Impact of Driver’s Age and Gender, Built Environment, and Road Conditions on Crash Severity: A Logit Modeling Approach
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data
- Regional and Locational Factors
- 2.
- Socio-Economic Factors
- 3.
- Land-Use Factors
- 4.
- Circulation and Network Factors
- 5.
- Physical Environment Factors
2.2. Modeling Methodology
- X1: age and gender of the at-fault driver;
- X2: vehicle type;
- X3: timing of crash;
- X4: crash location;
- X5: road condition;
- X6: other crash factors.
- Z1: regional and locational;
- Z2: socio-economic;
- Z3: land use;
- Z4: circulation and network;
- Z5: physical characteristics.
- : constant;
- : coefficient for categories of crash-specific variables;
- : coefficient for categories of TAZ-related variables;
- : categories of crash-specific variables;
- : categories of TAZ-related variables.
3. Results
3.1. Descripctive Statistics
3.2. Results of Logit Model
3.3. Age and Gender of the Culpable Drivers
- (1).
- Male Drivers
- (2).
- Female Drivers
3.4. Type of Vehicle
3.5. Timing of Crash
3.6. Type of Road and Intersection
3.7. Road Conditions
3.8. Alcohol, Drug, and Work Zone
3.9. Regional and Locational Factors
3.10. Socio-Economic Factors
3.11. Land-Use
3.12. Neighborhood Physical Factors
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Models | Variables | Main Results | Reference |
---|---|---|---|
Logistic model | Accident severity, namely, location, cause of accident, etc. | Contributing factors to accident severity, with data derived from a sample of 560 crashes in Riyadh, Saudi Arabia. Location (intersection) is closely associated with crash severity. | [2] |
Hit-and-run crashes, driver characteristics, vehicle types, crash characteristics, roadway features and environmental characteristics. | Identifies the associated factors of hit-and-run crashes in Singapore. Male drivers, drivers aged between 45 and 69 years, two-wheel vehicles are more associated with these crashes. | [3] | |
Road design elements (vertical and horizontal curves) road access (bus stops, public and private access lanes) and land use (gas stations, parking places). | Contributing road characteristics to crashes severity in Poland. The type of shoulders on both sides of a roadway, area type, pedestrian sidewalks, and intersection significantly influence crash severity. | [4] | |
Driver characteristics, road condition, collision type, safety equipment usage, driver ejection, alcohol involvement, speed limit. | Examines the relationship between crash severity and the characteristics of gravel roads, using data over 1996–2005 in Kansas. Safety equipment usage, alcohol involvement, speed limit, and driver-related factors, have significant influences on crash severity. | [5] | |
Educational attainment, median housing value, gender and age, median housing value, rurality percentage at the zip code level. | Investigates whether the socioeconomic characteristics of a driver-based residence zip code have any relationship with the likelihood of post-crash medical services. | [6] | |
Binary logit model | Wind data, overturning truck crash. | Wind speed is a critical factor in overturning freight vehicle crashes in Wyoming. | [7] |
Road (location, street, paving stretch, surface, signposting), external environment (day of week, weather, hour, season), driver (age, gender, license), accident (crash type, vehicle, dead). | It is found that male drivers are more likely to be involved in fatal crashes in an intersection crash than female drivers. Drivers aged 65 years and above are more likely to be involved in fatal intersection crashes than other age groups. Drivers aged below 45 years have lower probability to be involved in a front/side collision. | [8] | |
Generalized logit model | Railway features, highway features, crossing features, traffic controls, and land use. | Key factors for crash severity at a railroad grade crossing using logit analysis. The number of daily trains and the presence of a law enforcement camera can influence crash severity. | [9] |
Ordered mixed logit model | Driver, traffic, crash-related and vehicle characteristics. | Certain factors including tripped rollovers can increase chances of fatal injury and injuries can be sustained by moped riders. | [10] |
Mixed logit model | Roadway characteristics, vehicle attributes, and driver behavior. | The effect of the use of safety belts in single and multi-occupant vehicles in Indiana. Safety belt is associated with vehicle type, gender, time, but these effects vary across the population. | [11] |
Average daily traffic per lane, average daily truck traffic, truck percentage, interchanges per mile and weather effects such as snowfall, the number of horizontal curves, number of grade breaks per mile and pavement friction. | Volume-related variables are good for random parameters, while roadway-related parameters are good fixed parameters for these crashes. | [12] | |
Roadway characteristics, vehicle attributes, and driver behavior. | The possible unobserved heterogeneity in pedestrian injury severity caused by motor vehicle crashes in North Carolina. Darkness, truck, freeway, and age of pedestrians can increase the possibility of fatal injury in pedestrian-related crashes. | [13] | |
Roadway-surface conditions, driver’s age, gender. | Drivers in different age and gender groups perceive and react to road-surface conditions in different ways, which may result in varying crash severity. | [14] | |
Driver characteristics (age, gender.), vehicle attributes, and roadway characteristics (light condition,). | Driver injury severity in single-vehicle crashes in California, focusing on the heterogeneous effects of age and gender. Male driver, drunk driving, unsafe speed, older driver, driving older vehicle and driving in darkness without streetlights can increase the probability of fatal crashes. | [15] | |
Traffic volume, distance of the crash to the nearest ramp, and detailed driver’s age, vehicle types, and sides of impact, etc. | At-fault driver’s influential factors on crash severity on urban freeways in Florida. Age, traffic volume, distance of the crash to the nearest ramp, vehicle type, side of impact, and percentage of trucks are important. | [16] | |
Manner of collision, motorcycle rider and non-motorcycle driver and vehicle actions, roadway and environmental conditions, location and time, motorcycle rider and non-motorcycle driver and vehicle attributes, etc. | Adopts mixed logit models to investigate the effects of crash factors on crash severity. Non-uniform effects of rear-end collisions, roadway speed limit, type of area, riding season, motorcyclist’s gender, light conditions, roadway surface conditions, helmet use influence on crash severity. | [17] | |
Multinomial logit model | Environmental factors, roadway conditions, vehicle characteristics, and rider attributes. | Investigates the influential factors on crash severity (five levels) in single-vehicle motorcycle accidents, addressing the need of multinomial logit formulation. | [18] |
Driver characteristics (age, gender.), single-vehicle accidents involving passenger cars. | The effect of driver age and gender on crash injury severity in single-vehicle crashes. There are significant different behavioral issues between genders in different age groups | [19] | |
Roadway characteristics, accident types, weather conditions, etc. | Weather condition is frequently associated with roadway safety. | [20] | |
Road characteristics, vehicle attributes, and driver behavior. | Crash injury severity and influential factors (crosswalk spacing, presence of both horizontal and vertical curves), vehicle type, signal timings, alcohol, gender, light condition, weekend. | [21,22,23,24] | |
Pedestrian age, male driver, intoxicated driver, traffic sign, commercial area, darkness with or without streetlights, sport-utility vehicle, truck, freeway, two-way divided roadway, speeding-involved, off roadway, motorist turning or backing, both driver and pedestrian at fault, and pedestrian only at fault, etc. | Develops heteroskedastic logit analysis to investigate the influential factors associated with the injury severity of pedestrians in motor-vehicle crashes in North Carolina. Pedestrian age can increase the probability of fatal injury and it grows more pronounced with increasing age past 65 years. | [25] | |
Monetary cost factors, time cost factors, throughputs, airports, etc. | In terms of the methodology, it adopts a multinomial logit model. It shows that reducing air cargo connecting time at an airport via adequate investment in capacity is important in terms of saving time costs. | [26] | |
Standard multinomial logit and mixed logit models | Roadway characteristics (intersection and non-intersection), vehicle attributes, and driver characteristics (bicyclist, wearing a helmet, drugs, alcohol). | Finds that (1) driving under the influence of drugs or alcohol, (2) striking the side of the bicycle, and (3) crashes involved with a heavy-duty truck can increase the likelihood of severe injuries from motor vehicle crashes at intersections and non-intersection. | [27] |
Multinomial logit model and latent class logit model | Single-vehicle crashes on rural roads. | Uses multinomial logit model and latent class model at the same time, finding that vehicle age and surface condition (such as dry, wet, or icy) do not significantly impact driver injury severity. | [28] |
Ordered probit model | Road characteristics, vehicle attributes, driver behavior, and driver characteristics. | Uses ordered probit models for crash injury severity analysis to examine the factors that affect the risk of different injury levels sustained under various types of crashes including two-vehicle crashes, single-vehicle crashes, motorcycle, pedestrian, etc. | [29,30,31,32,33,34] |
Bayesian ordered probit models | Driver’s characteristics, vehicle type, and roadway conditions, etc. | Introduces Bayesian ordered probit models and compares the results with those of ordered probit models. When the sample data size is small, the Bayesian ordered probit model can produce better prediction performance than the ordered probit model. | [35] |
Multinomial probit model | Gender of the motorcyclist, speeding, use of alcohol and/or drugs, helmet use, being involved in a single-vehicle crash or at a non-intersection location, horizontal curves, graded segments, major roadway. | Finds that being a female motorcyclist, excessive speeding, use of alcohol and/or drugs, and riding without a helmet significantly increases fatality and severe injury in terms of in a single-vehicle crash, at a non-intersection location, on horizontal curves or graded segments, and major roadways. | [36] |
Multinomial logit, ordered probit, and mixed logit | Environmental factors, vehicle attributes, and driver characteristics. | Uses three commonly used methods, multinomial logit, ordered probit, and mixed logit, to investigate the effects of under-reported crash data. Fatal crashes must be the baseline severity for the MNL and ML models to minimize the bias and the variability of a model. The rank for the crash severity must be from fatal to property damage only in a descending order for the ordered probit models. | [37] |
Ordered probit model | Impact of vehicle, occupant, driver, and environmental characteristics. | Investigates the influential factors of large truck crash severity using ordered probit models. Finds that the likelihood of fatalities and severe injury rises with the number of trailers, while it falls with the truck length and gross vehicle weight rating. | [38] |
Ordered probit, ordered logit, and multinomial logit model | Time, emergency service arrival time, crash location, primary crash factors, weather, radius of curvature, vertical grade, type of vehicle at fault, driver age. | The time between midnight and 6:00 a.m.; driving while drowsy; median violation; car versus car collision; car versus people collision; car only collision; two or more related vehicles involved; van are the factors increasing risk of severe accident. Time between 6:00 A.M. and noon; ramp; toll gate; vehicle defects; obstacles and poor road conditions; rainy or snowy weather are the factors decreasing risk. | [39] |
Spatial analysis and negative binomial regression | Roadway characteristics and spatial/land use, vehicle attributes, and driver characteristics. | Incomplete sidewalks and high crosswalk density are associated with pedestrian crash risk. People perceive a lower risk near university libraries, stadiums, and academic buildings. | [40] |
Multivariate models | Spatial/land use, transit access, commercial access, and population density, built environment and design characteristics. | Examines both risk exposure and injuries sustained in child pedestrian-vehicular crashes in the vicinity of public schools. There is a significant association with several built-environment and design characteristics. | [41] |
Generalized ordered probit model | Environmental characteristics on the severity of injuries sustained in pedestrian–vehicle crashes | It is found that women pedestrians tend to be injured less frequently than male pedestrians; children have an increased likelihood of injuries; older persons are more likely to be fatally injured in pedestrian–vehicle crashes. | [42] |
Generalized linear model and negative binomial model | Pedestrian crashes and demographic (population and household units) and socio-economic characteristics (mean income and total employment), land use), and accessibility to public transit systems, road network characteristics (the number of lanes, speed limit, presence of median, and pedestrian and vehicular volume). | Population, transit stops, and pedestrians can increase pedestrian crashes. On the other hand, single family, urban residential commercial and neighborhood service can lower pedestrian crashes. Demographic, socio-economic, land use, and road data are better predictors than traffic data. Buffers of 0.5 mile yield better estimates for all and low activity intersections, while 1 mile buffers yield better estimates for high activity intersections. | [43] |
Visual inspection of Google Street View and logit model | Presence of sidewalks, buffers between the road and the sidewalk, street lighting, number of travel lanes and the presence of medians, traffic controls at intersections, and posted speed limits. | Lack of sidewalks, buffers, high-speed roads, roads with six or more lanes, lack of traffic lighting, speed are associated with severity of pedestrian casualties. Age of pedestrians can cause more severe casualties. | [44] |
Machine learning techniques | Survey data | In terms of Work-related Musculoskeletal Disorders (WMSDs), it is found that several risk factors (involvement in physical activities, frequent posture change, exposure to vibration, egress/ingress, duty breaks, and seat adaptability issues) influence the frequency of pain of drivers. | [45] |
Multinomial logit model and discrete choice model | Mode choice preference data collected from airport passengers (540 observations). | Discrete choice model optimization algorithms using Excel is proved to be efficient in managing model tasks. Maximum likelihood method is an optimal method for estimating the coefficients of the variables. Newton Raphson is one of the best algorithms, while the worst performed algorithm is the Steepest Ascent (SA) method. | [46] |
Review | Presents a complete review regarding analytical methodologies of crash injury severity models and approaches in highway accident research. | [47] |
Category | Variable | Description | Mean | Std Dev | Minimum | Maximum |
---|---|---|---|---|---|---|
Crash Severity | Bodily Damage | If the crash resulted in injury damage or fatality then = 1, else = 0 | 0.296 | 0.457 | 0 | 1 |
Sex | MD | If driver is male = 1, if not = 0 | 0.574 | 0.494 | 0 | 1 |
Age | Age | Driver’s age | 36 | 16 | 16 | 105 |
Age2 | Driver’s age2 | 1575 | 1460 | 256 | 11,025 | |
Age3 | Driver’s age3 | 80,391 | 112,918 | 4096 | 115,7625 | |
Type of Vehicle | Compact | If type of unit is sub-compact, compact then = 1, else = 0 | 0.172 | 0.377 | 0 | 1 |
MidSize | If type of unit is midsize then = 1, else = 0 | 0.329 | 0.47 | 0 | 1 | |
FullSize | If type of unit is full size then = 1, else = 0 | 0.101 | 0.301 | 0 | 1 | |
Van | If type of unit is mini-van or van then = 1, else = 0 | 0.081 | 0.273 | 0 | 1 | |
SUV | If type of unit is SUV then = 1, else = 0 | 0.156 | 0.363 | 0 | 1 | |
Pickup | If type of unit is pickup then = 1, else = 0 | 0.1 | 0.3 | 0 | 1 | |
Truck | If type of unit is single unit truck, trailer, truck tractor, truck trailer with double short or long, converter dolly, semi-trailer, fifth wheel, tractor with triples then = 1, else = 0 | 0.042 | 0.201 | 0 | 1 | |
Motorcycle | If type of unit is motorcycle then = 1, else = 0 | 0.007 | 0.083 | 0 | 1 | |
Bus | If type of unit is school bus, church bus, public bus, or other bus then = 1, else = 0 | 0.006 | 0.079 | 0 | 1 | |
Time | Afternoon | If time is between 12 pm and 5 pm then = 1, else = 0 | 0.362 | 0.481 | 0 | 1 |
Peak Time1 | If time is between 7 am and 9 am then = 1, else = 0 | 0.111 | 0.315 | 0 | 1 | |
Peak Time2 | If time is between 5 pm and 7 pm then = 1, else = 0 | 0.164 | 0.37 | 0 | 1 | |
Season | Summer | If date is June or July or August then = 1, else = 0 | 0.245 | 0.43 | 0 | 1 |
Winter | If date is December or January or February then = 1, else = 0 | 0.261 | 0.439 | 0 | 1 | |
Day of Week | Friday | If day of week is Friday then = 1, else = 0 | 0.18 | 0.384 | 0 | 1 |
Crash Location | Intersection_ 4ty | If crash occurred on four-way intersection, T-intersection and Y-intersection then = 1, else = 0. | 0.422 | 0.494 | 0 | 1 |
Intersection_ roundabout | If crash occurred on roundabout then = 1, else =0. | 0.003 | 0.052 | 0 | 1 | |
Ramp | If the crash occurred on the ramp then = 1, else = 0 | 0.043 | 0.203 | 0 | 1 | |
Driveway | If the crash occurred on the driveway then = 1, else =0 | 0.037 | 0.189 | 0 | 1 | |
Curve | If road contour is curved then = 1, else = 0 | 0.084 | 0.277 | 0 | 1 | |
Light Condition | Ice | If road condition is ice or snow then = 1, else = 0 | 0.06 | 0.238 | 0 | 1 |
Dark | If light condition is lighted roadway, roadway not lighted and unknown roadway lighting then = 1, else = 0 | 0.229 | 0.42 | 0 | 1 | |
Speed | Speed 45 | If the crash occurred on the road under posted speed 45 then = 1, else = 0 | 0.527 | 0.499 | 0 | 1 |
Other Crash Factors | Work zone | If the crash occurred within a work zone then = 1, else = 0 | 0.013 | 0.114 | 0 | 1 |
Alcohol | If the driver was influenced by alcohol then = 1, else = 0 | 0.049 | 0.216 | 0 | 1 | |
Drug | If the driver was influenced by drugs then = 1, else = 0 | 0.011 | 0.102 | 0 | 1 | |
Pedestrian | If pedestrian was involved in the crash then = 1, else = 0 | 0.004 | 0.064 | 0 | 1 |
Variable | Description | Mean | Std Dev | Minimum | Maximum |
---|---|---|---|---|---|
Urban | If the TAZ is urban = 1, if not = 0 | 0.85 | 0.357 | 0 | 1 |
Mile_ | Distance to center of Columbus (mile) | 9.978 | 8.25 | 0.073 | 44.466 |
Columbus | |||||
Popdensity | Population/TAZ area (acre) | 5.181 | 5.788 | 0 | 47.043 |
Hhinc10 | Household income 2010 | 52,079.21 | 25,292.19 | 8785 | 161,377 |
Empretsrv1 | Retail goods employment 2010 | 113.915 | 196.609 | 0 | 2124 |
WHITE_P | % of Whites in the TAZ | 0.717 | 0.269 | 0 | 1 |
Punder14 | % of population under 14 | 0.184 | 0.079 | 0 | 0.442 |
P5064 | % of population between 50 and 64 | 0.177 | 0.07 | 0 | 1 |
POver65 | % of population over 65 | 0.116 | 0.1 | 0 | 1 |
Agriculture | % of agricultural land use | 0.137 | 0.275 | 0 | 1 |
Residential | % of residential land use | 0.346 | 0.271 | 0 | 0.962 |
Built_age | Built age of construction in the TAZ where the crash occurred | 44.307 | 23.371 | 5 | 509.25 |
HistoricD | If the crash occurred in historic districts = 1, if not = 0 | 0.021 | 0.143 | 0 | 1 |
Shop_acre | Area of Shopping Centers | 3.432 | 9.085 | 0 | 76.859 |
Variable | DF | Estimate | Standard Error | Wald Chi-Square | Pr > ChiSq |
---|---|---|---|---|---|
Intercept | 1 | 1.0766 | 0.094 | 130.3 | <0.0001 |
Crash-Related Factors | |||||
AG | 1 | −0.0134 | 0.006 | 5.26 | 0.0219 |
AG2 | 1 | 0.000344 | 0.0001 | 6.64 | 0.01 |
AG3 | 1 | −0.00000285 | 0.00000092 | 9.46 | 0.0021 |
MD | 1 | 0.1269 | 0.012 | 116.73 | <0.0001 |
Compact | 1 | −0.3152 | 0.031 | 102.1 | <0.0001 |
MidSize | 1 | −0.331 | 0.03 | 124.17 | <0.0001 |
FullSize | 1 | −0.3033 | 0.033 | 84.89 | <0.0001 |
Van | 1 | −0.3295 | 0.034 | 95.49 | <0.0001 |
SUV | 1 | −0.39 | 0.031 | 157.35 | <0.0001 |
Pickup | 1 | −0.2699 | 0.033 | 68.94 | <0.0001 |
Motorcycle | 1 | −2.9743 | 0.093 | 1032.12 | <0.0001 |
Bus | 1 | 0.3928 | 0.092 | 18.15 | <0.0001 |
PeakTime1 | 1 | 0.0619 | 0.021 | 8.91 | 0.0028 |
PeakTime2 | 1 | 0.0821 | 0.017 | 22.38 | <0.0001 |
Afternoon | 1 | 0.033 | 0.015 | 4.6 | 0.032 |
Summer | 1 | −0.0612 | 0.014 | 20.06 | <0.0001 |
Winter | 1 | 0.153 | 0.014 | 118.38 | <0.0001 |
Friday | 1 | 0.0422 | 0.015 | 8.37 | 0.0038 |
Intersection_4ty | 1 | −0.2163 | 0.012 | 306.18 | <0.0001 |
Intersection_roundabout | 1 | 1.1475 | 0.152 | 56.7 | <0.0001 |
Ramp | 1 | 0.1664 | 0.029 | 31.92 | <0.0001 |
Driveway | 1 | −0.0542 | 0.031 | 3.07 | 0.0799 |
Curve | 1 | −0.1925 | 0.021 | 85.77 | <0.0001 |
Ice | 1 | 0.2259 | 0.04 | 32.04 | <0.0001 |
Dark | 1 | −0.0551 | 0.017 | 11.18 | 0.0008 |
Speed45 | 1 | 0.4173 | 0.013 | 1040.73 | <0.0001 |
Work zone | 1 | 0.1944 | 0.05 | 14.92 | 0.0001 |
Alcohol | 1 | −0.4581 | 0.027 | 288.72 | <0.0001 |
Drug | 1 | −0.4678 | 0.053 | 77.93 | <0.0001 |
Pedestrian | 1 | −3.3078 | 0.132 | 630.66 | <0.0001 |
TAZ- Related Factors | |||||
Urban | 1 | 0.0936 | 0.022 | 18.55 | <0.0001 |
Mile_Columbus | 1 | 0.00788 | 0.001 | 51.49 | <0.0001 |
Popdensity | 1 | 0.0133 | 0.001 | 79.92 | <0.0001 |
Hhinc10 | 1 | 0.00000086 | 0.0000003 | 8.38 | 0.0038 |
Empretsrv1 | 1 | 0.000258 | 0.00004 | 47.4 | <0.0001 |
WHITE_P | 1 | 0.233 | 0.028 | 71.55 | <0.0001 |
Punder14 | 1 | −0.6121 | 0.086 | 50.64 | <0.0001 |
P5064 | 1 | −0.5742 | 0.092 | 38.95 | <0.0001 |
POver65 | 1 | −0.3184 | 0.06 | 28.07 | <0.0001 |
Agriculture | 1 | −0.2358 | 0.036 | 42.54 | <0.0001 |
Residential | 1 | −0.1655 | 0.03 | 30.7 | <0.0001 |
Built_age | 1 | −0.00087 | 0.0003 | 9.29 | 0.0023 |
HistoricD | 1 | 0.2065 | 0.045 | 20.93 | <0.0001 |
Shop_acre | 1 | 0.00145 | 0.001 | 3.71 | 0.0539 |
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Lee, D.; Guldmann, J.-M.; von Rabenau, B. Impact of Driver’s Age and Gender, Built Environment, and Road Conditions on Crash Severity: A Logit Modeling Approach. Int. J. Environ. Res. Public Health 2023, 20, 2338. https://doi.org/10.3390/ijerph20032338
Lee D, Guldmann J-M, von Rabenau B. Impact of Driver’s Age and Gender, Built Environment, and Road Conditions on Crash Severity: A Logit Modeling Approach. International Journal of Environmental Research and Public Health. 2023; 20(3):2338. https://doi.org/10.3390/ijerph20032338
Chicago/Turabian StyleLee, Dongkwan, Jean-Michel Guldmann, and Burkhard von Rabenau. 2023. "Impact of Driver’s Age and Gender, Built Environment, and Road Conditions on Crash Severity: A Logit Modeling Approach" International Journal of Environmental Research and Public Health 20, no. 3: 2338. https://doi.org/10.3390/ijerph20032338