.28 (12.05–14.52) | 1.02 (0.89–1.18) |
>64 | 51.43 | 456 | 14.78 (13.42–16.14) | 38.95 | 516 | 22.08 (20.18–23.99) | 0.67 (0.59–0.76) |
Total | 162.52 | 1635 | 16.77 (15.95–17.58) | 204.09 | 1948 | 15.91 (15.20–16.61) | 1.05 (0.99–1.13) |
Car | |||||||
16–29 | 30.95 | 3102 | 167.03 (161.16–172.91) | 37.97 | 2740 | 120.26 (115.76–124.76) | 1.39 (1.32–1.46) |
30–39 | 38.55 | 2068 | 89.41 (85.56–93.26) | 43.55 | 1879 | 71.91 (68.66–75.16) | 1.24 (1.17–1.32) |
40–49 | 41.18 | 1282 | 51.89 (49.05–54.73) | 42.54 | 1267 | 49.64 (46.91–52.37) | 1.05 (0.97–1.13) |
50–64 | 40.64 | 983 | 40.32 (37.80–42.84) | 21.25 | 980 | 76.88 (72.06–81.69) | 0.52 (0.48–0.57) |
>64 | 9.43 | 401 | 70.87 (63.94–77.81) | 8.62 | 289 | 55.85 (49.41–62.29) | 1.27 (1.09–1.48) |
Total | 160.74 | 7836 | 81.25 (79.45–83.05) | 153.94 | 7155 | 77.47 (75.67–79.26) | 1.05 (1.02–1.08) |
Motorcycle | |||||||
16–29 | 6.70 | 4418 | 1099.16 (1066.75–1131.57) | 1.14 | 1946 | 2857.46 (2730.50–2984.42) | 0.38 (0.36–0.41) |
30–39 | 3.60 | 2367 | 1097.16 (1052.96–1141.36) | 2.32 | 830 | 595.58 (555.06–636.10) | 1.84 (1.70–1.99) |
40–49 | 10.99 | 1904 | 288.63 (275.67–301.60) | 1.89 | 545 | 481.44 (441.02–521.86) | 0.60 (0.55–0.66) |
50–64 | 4.23 | 1221 | 480.89 (453.91–507.86) | 1.25 | 233 | 310.40 (270.54–350.26) | 1.55 (1.35–1.78) |
>64 | 1.40 | 275 | 327.75 (289.01–366.48) | 0.00 | 19 | - | - |
Total | 26.92 | 10,185 | 630.59 (618.34–642.83) | 6.60 | 3573 | 902.89 (873.29–932.50) | 0.70 (0.67–0.73) |
Rural Roads | |||||||
---|---|---|---|---|---|---|---|
Minor Victims in Traffic Accidents (2008–2013) | |||||||
Men | Women | ||||||
Age (Years Old) | Hours Travel-People 2011 (Mill) | Victims | Rate 1 (95% CI) 2 | Hours Travel-People 2011 (Mill) | Victims | Rate 1 (95% CI) 2 | Rate Ratio Men/Women (95% CI) |
Pedestrian | |||||||
16–29 | 0.64 | 39 | 101.56 (69.69–133.44) | 0.48 | 23 | 80.23 (47.44–113.02) | 1.27 (0.76–2.12) |
30–39 | 0.00 | 30 | - | 0.71 | 20 | 47.03 (26.42–67.65) | - |
40–49 | 0.23 | 39 | 282.61 (193.91–371.31) | 0.90 | 16 | 29.49 (15.04–43.94) | 9.58 (5.36–17.15) |
50–64 | 2.08 | 35 | 28.04 (18.75–37.34) | 0.29 | 20 | 114.39 (64.25–164.52) | 0.25 (0.14–0.42) |
>64 | 0.57 | 34 | 99.42 (66.00–132.83) | 1.78 | 19 | 17.79 (9.79–25.80) | 5.59 (3.19–9.79) |
Total | 3.52 | 177 | 83.81 (71.46–96.15) | 4.16 | 98 | 39.25 (31.48–47.02) | 2.14 (1.67–2.73) |
Car | |||||||
16–29 | 35.38 | 4801 | 226.19 (219.79–232.59) | 27.45 | 4234 | 257.11 (249.36–264.85) | 0.88 (0.84–0.92) |
30–39 | 61.83 | 3425 | 92.33 (89.24–95.42) | 39.10 | 2805 | 119.58 (115.15–124.00) | 0.77 (0.73–0.81) |
40–49 | 59.44 | 2188 | 61.35 (58.78–63.92) | 35.82 | 1855 | 86.31 (82.38–90.24) | 0.71 (0.67–0.76) |
50–64 | 48.23 | 1783 | 61.61 (58.75–64.47) | 21.80 | 1600 | 122.33 (116.33–128.32) | 0.50 (0.47–0.54) |
>64 | 12.08 | 1034 | 142.70 (134.00–151.40) | 5.69 | 691 | 202.48 (187.39–217.58) | 0.70 (0.64–0.78) |
Total | 216.95 | 13,231 | 101.64 (99.91–103.37) | 129.85 | 11185 | 143.56 (140.90–146.22) | 0.71 (0.69–0.73) |
Motorcycle | |||||||
16–29 | 1.16 | 768 | 1100.60 (1022.76–1178.45) | 0.37 | 229 | 1018.52 (886.60–1150.44) | 1.08 (0.93–1.25) |
30–39 | 3.87 | 785 | 338.07 (314.42–361.72) | 0.45 | 143 | 534.12 (446.58–621.66) | 0.63 (0.53–0.76) |
40–49 | 2.35 | 628 | 445.47 (410.63–480.31) | 0.19 | 106 | 911.11 (737.66–1084.56) | 0.49 (0.40–0.60) |
50–64 | 0.86 | 427 | 828.54 (749.95–907.13) | 0.22 | 52 | 390.05 (284.03–496.06) | 2.12 (1.59–2.83) |
>64 | 0.00 | 135 | - | 0.00 | 5 | - | - |
Total | 8.24 | 2743 | 554.71 (533.95–575.47) | 1.24 | 535 | 720.81 (659.73–781.89) | 0.77 (0.70–0.84) |
Urban Roads | |||||||
---|---|---|---|---|---|---|---|
Serious or Fatal Victims In Traffic Accidents (2008–2013) | |||||||
Men | Women | ||||||
Age (Years old) | Hours Travel-People 2011 (Mill) | Victims | Rate 1 (95% CI) 2 | Hours Travel-People 2011 (Mill) | Victims | Rate 1 (95% CI) 2 | Rate Ratio Men/Women (95% CI) |
Pedestrian | |||||||
16–29 | 30.41 | 37 | 2.03 (1.37–2.68) | 36.14 | 55 | 2.54 (1.87–3.21) | 0.80 (0.53–1,21) |
30–39 | 18.74 | 33 | 2.94 (1.93–3.94) | 36.55 | 31 | 1.41 (0.92–1.91) | 2.08 (1.27–3.39) |
40–49 | 20.43 | 47 | 3.83 (2.74–4.93) | 36.50 | 39 | 1.78 (1.22–2.34) | 2.15 (1.41–3.29) |
50–64 | 41.53 | 62 | 2.49 (1.87–3.11) | 55.96 | 76 | 2.26 (1.75–2.77) | 1.10 (0.79–1.54) |
>64 | 51.43 | 159 | 5.15 (4.35–5.95) | 38.95 | 189 | 8.09 (6.93–9.24) | 0.64 (0.52–0.79) |
Total | 162.52 | 338 | 3.47 (3.10–3.84) | 204.09 | 390 | 3.18 (2.87–3.50) | 1.09 (0.94–1.26) |
Car | |||||||
16–29 | 30.95 | 79 | 4.25 (3.32–5.19) | 37.97 | 39 | 1.71 (1.17–2.25) | 2.49 (1.69–3,65) |
30–39 | 38.55 | 35 | 1.51 (1.01–2.01) | 43.55 | 25 | 0.96 (0.58–1.33) | 1.58 (0.95–2.64) |
40–49 | 41.18 | 29 | 1.17 (0.75–1.60) | 42.54 | 19 | 0.74 (0.41–1.08) | 1.58 (0.88–2.81) |
50–64 | 40.64 | 39 | 1.60 (1.10–2.10) | 21.25 | 14 | 1.10 (0.52–1.67) | 1.46 (0.79–2.68) |
>64 | 9.43 | 21 | 3.71 (2.12–5.30) | 8.62 | 10 | 1.93 (0.73–3.13) | 1.92 (0.90–4.08) |
Total | 160.74 | 203 | 2.10 (1.82–2.39) | 153.94 | 107 | 1.16 (0.94–1.38) | 1.82 (1.44–2.30) |
Motorcycle | |||||||
16–29 | 6.70 | 478 | 118.92 (108.26–129.58) | 1.14 | 119 | 174.74 (143.34–206.13) | 0.68 (0.56–0.83) |
30–39 | 3.60 | 310 | 143.69 (127.70–159.69) | 2.32 | 46 | 33.01 (23.47–42.55) | 4.35 (3.19–5.93) |
40–49 | 10.99 | 266 | 40.32 (35.48–45.17) | 1.89 | 52 | 45.94 (33.45–58.42) | 0.88 (0.65–1.18) |
50–64 | 4.23 | 211 | 83.10 (71.89–94.31) | 1.25 | 38 | 50.62 (34.53–66.72) | 1.64 (1.16–2.32) |
>64 | 1.40 | 62 | 73.89 (55.50–92.29) | 0.00 | 3 | - | - |
Total | 26.92 | 1327 | 82.16 (77.74–86.58) | 6.60 | 258 | 65.20 (57.24–73.15) | 1.26 (1.10–1.44) |
Rural Roads | |||||||
---|---|---|---|---|---|---|---|
Serious or Fatal Victims in Traffic Accidents (2008–2013) | |||||||
Men | Women | ||||||
Age (Years Old) | Hours Travel-People 2011 (Mill) | Victims | Rate 1 (95% CI) 2 | Hours Travel-People 2011 (Mill) | Victims | Rate 1 (95% CI) 2 | Rate Ratio Men/Women (95% CI) |
Pedestrian | |||||||
16–29 | 0.64 | 39 | 101.56 (69.69–133.44) | 0.48 | 12 | 41.86 (18.18–65.54) | 2.43 (1.27–4.63) |
30–39 | 0.00 | 47 | - | 0.71 | 7 | 16.46 (4.27–28.66) | - |
40–49 | 0.23 | 40 | 289.86 (200.03–379.68) | 0.90 | 13 | 23.96 (10.94–36.99) | 12.10 (6.47–22.62) |
50–64 | 2.08 | 45 | 36.06 (25.52–46.59) | 0.29 | 25 | 142.98 (86.93–199.03) | 0.25 (0.15–0.41) |
>64 | 0.57 | 67 | 195.91 (149.00–242.82) | 1.78 | 16 | 14.98 (7.64–22.33) | 13.07 (7.58–22.56) |
Total | 3.52 | 238 | 112.69 (98.37–127.01) | 4.16 | 73 | 29.23 (22.53–35.94) | 3.85 (2.97–5.01) |
Car | |||||||
16–29 | 35.38 | 768 | 36.18 (33.62–38.74) | 27.45 | 323 | 19.61 (17.47–21.75) | 1.84 (1.62–2.10) |
30–39 | 61.83 | 593 | 15.99 (14.70–17.27) | 39.10 | 221 | 9.42 (8.18–10.66) | 1.70 (1.45–1.98) |
40–49 | 59.44 | 374 | 10.49 (9.42–11.55) | 35.82 | 202 | 9.40 (8.10–10.69) | 1.12 (0.94–1.32) |
50–64 | 48.23 | 364 | 12.58 (11.29–13.87) | 21.80 | 198 | 15.14 (13.03–17.25) | 0.83 (0.70–0.99) |
>64 | 12.08 | 232 | 32.02 (27.90–36.14) | 5.69 | 158 | 46.30 (39.08–53.52) | 0.69 (0.56–0.85) |
Total | 216.95 | 2331 | 17.91 (17.18–18.63) | 129.85 | 1102 | 14.14 (13.31–14.98) | 1.27 (1.18–1.36) |
Motorcycle | |||||||
16–29 | 1.16 | 395 | 566.07 (510.24–621.89) | 0.37 | 74 | 329.13 (254.14–404.12) | 1.72 (1.34–2.20) |
30–39 | 3.87 | 397 | 170.97 (154.15–187.79) | 0.45 | 34 | 126.99 (84.31–169.68) | 1.35 (0.95–1.91) |
40–49 | 2.35 | 328 | 232.67 (207.49–257.84) | 0.19 | 30 | 257.86 (165.59–350.14) | 0.90 (0.62–1.31) |
50–64 | 0.86 | 232 | 450.17 (392.24–508.09) | 0.22 | 17 | 127.52 (66.90–188.13) | 3.53 (2.16–5.78) |
>64 | 0.00 | 87 | - | 0.00 | 3 | - | - |
Total | 8.24 | 1439 | 291.00 (275.97–306.04) | 1.24 | 158 | 212.87 (179.68–246.07) | 1.37 (1.16–1.61) |
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González-Sánchez, G.; Olmo-Sánchez, M.I.; Maeso-González, E.; Gutiérrez-Bedmar, M.; García-Rodríguez, A. Traffic Injury Risk Based on Mobility Patterns by Gender, Age, Mode of Transport and Type of Road. Sustainability 2021, 13, 10112. https://doi.org/10.3390/su131810112
González-Sánchez G, Olmo-Sánchez MI, Maeso-González E, Gutiérrez-Bedmar M, García-Rodríguez A. Traffic Injury Risk Based on Mobility Patterns by Gender, Age, Mode of Transport and Type of Road. Sustainability. 2021; 13(18):10112. https://doi.org/10.3390/su131810112
Chicago/Turabian StyleGonzález-Sánchez, Guadalupe, María Isabel Olmo-Sánchez, Elvira Maeso-González, Mario Gutiérrez-Bedmar, and Antonio García-Rodríguez. 2021. "Traffic Injury Risk Based on Mobility Patterns by Gender, Age, Mode of Transport and Type of Road" Sustainability 13, no. 18: 10112. https://doi.org/10.3390/su131810112