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A multinomial logit model of pedestrian-vehicle crash severity in North Carolina
Accepted Manuscript A multinomial logit model of pedestrian-vehicle crash severity in North Carolina Zhen Chen, Wei (David) Fan PII: DOI: Reference:
S2046-0430(18)30039-X https://doi.org/10.1016/j.ijtst.2018.10.001 IJTST 82
To appear in:
International Journal of Transportation Science and Technology
Received Date: Revised Date: Accepted Date:
12 March 2018 29 August 2018 5 October 2018
Please cite this article as: Z. Chen, W. Fan, A multinomial logit model of pedestrian-vehicle crash severity in North Carolina, International Journal of Transportation Science and Technology (2018), doi: https://doi.org/10.1016/ j.ijtst.2018.10.001
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A multinomial logit model of pedestrian-vehicle crash severity in North Carolina Zhen Chen a, Wei (David) Fan b a
INES Ph.D. Research Assistant, USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, Room 3366, 9201 University City Boulevard, Charlotte, NC 28223-0001. Tel: 1-412-721-6557; Fax: 1-704-687-0957. E-mail: [email protected] b
(Corresponding Author) Director, USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE) and Professor, Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, Room 3261, 9201 University City Boulevard, Charlotte, NC 28223-0001. Tel: 1-704-687-1222; Fax: 1-704-687-0957. E-mail: [email protected]
Abstract: This article develops a multinomial logit (MNL) model to investigate and identify significant contributing factors that determine the pedestrian-vehicle crash severity in North Carolina, United States. Pedestrian-vehicle crash data from Highway Safety Information System (HSIS) database from 2005 to 2012 are collected and used in this study. Crash injury severities are classified into five categories: no injury (property damage only), injury class 3 (possible injury), injury class 2 (evident injury), injury class 1 (disabling injury), and fatality. A preferred multinomial logit model is developed using SAS PROC MDC procedure and marginal effects are also calculated. The results show that the factors that significantly increase the probability of fatalities and disabling injuries include: driver’s physical condition (bad condition), vehicle type (motorcycle and heavy truck), pedestrian age (26-65 and over 65), weekend, light condition (dawn, dusk and dark), roadway characteristics (curve), roadway surface (water), roadway class (NC route) and speed limit (35-50 mph and above 50 mph). The developed model and analysis results provide insights on developing effective countermeasures to reduce vehicle-pedestrian crash severities and improve traffic system safety performance. 1. Introduction As the number of traffic accidents increases globally, traffic safety has become a major public issue. Motor vehicle crashes are one of the leading causes of deaths and serious injuries, and the worldwide number of deaths due to traffic accidents reached 1.25 million based on the data provided by World Health Organization (WHO). Records also indicated that a total of 37,461 road traffic deaths occurred in 2016 in the United States (National Highway Traffic Safety Administration, 2017). Among these crashes, vehicle-pedestrian crashes are a major concern as such crashes have a unique inevitability and high severity level. Walking as a pedestrian has become increasingly dangerous since there have been a large number of vehicle-pedestrian crashes that cause pedestrian fatalities and serious injuries (Zhang et al., 2014). The historical data showed that about 80,000 to 120,000 pedestrians are injured and about 4,600 to 4,900 of them died in vehicle-pedestrian crashes each year in the United States (Retting et al., 2003). Moudon et al. (2011) presented that the pedestrians’ fragility, slow movement and lack of lighting equipment contributed to their higher fatality risk than the driver(s) and passenger(s) in a crash. Therefore, the high fatality rate of pedestrians in traffic crashes is not a coincidence. 1
An understanding of the influencing factors that contribute to the pedestrian-vehicle crash injury severity is important to make the transportation system safer. Transportation planners and decision makers can also utilize the results of this study to plan, design, operate, and manage a safer transportation system and reduce vehicle-pedestrian collisions. The purpose of this research is to develop a MNL model to illustrate how the severity level of vehicle-pedestrian crashes can be influenced by the driver characteristics, pedestrian characteristics, roadway characteristics and environmental characteristics. The MNL model is developed by using the Highway Safety Information System (HSIS) database from 2005 to 2012 in North Carolina in this study. The remainder of this paper is organized as follows: The second section provides a comprehensive literature review on existing studies regarding crash severity. The third section presents the detailed MNL modeling methodology. The fourth section describes the data assembling and analysis work in detail. The fifth section discusses the numerical results of this study. The sixth section provides conclusions of this study and future research directions. 2. Literature review There are many factors contributing to the severity of vehicle-pedestrian crashes. Zajac and Ivan (2003) pointed out that variables significantly influencing vehicle-pedestrian crash severity were roadway width, vehicle type, driver alcohol status, pedestrian age 65 years or older, and pedestrian alcohol involvement. Lee and Abdel-Aty (2005) found that pedestrian and driver demographic factors, road geometry, traffic and environment conditions were closely related to the frequency and severity level of vehicle-pedestrian crashes. They also presented that higher average traffic volume at intersections increases the number of pedestrian crashes. Sze and Wong (2007) developed a predictive model for pedestrian crash risk analysis. Their study indicated that factors such as male, aged below 15, being on an overcrowded or obstructed footpath, being on a road section with severe congestion and daytime lead to a lower pedestrian injury severity. Factors (such as age above 65 years, head injury, a crash at a crossing or within 15 meters of a crosswalk, speed limit above 50 km/h, a signalized intersection, and number of lanes being two or more lanes) were found to lead to a higher probability of severe injury. MNL model is one of the most commonly used models to estimate the impact of each variable on traffic accident severities. Shankar and Mannering (1996) conducted a study to address the need for multivariate analysis of all factors that influence the crash severity. The MNL model was developed in their study using single-vehicle motorcycle crash data collected in five years (1989-1994) from the state of Washington. The results of this study uncovered many important relationships between accident severity and several influencing factors (such as rider age, alcoholic status, rider ejection, driving speed, motorcycle displacement, and rider attention). Tay et al. (2011) conducted a study to determine factors that contribute to the severity of pedestrian-vehicle crashes in South Korea. A MNL model was developed in the study to relate crash severity to several factors, such as roadway environment, traffic control devices, weather conditions, pedestrian characteristics, driver characteristics, and vehicle characteristics. The results of this study showed that key factors (heavy vehicles, drunk drivers, male drivers, drivers under the age of 65, pedestrians who are over the age of 65 or female, pedestrians who are hit in the middle of the road, on high speed roads, under inclement weather conditions, at night, on road links, in tunnels, on bridges, on wider roads) significantly increase the probability of each severity level of injury to pedestrians. The author also suggested that campaigns should be targeted at male drivers, drivers under the age of 65, female pedestrians and older pedestrians. 2
Kim et al. (2008) investigated the pedestrian injury severity in motor-vehicle crashes using police-reported crash data between 1997 and 2000 in North Carolina. A heteroskedastic model was used and compared with the MNL model. The results showed that the probability of a fatal crash decreases: during the PM traffic peak, with traffic signal control, with increasing driver age, on a curved roadway, in inclement weather, at a crosswalk, and when walking along roadway. The author also mentioned that the heteroskedastic model could provide a better fit than the MNL model. Khorashadi et al. (2005) evaluated the differences in driver injury severity in urban and rural truck-involved crashes. The accident data during a four-year period (i.e., 1997–2000) in California were used in the study. A MNL model was developed. Results showed that there were 13 variables that significantly influence severity level in rural but not urban areas, and 17 variables significantly influencing severity level in urban but not rural areas. Gkritza et al. (2010) investigated severity levels of farm vehicle crashes based on a MNL model. The farm vehicle crashes data in three years (2004-2006) that occurred on Iowa’s public roads were used in this study. Factors that were found to be more likely to contribute to a farm vehicle crash injury were: rear-end manner of collision; single-vehicle crash; farm vehicle crossed the centerline or median; dry road conditions; and farm vehicle was older than 29 years, etc. The authors also emphasized the needs for stricter licensing requirements and better training of young farm vehicle operators in their study. Celik and Oktay (2014) analyzed 11,771 traffic accidents happened between 2008 and 2013 in Turkey. A MNL model was developed to determine the risk factors affecting the crash injury severity. The analysis results of this study revealed that several factors (such as primary-educated drivers, drivers over the age of 65, accidents occurring on state routes, highways or provincial roads, single-vehicle accidents as well as the presence of pedestrian crosswalks) would increase the probability of fatal injuries. Chen et al. (2015) performed a study using a MNL model to analyze the driver injury severity level in rear-end crashes based on the state-wide crash data in 2 years (2010-2011) in New Mexico. The results of this study indicated that factors including truck-involvement, inferior lighting conditions, windy weather conditions, the number of vehicles involved, etc. could significantly increase driver injury severity in rear-end crashes. Fan et al. (2015) identified the factors impacting the injury severity levels of vehicle related crashes at highway-rail grade crossings (HRGCs). The pedestrian-rail and vehicle-rail crash data from USDOT highway-rail crossing inventory from 2005 to 2012 were used in this study. A MNL model was utilized to identify the statistically significant factors. The results of the study showed the factors that were more likely to result in injury and fatal crashes were: pickup trucks, rail equipment struck vehicle, high temperatures, foggy weather, and open space development areas, etc. The authors pointed educating and equipping drivers with good driving habits can potentially help minimize the chance of resulting in more severe crashes. Wu et al. (2016) presented the statistically significant contributing factors and analyzed their impacts on driver injury severities in their study. Two MNL models for teenage and adult drivers were developed using crash data in New Mexico from 2010 to 2011. The results showed that it is necessary to distinguish certain different attributes to specifically develop effective safety solutions for the two driver groups. Other commonly used discrete choice models (such as ordered probit model, ordered logit model, nested logit model and mixed logit model) were also employed to analyze crash injury severity to address different methodological issues associated with the specific datasets. For 3
example, Moore et al. (2011) modeled the injury severity of bicyclist-vehicle crashes at intersections. Both the MNL model and mixed logit model were developed by the authors to estimate the degree of influence of each variable. The crash data from 2002 to 2008 in Ohio were used. The results of the study revealed that the injury mechanisms were substantially different in crashes occurring at non-intersection locations and intersections. Ye and Lord (2011) examined how three commonly used traffic crash severity models (MNL model, ordered probit model, and mixed logit model) perform under different unreported crash rates. Two simulation scenarios were built, and a Monte Carlo approach was used to evaluate those three models. The results of the study showed that the analysis using simulated and observed crash data achieved consistent results with the consideration of underreporting crash rate for each crash severity level. 3. Methodology Although each model used in the crash severity related literature has its advantages, it appears that the MNL model is the most common technique used to identify the relationship between the dependent and independent variables. The MNL Model formulation was well discussed by Shankar and Mannering (1996). As discussed before, the severity of a crash is specified to be one of five discrete categories: No injury (property damage only), injury class 3 (possible injury), injury class 2 (evident injury), injury class 1 (disabling injury), and fatality. With these five discrete crash severity levels, a statistical model that can be used to determine the probability of a crash having a specific severity level can be derived. The probability of crash n with severity outcome i is written as: (1)
where is the probability of crash n having severity level i, P denotes the probability, and is a function that determines the utility of crash n resulting in severity i. To estimate this probability, the linear function of can be expressed as: (2)
where is a vector of explanatory variables that determine the severity, represents a vector of estimable coefficients for injury outcome i, and is an error term that accounts for unobserved factors influencing crash severity level and is assumed to be identically and independently distributed. The term in this equation is the observable component and is the unobserved portion. Based on equations 1 and 2, the following equation can be written: (3)
With equation 3, an estimable severity model can be derived by assuming a generalized extreme value (GEV) distributional form for the error term. Finally, the GEV assumption produces a MNL model by using the following equation: (4)
4
where all variables are same as previously defined and the vector can be estimated by using the standard maximum likelihood methods (Shankar and Mannering, 1996; Tay et al., 2011). The marginal effect analysis could help evaluate how the significant variables estimated in the MNL model impact the pedestrian injury outcome probabilities (Scott-Long, 1997). Since binary indicator variables (with the value of 0 or 1) are used in this study, the marginal effect can be computed as: (5)
4. Data assembling and analysis Pedestrian-vehicle crash data in the Highway Safety Information System (HSIS) database from 2005 to 2012 in North Carolina are used in this study. All these crash data were collected by all police departments in North Carolina on a standard form as prescribed by state law. The variables are selected based on the empirical results from existing literatures and records with missing information are dropped. After the data merging and cleaning process with the help of SAS PROC SQL function, 3553 cases are selected and used in the MNL modeling. Table 1 presents the count and ratio information about each contributing factor in which all continuous variables are converted into categorical variables. As shown, the distribution of pedestrian-vehicle crash severity is 15.96%, 12.16%, 36.7%, 28.76% and 6.42% for fatal, injury class 1, injury class 2, injury class 3 and no injury, respectively. For the drivers involved in the pedestrian-vehicle crashes, this table indicates that the majority (96.09%) of them are North Carolina drivers, the majority (66.51%) of drivers are between 26-65 years old, the majority (59.61%) of them are males, 76.78% of them are under sober status, 23.22% of them are drunk drivers, and 94.65% of them are under good physical condition. For the vehicles involved in the pedestrian-vehicle crashes, the table indicates the majority (71.57%) of them are passenger vehicles. For the pedestrians involved in the pedestrianvehicle crashes, the majority (57.84%) of them are between 26-65 years old. For the roadways involved in the pedestrian-vehicle crashes, the surface of the majority of them (86.72%) are dry, the pavement of the majority of them (97.3%) are asphalt and 93.75% of them are straight road. The majority of road classes are NC route (38.25%) and Local Street (40.59%), and the majority (67.94%) of terrains are rolling. For the environmental condition of the pedestrian-vehicle crashes, 91.64% of crashes occurred under good weather condition. The majority (97.86%) of crashes did not happened in work zones. Detailed descriptive statistics of other variables are also provided in the table. All the variables are considered in the study and used in the MNL model development. Table 1 Distribution (%) of Potential Contributing Factors at Different Severity Levels
Variable Driver State NC driver Out of state
Fatality count ratio 532 35
14.97 0.99
Injury class 1 count ratio 418 14
11.76 0.39
Severity level Injury class 2 Injury class 3 count ratio count ratio 1251 53
35.21 1.49
995 27
28 0.76
No Injury count ratio 218 10
6.14 0.28
Total count
ratio
3414 139
96.09 3.91
5
Variable driver Total Driver Age Under 26 26-65 Over 65 Total Driver Gender Male Female Total Alcoholic Status Sober Drunk Total Physical Condition Good condition Bad condition Total Vehicle Type Passenger veh Motorcycle Light truck Van Bus Heavy truck Total Pedestrian Age Under 26 26-65 Over 65 Total Road Surface Dry Wet Water Ice Snow Total Crash Location On roadway Outside roadway Total Road Characteristics Straight Curve Total Roadway Class Interstate US route NC route
Fatality count ratio
Injury class 1 count ratio
Severity level Injury class 2 Injury class 3 count ratio count ratio
No Injury count ratio
Total count
ratio
567
15.96
432
12.16
1304
36.7
1022
28.76
228
6.42
3553
100
141 377 49 567
3.97 10.61 1.38 15.96
108 288 36 432
3.04 8.11 1.01 12.16
302 859 143 1304
8.5 24.18 4.02 36.7
209 678 135 1022
5.88 19.08 3.8 28.76
41 161 26 228
1.15 4.53 0.73 6.42
801 2363 389 3553
22.54 66.51 10.95 100
389 178 567
10.95 5.01 15.96
252 180 432
7.09 5.07 12.16
763 541 1304
21.47 15.23 36.7
588 434 1022
16.55 12.22 28.76
126 102 228
3.55 2.87 6.42
2118 1435 3553
59.61 40.39 100
358 209 567
10.08 5.88 15.96
308 124 432
8.67 3.49 12.16
1012 292 1304
28.48 8.22 36.7
865 157 1022
24.35 4.42 28.76
185 43 228
5.21 1.21 6.42
2728 825 3553
76.78 23.22 100
514 53 567
14.47 1.49 15.96
406 26 432
11.43 0.73 12.16
1249 55 1304
35.15 1.55 36.7
979 43 1022
27.55 1.21 28.76
215 13 228
6.05 0.37 6.42
3363 190 3553
94.65 5.35 100
364 5 107 44 2 45 567
10.24 0.14 3.01 1.24 0.06 1.27 15.96
321 5 69 23 2 12 432
9.03 0.14 1.94 0.65 0.06 0.34 12.16
944 6 240 89 3 22 1304
26.57 0.17 6.75 2.5 0.08 0.62 36.7
744 4 186 72 8 8 1022
20.94 0.11 5.24 2.03 0.23 0.23 28.76
170 1 29 18 2 8 228
4.78 0.03 0.82 0.51 0.06 0.23 6.42
2543 21 631 246 17 95 3553
71.57 0.59 17.76 6.92 0.48 2.67 100
128 380 59 567
3.6 10.7 1.66 15.96
149 254 29 432
4.19 7.15 0.82 12.16
539 694 71 1304
15.17 19.53 2 36.7
377 592 53 1022
10.61 16.66 1.49 28.76
71 135 22 228
2 3.8 0.62 6.42
1264 2055 234 3553
35.58 57.84 6.59 100
496 65 4 0 2 567
13.96 1.83 0.11 0 0.06 15.96
366 64 1 0 1 432
10.3 1.8 0.03 0 0.03 12.16
1123 173 1 5 2 1304
31.61 4.87 0.03 0.14 0.06 36.7
890 126 4 1 1 1022
25.05 3.55 0.11 0.03 0.03 28.76
206 22 0 0 0 228
5.8 0.62 0 0 0 6.42
3081 450 10 6 6 3553
86.72 12.67 0.28 0.17 0.17 100
544 23 567
15.31 0.65 15.96
410 22 432
11.54 0.62 12.16
1232 72 1304
34.67 2.03 36.7
939 83 1022
26.43 2.34 28.76
213 15 228
5.99 0.42 6.42
3338 215 3553
93.95 6.05 100
515 52 567
14.49 1.46 15.96
390 42 432
10.98 1.18 12.16
1230 74 1304
34.62 2.08 36.7
979 43 1022
27.55 1.21 28.76
217 11 228
6.11 0.31 6.42
3331 222 3553
93.75 6.25 100
51 160 245
1.44 4.5 6.9
24 87 192
0.68 2.45 5.4
23 210 497
0.65 5.91 13.99
10 134 358
0.28 3.77 10.08
10 43 67
0.28 1.21 1.89
118 634 1359
3.32 17.84 38.25
6
Variable Local street Total Pavement Type Concrete Asphalt Others Total Terrain Flat Rolling Mountainous Total Speed Limit Under 35mph 35-50 mph Above 50 mph Total AADT Low Medium High Total Time of Day (TOD) Morning peak Daytime off peak Afternoon peak Night Total Day of Week (DOW) Weekday Weekend Total Rural/Urban Area Rural Urban Total Weather Condition Good condition Bad condition Total Light Condition Daylight Dusk/Dawn Dark Total Work Zone Not Work zone Work zone Total
Fatality count ratio 111 3.12 567 15.96
Injury class 1 count ratio 129 3.63 432 12.16
Severity level Injury class 2 Injury class 3 count ratio count ratio 574 16.16 520 14.64 1304 36.7 1022 28.76
No Injury count ratio 108 3.04 228 6.42
Total count 1442 3553
ratio 40.59 100
20 546 1 567
0.56 15.37 0.03 15.96
13 419 0 432
0.37 11.79 0 12.16
20 1278 6 1304
0.56 35.97 0.17 36.7
26 993 3 1022
0.73 27.95 0.08 28.76
6 221 1 228
0.17 6.22 0.03 6.42
85 3457 11 3553
2.39 97.3 0.31 100
203 335 29 567
5.71 9.43 0.82 15.96
115 291 26 432
3.24 8.19 0.73 12.16
324 916 64 1304
9.12 25.78 1.8 36.7
268 705 49 1022
7.54 19.84 1.38 28.76
46 167 15 228
1.29 4.7 0.42 6.42
956 2414 183 3553
26.91 67.94 5.15 100
93 140 334 567
2.62 3.94 9.4 15.96
127 134 171 432
3.57 3.77 4.81 12.16
489 374 441 1304
13.76 10.53 12.41 36.7
446 283 293 1022
12.55 7.97 8.25 28.76
111 67 50 228
3.12 1.89 1.41 6.42
1266 998 1289 3553
35.63 28.09 36.28 100
328 198 41 567
9.23 5.57 1.15 15.96
252 152 28 432
7.09 4.28 0.79 12.16
759 499 46 1304
21.36 14.04 1.29 36.7
578 420 24 1022
16.27 11.82 0.68 28.76
122 95 11 228
3.43 2.67 0.31 6.42
2039 1364 150 3553
57.39 38.39 4.22 100
38 45 131 353 567
1.07 1.27 3.69 9.94 15.96
62 70 113 187 432
1.75 1.97 3.18 5.26 12.16
156 287 396 465 1304
4.39 8.08 11.15 13.09 36.7
126 284 318 294 1022
3.55 7.99 8.95 8.27 28.76
17 62 81 68 228
0.48 1.75 2.28 1.91 6.42
399 748 1039 1367 3553
11.23 21.05 29.24 38.47 100
381 186 567
10.72 5.23 15.96
318 114 432
8.94 3.21 12.16
987 317 1304
27.78 8.92 36.7
803 219 1022
22.61 6.16 28.76
175 53 228
4.94 1.49 6.42
2664 889 3553
74.97 25.03 100
352 215 567
9.91 6.05 15.96
213 219 432
5.99 6.16 12.16
548 756 1304
15.42 21.28 36.7
380 642 1022
10.7 18.07 28.76
77 151 228
2.17 4.25 6.42
1570 1983 3553
44.19 55.81 100
519 48 567
14.61 1.35 15.96
394 38 432
11.09 1.07 12.16
1193 111 1304
33.58 3.12 36.7
942 80 1022
26.51 2.25 28.76
208 20 228
5.85 0.56 6.42
3256 297 3553
91.64 8.36 100
100 19 448 567
2.81 0.53 12.61 15.96
148 13 271 432
4.17 0.37 7.63 12.16
589 74 641 1304
16.58 2.08 18.04 36.7
550 34 438 1022
15.48 0.96 12.33 28.76
122 11 95 228
3.43 0.31 2.67 6.42
1509 151 1893 3553
42.47 4.25 53.28 100
555 12 567
15.62 0.34 15.96
426 6 432
11.99 0.17 12.16
1267 37 1304
35.66 1.04 36.7
1006 16 1022
28.31 0.45 28.76
223 5 228
6.28 0.14 6.42
3477 76 3553
97.86 2.14 100
7
5. Numerical results and discussions Many variables from the HSIS database are selected in the MNL model development. However, based on the results obtained by using the SAS PROC LOGISTIC procedure, the pvalues of some of the variables are larger than 0.1, which means that these variables are found to be insignificant and hence are removed from the list of significant variables. Table 2 and 3 show the results obtained from the study. Five vehicle-pedestrian crash severity levels (fatal crashes, injury class 1 crashes, injury class 2 crashes, injury class 3 crashes and no injury crashes) are considered as the dependent variable and no injury crashes are considered as the base case among these five crash severity levels. Therefore, all the estimated coefficients for the selected variables represent the effect of the variables on the specific fatal/injury level compared with no injury level. Table 2 Estimated Coefficients of Each Variable Variables
Fatal P-value 0.32
Injury class 1 Coef. P-value 0.97 0.00
Injury class 2 Coef. P-value 0.95 0.01
Injury class 3 Coef. P-value 0.19 0.65
Intercept
Coef. -0.28
Driver age (26-65)
-0.36
0.01
-0.29
0.03
-0.17
0.08
-
-
Driver age (over 65)
-0.60
0.01
-0.65
0.00
-0.26
0.08
-
-
0.46
0.05
-
-
-0.40
0.07
-0.40
0.09
1.18
0.04
1.03
0.06
-
-
-
-
-
-
-
-
-1.29
0.05
-
-
1.12
0.00
-
-
-
-
-0.97
0.01
0.52
0.00
-
-
-0.23
0.00
-
-
1.12
0.00
-
-
-0.63
0.00
-0.71
0.00
-0.87
0.00
-1.06
0.00
-0.66
0.01
-0.54
0.03
-0.55
0.00
-1.19
0.00
-0.71
0.00
-0.74
0.00
0.19
0.09
-0.90 -
0.00 -
-0.34 -
0.08 -
-0.62 -
0.00 -
0.81
0.01
-
-
0.49
0.01
-
-
0.98
0.00
0.63
0.00
-
-
-
-
-0.22
0.05
-
-
-
-
-0.16
0.07
-0.76
0.00
-
-
-
-
0.52
0.00
0.40
0.04
0.52
0.01
-
-
-
-
-0.23 -0.28
0.09 0.07
-
-
0.63 -
0.01 -
-
-
Physical condition (bad condition) Vehicle type (motorcycle) Vehicle type (bus) Vehicle type (heavy truck) Pedestrian age (2665) Pedestrian age (over 65) TOD (daytime offpeak) TOD (afternoon peak) TOD (night) DOW (weekend) Light condition (dusk and dawn) Light condition (dark) Terrain (rolling) Crash location (outside roadway) Roadway characteristics (curve) Work-zone Area type (urban) Road surface (wet)
8
Variables Road surface (water) Roadway class (US route) Roadway class (NC route) Roadway class (local street) Speed limit (35-50 mph) Speed limit (above 50 mph)
Coef.
Fatal P-value
Injury class 1 Coef. P-value
Injury class 2 Coef. P-value
Injury class 3 Coef. P-value
1.25
0.09
-
-
-
-
-
-
-
-
-
-
1.01
0.00
1.37
0.00
-
-
0.30
0.03
1.32
0.00
1.80
0.00
-
-
-
-
1.54
0.00
2.18
0.00
0.72
0.00
0.40
0.01
0.17
0.08
-
-
1.65
0.00
0.79
0.00
0.86
0.00
0.69
0.00
With the application of the MNL models, 26 variables of 15 categories are found to be significant in predicting the severity level of the crashes. The detailed MNL model can be written as follows (eqs. 6-9). (6)
= -0.28 -0.36 -0.87 +0.40
(7)
+0.52
= 0.95 -0.17 0.71 +0.17
(9)
-0.55 -0.23
-0.65 +0.30 -0.26
-0.34 +0.49 +0.86
= 0.19 -0.40 0.16
+0.46
+0.19 -0.28
= 0.97 -0.29 +0.63
(8)
-0.60
+0.52
-0.97 +1.37
+1.18
+0.81 +1.25
+1.12
-0.40
-0.22 +1.65
-0.76
-1.06
-1.19
-0.90
-0.71 +1.80
+0.79
-1.29
+0.63
+1.12
+0.98 +0.72
+1.03 +0.40
+0.52
-0.23
-0.63
+1.01
+1.32
-0.54
-0.74
+2.18
+0.69
-0.66
-
+1.54
-0.62
-
Where: X1 = Driver age indicator (1 if driver age is 26-65, 0 otherwise) X2 = Driver age indicator (1 if driver age is >65, 0 otherwise) X3 = Driver physical condition indicator (1 if condition is not normal, 0 otherwise) X4 = Vehicle type indicator (1 if vehicle is motorcycle, 0 otherwise) X5 = Vehicle type indicator (1 if vehicle is bus, 0 otherwise) X6 = Vehicle type indicator (1 if vehicle is heave truck, 0 otherwise) X7 = Pedestrian age indicator (1 if pedestrian age is 26-65, 0 otherwise) X8 = Pedestrian age indicator (1 if pedestrian age is >65, 0 otherwise) X9 = Time of day indicator (1 if time period is daytime off-peak (10AM-15PM), 0 otherwise) X10 = Time of day indicator (1 if time period is afternoon peak (16PM-19PM), 0 otherwise) X11 = Time of day indicator (1 if time period is night (20PM-5AM), 0 otherwise) X12 = Day of week indicator (1 if weekend, 0 otherwise) 9
X13 = Light condition indicator (1 if light condition is dusk and dawn, 0 otherwise) X14 = Light condition indicator (1 if light condition is dark, 0 otherwise) X15 = Terrain indicator (1 if terrain is rolling, 0 otherwise) X16 = Crash location indicator (1 if outside roadway, 0 otherwise) X17 = Roadway characteristic indicator (1 if location is curve, 0 otherwise) X18 = Work zone indicator (1 if location is work-zone, 0 otherwise) X19 = Area type indicator (1 if urban area, 0 otherwise) X20 = Road surface indicator (1 if wet surface, 0 otherwise) X21 = Road surface indicator (1 if water surface, 0 otherwise) X22 = Roadway class indicator (1 if US route, 0 otherwise) X23 = Roadway class indicator (1 if NC route, 0 otherwise) X24 = Roadway class indicator (1 if Local street, 0 otherwise) X25 = Road speed limit indicator (1 if speed limit between 35mph to 50mph, 0 otherwise) X26 = Road speed limit indicator (1 if speed limit above 50mph, 0 otherwise) Based on the developed equations (eqs.6-9) above, the marginal effects of the model are also calculated and presented in Table 3. Using the first variable as an example, if the driver age category changes from under 26 to 26-65, the probability of fatality, injury class 1 (disabling injury) and injury class 2 (evident injury) decreases by 0.025, 0.019 and 0.01, respectively, while the probability of injury class 3 (possible injury) and no injury increases by 0.047 and 0.007, respectively. Note that the sum of marginal effects of each variable is zero, which satisfies the overall requirement that the sum of probability is 1. Table 3 Marginal Effects Results Variable Driver age (26-65) Driver age (over 65) Physical condition (bad condition) Vehicle type (motorcycle) Vehicle type (bus) Vehicle type (heavy truck) Pedestrian age (26-65) Pedestrian age (over 65) TOD (daytime off-peak) TOD (afternoon peak) TOD (night) DOW (Weekend) Light condition (dusk and dawn) Light condition (dark) Terrain (rolling) Area type (urban) Crash location (outside roadway) Roadway characteristics (curve) Work-zone Road surface (wet)
P(Fatality) -0.025 -0.037 0.069 0.097 0.002 0.227 0.06 0.223 -0.009 0.04 0.078 0.022 0.105 0.09 -0.017 -0.024 -0.079 0.03 0.002 -0.03
P (Injury class 1) -0.019 -0.047 0.092 0.152 0.205 -0.001 -0.004 0.009 -0.037 -0.046 -0.045 -0.005 -0.019 0.054 0.016 0.005 -0.016 0.055 0.09 0.007
P (Injury class 2) -0.01 -0.013 -0.069 -0.065 -0.257 0.016 -0.061 -0.089 -0.102 -0.097 -0.071 -0.005 -0.049 -0.039 0.018 0.008 -0.041 -0.032 -0.116 0.007
P (Injury class 3) 0.047 0.084 -0.098 -0.16 0.045 -0.251 0.005 -0.154 0.094 0.045 -0.002 -0.01 -0.031 -0.091 -0.024 0.009 0.145 -0.046 0.021 0.014
P (No Injury) 0.007 0.013 0.005 -0.024 0.005 0.008 0 0.012 0.055 0.058 0.04 -0.002 -0.005 -0.014 0.006 0.002 -0.009 -0.007 0.003 0.002
10
Road surface (water) Roadway class (US route) Roadway class (NC route) Roadway class (local street) Speed limit (35-50 mph) Speed limit (above 50 mph)
0.182 -0.065 -0.1 -0.117 0.084 0.126
-0.042 -0.074 -0.071 -0.137 0.039 0.004
-0.04 -0.077 -0.062 -0.053 -0.101 -0.094
-0.087 0.248 0.281 0.377 -0.018 0.002
-0.013 -0.032 -0.048 -0.07 -0.004 -0.037
5.1.Drivers’ characteristics results With respect to drivers’ ages, previous studies usually divided ages into three groups to represent young people (under 26), mid-age people (26-65) and elderly (over 65), respectively (e.g., Tay et al., 2011). Therefore, three age groups (under 26, 26-65 and over 65) are also considered in this study. The results in Table 2 show that senior drivers (over 65 years old) and middle-aged drivers (26-65 years old) are less likely to be involved in fatality compared to young drivers. In addition, the marginal effect result as shown in Table 3 also indicates that the probability of fatal crash will reduce when the driver group changes from young driver to senior driver. This might because senior drivers tend to drive at lower speeds and therefore, the pedestrian will be less likely to be involved in a fatal crash (Shankar and Mannering, 1996; Fan et al., 2011). The result of physical condition shown in Table 2 indicates that drivers with bad physical condition are more likely to be involved in a fatal crash. The marginal effect result as shown in Table 3 also indicates that the physical condition being not normal increases the likelihood of fatal and injury class 1 crashes (disabling injury). Such results are consistent with previous studies that bad physical condition (such as fatigue) will increase the probability of fatal crashes (e.g., Desmond et al., 1998). Six vehicle categories (ranged from passenger car to heavy truck types) are considered in this study. As shown in table 2, motorcycle and heavy truck are more likely to result in fatal crashes. The marginal effect results in Table 3 also indicate motorcycle, bus and heavy truck would increase the likelihood of fatal crashes. The results are similar to previous studies that focused on the other type of crashes (e.g., Fan et al. 2011) and could be explained by the physical (and thus more dangerous) characteristics of buses and heavy trucks. 5.2.Pedestrian’s characteristics results Similar to driver age groups, three pedestrian age groups (pedestrian age under 26, between 26-65 years old, and age over 65) are also considered in this study. For the pedestrian age between 26-65 and age over 65 groups, the estimation result in Table 2 and marginal effect result in Table 3 both indicate that these two groups increase the likelihood of fatal crashes compared with young pedestrians. This result can be explained by the older pedestrian’s physiological characteristics, such as the need for more reaction times and the generally lower physical fitness level compared with younger pedestrian group. This result is consistent with findings from the previous studies (Kim et al., 2008; Tay et al., 2011). 5.3.Environmental condition characteristics results Four time of day categories (ranging from morning peak to night) are considered in this study. As shown in Table 2, daytime off-peak and afternoon peak are less likely to result in fatal 11
crashes. The marginal effect result shown in Table 3 indicates that night increases the likelihood of fatal crashes. These probably suggest that fatigue and bad visibility during night period could increase the rate of fatal crashes (Sze and Wong, 2007; Tay et al., 2011). With respect to day of week, the result in Table 2 shows that weekend is more likely to increase the crash severity. The marginal effect result as shown in Table 3 also indicates that weekend increases the likelihood of fatal crashes. This could be explained by that during weekends traffic volume is typically lower than that in weekdays, which in turn results in higher travel speeds and thus the likelihood of fatal crashes. Three light condition categories (ranged from daylight to dark) are considered in this study. As shown in Table 2, dusk, dawn and dark are more likely to result in fatal crashes. The marginal effect results shown in Table 3 also indicate these conditions increase the likelihood of fatal crashes. These might suggest that compared with day light, bad visibility of pedestrians is more likely to increase the fatal crash possibility (Kim et al., 2008). Three terrain condition categories (flat, rolling and mountainous) are considered in this study. As shown in Table 2, rolling terrain is less likely to result in fatal crashes. The marginal effect result as shown in Table 3 also indicates that changing from flat terrain category to rolling category decreases the likelihood of fatal crashes. This could be explained by that compared with flat terrain condition, the vehicle speed may become lower than that under rolling condition. With respect to crash area type, the result in Table 2 shows that rural areas are more likely to be involved in serious injury compared to urban areas. The marginal effect result as shown in Table 3 also indicates that urban area decreases the likelihood of fatal crashes. This might suggest that compared with rural areas, urban areas are more likely to have better traffic control (e.g. pedestrian signal phases), traffic infrastructures and medical facilities. This result is consistent with a previous study (Lee and Abdel-Aty, 2005). 5.4.Road condition characteristics results With respect to crash location type, the result in Table 2 shows that outside roadway is more likely to reduce the crash severity. The marginal effect result as shown in Table 3 also indicates that outside roadway decreases the likelihood of fatal crashes. This could be explained by that compared with crashes on the roads, the vehicle speed may become lower when driving outside the roadways and the damage to the pedestrian will be lower as well. The result of roadway characteristics shows that curve roads are more likely to increase the fatal crash possibility than the straight roads. The marginal effect result as shown in Table 3 also indicates that curve locations increase the likelihood of fatal crashes, perhaps due to the fact that the line of sight might become unavailable at curve locations. As shown in Table 3, work-zone areas are more likely to increase the fatal crash possibility than the non-work zone areas. On the other hand, this result indicates that non-work zone areas are less likely to lead severe injury and fatal crashes than work-zone areas. The reason of this outcome is uncertain but it is consistent with the previous study (Khattak et al., 2002). Five road surface categories (ranged from dry to snow) are considered in this study. As shown in table 2, water surface is more likely to result in fatal crashes. The marginal effect results in Table 3 indicate only wet surface decreases the likelihood of fatal crashes. The reasons behind these interesting results might suggest that the vehicles are more likely to be driven with caution under low speed on the wet surface. However, the water surface usually associated with 12
the bad weather condition and the visibility of pedestrian and driver may be influenced under that situation. Four roadway class categories (ranged from Interstate to Local Street) are accounted for in this study. The marginal effect results shown in Table 3 indicate Interstate may increase the likelihood of fatal crashes. These probably suggest that Interstates are associated with higher vehicle speed which could increase the injury severity and the result is consistent with the previous studies (e.g., Tay et al. 2011). Three speed limit categories (less than 35 mph, 35-50 mph and above 50 mph) are considered in this study. The results of speed limit shown in Table 2 indicate that speed limit between 35-50 mph and speed limit above 50 mph are significant and more likely to increase the fatal crash possibility than the speed limit less than 35 mph. This result is consistent with the previous studies (e.g., Kim et al. 2008). 6. Conclusions and future research directions Pedestrian-vehicle collision is a serious transportation issue today. This study aims to identify the significant influencing factors contributing to the severity level of vehicle-pedestrian crashes to provide policy makers with evidence-based recommendations to address this transportation issue. The results show that the factors that may significantly increase the fatal crash probability include: driver’s physical condition (bad condition); vehicle type (motorcycle and heavy truck); pedestrian age (26-65 and over 65); weekend; light condition (dawn, dusk and dark); roadway characteristic (curve), roadway surface (water), and speed limit (35-50 mph and above 50 mph). The results show that the factors that may significantly increase the injury class 1 crash (disabling injury) probability include: vehicle type (motorcycle); light condition (dark); roadway characteristic (curve); road class (NC route), and speed limit (35-50 mph and above 50 mph). The results show that the factors that may significantly increase the injury class 2 crash (evident injury) probability include: light condition (dusk and dawn); work-zone; roadway class (US route, NC route and local street), and speed limit (35-50 mph and above 50 mph). The results show that the factors that may significantly increase the injury class 3 crash (possible injury) probability include: crash location (outside roadway), roadway class (US route, NC route and local street), and speed limit (above 50 mph). The methodology and results of this study can be helpful for the identification of the contributing crash factors related work in the real world. However, the results of some factors are still uncertain and need to be further investigated. In the future, other types of model could also be developed and compared with the MNL model. Furthermore, elasticity analysis could also be conducted.
Acknowledgments The authors want to express their deepest gratitude to the financial support by the United States Department of Transportation, University Transportation Center through the Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE) at The University of North Carolina at Charlotte (Grant Number: 69A3551747133). 13
References Celik, A. K., Oktay, E., 2014. A multinomial logit analysis of risk factors influencing road traffic injury severities in the Erzurum and Kars provinces of Turkey. Accid. Anal. Prev. 72, 66-77. Chen, C., Zhang, G., Tarefder, R., Ma, J., Wei, H., Guan, H., 2015. A multinomial logit modelbayesian network hybrid approach for driver injury severity analyses in rear-end crashes. Accid. Anal. Prev. 80, 76-88. Desmond, P., Hancock, P., Monette, J., 1998. Fatigue and automation-induced impairments in simulated driving performance. Transp. Res. Rec. J. Transp. Res. Board 1628, 8-14. Fan, W. D., Kane, M. R., Haile, E., 2015. Analyzing severity of vehicle crashes at highway-rail grade crossings: multinomial logit modeling. In: Journal of the Transportation Research Forum (Vol. 54, No. 2). 2015. Gkritza, K., Kinzenbaw, C. R., Hallmark, S., Hawkins, N., 2010. An empirical analysis of farm vehicle crash injury severities on Iowa's public road system. Accid. Anal. Prev. 42(4), 13921397. Khattak, A. J., Khattak, A. J., Council, F. M., 2002. Effects of work zone presence on injury and non-injury crashes. Accid. Anal. Prev. 34(1), 19-29. Khorashadi, A., Niemeier, D., Shankar, V., Mannering, F., 2005. Differences in rural and urban driver-injury severities in accidents involving large-trucks: an exploratory analysis. Accid. Anal. Prev. 37(5), 910-921. Kim, J. K., Ulfarsson, G. F., Shankar, V. N., Kim, S., 2008. Age and pedestrian injury severity in motor-vehicle crashes: a heteroskedastic logit analysis. Accid. Anal. Prev. 40(5), 1695-1702. Lee, C., Abdel-Aty, M., 2005. Comprehensive analysis of vehicle–pedestrian crashes at intersections in Florida. Accid. Anal. Prev. 37(4), 775-786. Li, G., Baker, S.P., Langlois, J.A., Kelen, G.D., 1998. Are female drivers safer? an application of the decomposition method. Epidemiol. 9, 379–384. Moore, D. N., Schneider IV, W. H., Savolainen, P. T., Farzaneh, M., 2011. Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and nonintersection locations. Accid. Anal. Prev. 43(3), 621-630. Moudon, A. V., Lin, L., Jiao, J., Hurvitz, P., Reeves, P., 2011. The risk of pedestrian injury and fatality in collisions with motor vehicles, a social ecological study of state routes and city streets in King County, Washington. Accid. Anal. Prev. 43(1), 11-24. National Highway Traffic Safety Administration. 2017. 2016 motor vehicle crashes: overview. Available from: https://crashstats.nhtsa.dot.gov/Api/Public/Publication/812456 Retting, R. A., Ferguson, S. A., McCartt, A. T., 2003. A review of evidence-based traffic engineering measures designed to reduce pedestrian–motor vehicle crashes. Am. J. Public Health 93(9), 1456-1463. Scott-Long, J.C., Regression Models for Categorical and Limited Dependent Variables, Sage Publications, Thousand Oaks, California, 1997. Shankar, V., Mannering, F., 1996. An exploratory multinomial logit analysis of single-vehicle motorcycle accident severity. J. Safety Res. 27(3), 183-194. Sze, N. N., Wong, S. C., 2007. Diagnostic analysis of the logistic model for pedestrian injury severity in traffic crashes. Accid. Anal. Prev. 39(6), 1267-1278. Tay, R., Choi, J., Kattan, L., Khan, A., 2011. A multinomial logit model of pedestrian–vehicle crash severity. Intl. J. Sustainable Transp. 5(4), 233-249.
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Weiss, H. B., Kaplan, S., Prato, C. G., 2014. Analysis of factors associated with injury severity in crashes involving young New Zealand drivers. Accid. Anal. Prev. 65, 142-155. World Health Organization. 2015. Global status report on road safety 2015. Available from: http://www.who.int/violence_injury_prevention/road_safety_status/2015/en/ Wu, Q., Zhang, G., Ci, Y., Wu, L., Tarefder, R. A., Alcántara, A. D., 2016. Exploratory multinomial logit model–based driver injury severity analyses for teenage and adult drivers in intersection-related crashes. Traffic Inj. Prev. 17(4), 413-422. Ye, F., Lord, D., 2011. Investigation of effects of underreporting crash data on three commonly used traffic crash severity models: multinomial logit, ordered probit, and mixed logit. Transp. Res. Rec. J. Transp. Res. Board 2241, 51-58. Zajac, S. S., Ivan, J. N., 2003. Factors influencing injury severity of motor vehicle–crossing pedestrian crashes in rural Connecticut. Accid. Anal. Prev. 35(3), 369-379. Zhang, G., Yau, K. K., Zhang, X., 2014. Analyzing fault and severity in pedestrian–motor vehicle accidents in China. Accid. Anal. Prev. 73, 141-150.
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S2046-0430(18)30039-X https://doi.org/10.1016/j.ijtst.2018.10.001 IJTST 82
To appear in:
International Journal of Transportation Science and Technology
Received Date: Revised Date: Accepted Date:
12 March 2018 29 August 2018 5 October 2018
Please cite this article as: Z. Chen, W. Fan, A multinomial logit model of pedestrian-vehicle crash severity in North Carolina, International Journal of Transportation Science and Technology (2018), doi: https://doi.org/10.1016/ j.ijtst.2018.10.001
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A multinomial logit model of pedestrian-vehicle crash severity in North Carolina Zhen Chen a, Wei (David) Fan b a
INES Ph.D. Research Assistant, USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, Room 3366, 9201 University City Boulevard, Charlotte, NC 28223-0001. Tel: 1-412-721-6557; Fax: 1-704-687-0957. E-mail: [email protected] b
(Corresponding Author) Director, USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE) and Professor, Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, Room 3261, 9201 University City Boulevard, Charlotte, NC 28223-0001. Tel: 1-704-687-1222; Fax: 1-704-687-0957. E-mail: [email protected]
Abstract: This article develops a multinomial logit (MNL) model to investigate and identify significant contributing factors that determine the pedestrian-vehicle crash severity in North Carolina, United States. Pedestrian-vehicle crash data from Highway Safety Information System (HSIS) database from 2005 to 2012 are collected and used in this study. Crash injury severities are classified into five categories: no injury (property damage only), injury class 3 (possible injury), injury class 2 (evident injury), injury class 1 (disabling injury), and fatality. A preferred multinomial logit model is developed using SAS PROC MDC procedure and marginal effects are also calculated. The results show that the factors that significantly increase the probability of fatalities and disabling injuries include: driver’s physical condition (bad condition), vehicle type (motorcycle and heavy truck), pedestrian age (26-65 and over 65), weekend, light condition (dawn, dusk and dark), roadway characteristics (curve), roadway surface (water), roadway class (NC route) and speed limit (35-50 mph and above 50 mph). The developed model and analysis results provide insights on developing effective countermeasures to reduce vehicle-pedestrian crash severities and improve traffic system safety performance. 1. Introduction As the number of traffic accidents increases globally, traffic safety has become a major public issue. Motor vehicle crashes are one of the leading causes of deaths and serious injuries, and the worldwide number of deaths due to traffic accidents reached 1.25 million based on the data provided by World Health Organization (WHO). Records also indicated that a total of 37,461 road traffic deaths occurred in 2016 in the United States (National Highway Traffic Safety Administration, 2017). Among these crashes, vehicle-pedestrian crashes are a major concern as such crashes have a unique inevitability and high severity level. Walking as a pedestrian has become increasingly dangerous since there have been a large number of vehicle-pedestrian crashes that cause pedestrian fatalities and serious injuries (Zhang et al., 2014). The historical data showed that about 80,000 to 120,000 pedestrians are injured and about 4,600 to 4,900 of them died in vehicle-pedestrian crashes each year in the United States (Retting et al., 2003). Moudon et al. (2011) presented that the pedestrians’ fragility, slow movement and lack of lighting equipment contributed to their higher fatality risk than the driver(s) and passenger(s) in a crash. Therefore, the high fatality rate of pedestrians in traffic crashes is not a coincidence. 1
An understanding of the influencing factors that contribute to the pedestrian-vehicle crash injury severity is important to make the transportation system safer. Transportation planners and decision makers can also utilize the results of this study to plan, design, operate, and manage a safer transportation system and reduce vehicle-pedestrian collisions. The purpose of this research is to develop a MNL model to illustrate how the severity level of vehicle-pedestrian crashes can be influenced by the driver characteristics, pedestrian characteristics, roadway characteristics and environmental characteristics. The MNL model is developed by using the Highway Safety Information System (HSIS) database from 2005 to 2012 in North Carolina in this study. The remainder of this paper is organized as follows: The second section provides a comprehensive literature review on existing studies regarding crash severity. The third section presents the detailed MNL modeling methodology. The fourth section describes the data assembling and analysis work in detail. The fifth section discusses the numerical results of this study. The sixth section provides conclusions of this study and future research directions. 2. Literature review There are many factors contributing to the severity of vehicle-pedestrian crashes. Zajac and Ivan (2003) pointed out that variables significantly influencing vehicle-pedestrian crash severity were roadway width, vehicle type, driver alcohol status, pedestrian age 65 years or older, and pedestrian alcohol involvement. Lee and Abdel-Aty (2005) found that pedestrian and driver demographic factors, road geometry, traffic and environment conditions were closely related to the frequency and severity level of vehicle-pedestrian crashes. They also presented that higher average traffic volume at intersections increases the number of pedestrian crashes. Sze and Wong (2007) developed a predictive model for pedestrian crash risk analysis. Their study indicated that factors such as male, aged below 15, being on an overcrowded or obstructed footpath, being on a road section with severe congestion and daytime lead to a lower pedestrian injury severity. Factors (such as age above 65 years, head injury, a crash at a crossing or within 15 meters of a crosswalk, speed limit above 50 km/h, a signalized intersection, and number of lanes being two or more lanes) were found to lead to a higher probability of severe injury. MNL model is one of the most commonly used models to estimate the impact of each variable on traffic accident severities. Shankar and Mannering (1996) conducted a study to address the need for multivariate analysis of all factors that influence the crash severity. The MNL model was developed in their study using single-vehicle motorcycle crash data collected in five years (1989-1994) from the state of Washington. The results of this study uncovered many important relationships between accident severity and several influencing factors (such as rider age, alcoholic status, rider ejection, driving speed, motorcycle displacement, and rider attention). Tay et al. (2011) conducted a study to determine factors that contribute to the severity of pedestrian-vehicle crashes in South Korea. A MNL model was developed in the study to relate crash severity to several factors, such as roadway environment, traffic control devices, weather conditions, pedestrian characteristics, driver characteristics, and vehicle characteristics. The results of this study showed that key factors (heavy vehicles, drunk drivers, male drivers, drivers under the age of 65, pedestrians who are over the age of 65 or female, pedestrians who are hit in the middle of the road, on high speed roads, under inclement weather conditions, at night, on road links, in tunnels, on bridges, on wider roads) significantly increase the probability of each severity level of injury to pedestrians. The author also suggested that campaigns should be targeted at male drivers, drivers under the age of 65, female pedestrians and older pedestrians. 2
Kim et al. (2008) investigated the pedestrian injury severity in motor-vehicle crashes using police-reported crash data between 1997 and 2000 in North Carolina. A heteroskedastic model was used and compared with the MNL model. The results showed that the probability of a fatal crash decreases: during the PM traffic peak, with traffic signal control, with increasing driver age, on a curved roadway, in inclement weather, at a crosswalk, and when walking along roadway. The author also mentioned that the heteroskedastic model could provide a better fit than the MNL model. Khorashadi et al. (2005) evaluated the differences in driver injury severity in urban and rural truck-involved crashes. The accident data during a four-year period (i.e., 1997–2000) in California were used in the study. A MNL model was developed. Results showed that there were 13 variables that significantly influence severity level in rural but not urban areas, and 17 variables significantly influencing severity level in urban but not rural areas. Gkritza et al. (2010) investigated severity levels of farm vehicle crashes based on a MNL model. The farm vehicle crashes data in three years (2004-2006) that occurred on Iowa’s public roads were used in this study. Factors that were found to be more likely to contribute to a farm vehicle crash injury were: rear-end manner of collision; single-vehicle crash; farm vehicle crossed the centerline or median; dry road conditions; and farm vehicle was older than 29 years, etc. The authors also emphasized the needs for stricter licensing requirements and better training of young farm vehicle operators in their study. Celik and Oktay (2014) analyzed 11,771 traffic accidents happened between 2008 and 2013 in Turkey. A MNL model was developed to determine the risk factors affecting the crash injury severity. The analysis results of this study revealed that several factors (such as primary-educated drivers, drivers over the age of 65, accidents occurring on state routes, highways or provincial roads, single-vehicle accidents as well as the presence of pedestrian crosswalks) would increase the probability of fatal injuries. Chen et al. (2015) performed a study using a MNL model to analyze the driver injury severity level in rear-end crashes based on the state-wide crash data in 2 years (2010-2011) in New Mexico. The results of this study indicated that factors including truck-involvement, inferior lighting conditions, windy weather conditions, the number of vehicles involved, etc. could significantly increase driver injury severity in rear-end crashes. Fan et al. (2015) identified the factors impacting the injury severity levels of vehicle related crashes at highway-rail grade crossings (HRGCs). The pedestrian-rail and vehicle-rail crash data from USDOT highway-rail crossing inventory from 2005 to 2012 were used in this study. A MNL model was utilized to identify the statistically significant factors. The results of the study showed the factors that were more likely to result in injury and fatal crashes were: pickup trucks, rail equipment struck vehicle, high temperatures, foggy weather, and open space development areas, etc. The authors pointed educating and equipping drivers with good driving habits can potentially help minimize the chance of resulting in more severe crashes. Wu et al. (2016) presented the statistically significant contributing factors and analyzed their impacts on driver injury severities in their study. Two MNL models for teenage and adult drivers were developed using crash data in New Mexico from 2010 to 2011. The results showed that it is necessary to distinguish certain different attributes to specifically develop effective safety solutions for the two driver groups. Other commonly used discrete choice models (such as ordered probit model, ordered logit model, nested logit model and mixed logit model) were also employed to analyze crash injury severity to address different methodological issues associated with the specific datasets. For 3
example, Moore et al. (2011) modeled the injury severity of bicyclist-vehicle crashes at intersections. Both the MNL model and mixed logit model were developed by the authors to estimate the degree of influence of each variable. The crash data from 2002 to 2008 in Ohio were used. The results of the study revealed that the injury mechanisms were substantially different in crashes occurring at non-intersection locations and intersections. Ye and Lord (2011) examined how three commonly used traffic crash severity models (MNL model, ordered probit model, and mixed logit model) perform under different unreported crash rates. Two simulation scenarios were built, and a Monte Carlo approach was used to evaluate those three models. The results of the study showed that the analysis using simulated and observed crash data achieved consistent results with the consideration of underreporting crash rate for each crash severity level. 3. Methodology Although each model used in the crash severity related literature has its advantages, it appears that the MNL model is the most common technique used to identify the relationship between the dependent and independent variables. The MNL Model formulation was well discussed by Shankar and Mannering (1996). As discussed before, the severity of a crash is specified to be one of five discrete categories: No injury (property damage only), injury class 3 (possible injury), injury class 2 (evident injury), injury class 1 (disabling injury), and fatality. With these five discrete crash severity levels, a statistical model that can be used to determine the probability of a crash having a specific severity level can be derived. The probability of crash n with severity outcome i is written as: (1)
where is the probability of crash n having severity level i, P denotes the probability, and is a function that determines the utility of crash n resulting in severity i. To estimate this probability, the linear function of can be expressed as: (2)
where is a vector of explanatory variables that determine the severity, represents a vector of estimable coefficients for injury outcome i, and is an error term that accounts for unobserved factors influencing crash severity level and is assumed to be identically and independently distributed. The term in this equation is the observable component and is the unobserved portion. Based on equations 1 and 2, the following equation can be written: (3)
With equation 3, an estimable severity model can be derived by assuming a generalized extreme value (GEV) distributional form for the error term. Finally, the GEV assumption produces a MNL model by using the following equation: (4)
4
where all variables are same as previously defined and the vector can be estimated by using the standard maximum likelihood methods (Shankar and Mannering, 1996; Tay et al., 2011). The marginal effect analysis could help evaluate how the significant variables estimated in the MNL model impact the pedestrian injury outcome probabilities (Scott-Long, 1997). Since binary indicator variables (with the value of 0 or 1) are used in this study, the marginal effect can be computed as: (5)
4. Data assembling and analysis Pedestrian-vehicle crash data in the Highway Safety Information System (HSIS) database from 2005 to 2012 in North Carolina are used in this study. All these crash data were collected by all police departments in North Carolina on a standard form as prescribed by state law. The variables are selected based on the empirical results from existing literatures and records with missing information are dropped. After the data merging and cleaning process with the help of SAS PROC SQL function, 3553 cases are selected and used in the MNL modeling. Table 1 presents the count and ratio information about each contributing factor in which all continuous variables are converted into categorical variables. As shown, the distribution of pedestrian-vehicle crash severity is 15.96%, 12.16%, 36.7%, 28.76% and 6.42% for fatal, injury class 1, injury class 2, injury class 3 and no injury, respectively. For the drivers involved in the pedestrian-vehicle crashes, this table indicates that the majority (96.09%) of them are North Carolina drivers, the majority (66.51%) of drivers are between 26-65 years old, the majority (59.61%) of them are males, 76.78% of them are under sober status, 23.22% of them are drunk drivers, and 94.65% of them are under good physical condition. For the vehicles involved in the pedestrian-vehicle crashes, the table indicates the majority (71.57%) of them are passenger vehicles. For the pedestrians involved in the pedestrianvehicle crashes, the majority (57.84%) of them are between 26-65 years old. For the roadways involved in the pedestrian-vehicle crashes, the surface of the majority of them (86.72%) are dry, the pavement of the majority of them (97.3%) are asphalt and 93.75% of them are straight road. The majority of road classes are NC route (38.25%) and Local Street (40.59%), and the majority (67.94%) of terrains are rolling. For the environmental condition of the pedestrian-vehicle crashes, 91.64% of crashes occurred under good weather condition. The majority (97.86%) of crashes did not happened in work zones. Detailed descriptive statistics of other variables are also provided in the table. All the variables are considered in the study and used in the MNL model development. Table 1 Distribution (%) of Potential Contributing Factors at Different Severity Levels
Variable Driver State NC driver Out of state
Fatality count ratio 532 35
14.97 0.99
Injury class 1 count ratio 418 14
11.76 0.39
Severity level Injury class 2 Injury class 3 count ratio count ratio 1251 53
35.21 1.49
995 27
28 0.76
No Injury count ratio 218 10
6.14 0.28
Total count
ratio
3414 139
96.09 3.91
5
Variable driver Total Driver Age Under 26 26-65 Over 65 Total Driver Gender Male Female Total Alcoholic Status Sober Drunk Total Physical Condition Good condition Bad condition Total Vehicle Type Passenger veh Motorcycle Light truck Van Bus Heavy truck Total Pedestrian Age Under 26 26-65 Over 65 Total Road Surface Dry Wet Water Ice Snow Total Crash Location On roadway Outside roadway Total Road Characteristics Straight Curve Total Roadway Class Interstate US route NC route
Fatality count ratio
Injury class 1 count ratio
Severity level Injury class 2 Injury class 3 count ratio count ratio
No Injury count ratio
Total count
ratio
567
15.96
432
12.16
1304
36.7
1022
28.76
228
6.42
3553
100
141 377 49 567
3.97 10.61 1.38 15.96
108 288 36 432
3.04 8.11 1.01 12.16
302 859 143 1304
8.5 24.18 4.02 36.7
209 678 135 1022
5.88 19.08 3.8 28.76
41 161 26 228
1.15 4.53 0.73 6.42
801 2363 389 3553
22.54 66.51 10.95 100
389 178 567
10.95 5.01 15.96
252 180 432
7.09 5.07 12.16
763 541 1304
21.47 15.23 36.7
588 434 1022
16.55 12.22 28.76
126 102 228
3.55 2.87 6.42
2118 1435 3553
59.61 40.39 100
358 209 567
10.08 5.88 15.96
308 124 432
8.67 3.49 12.16
1012 292 1304
28.48 8.22 36.7
865 157 1022
24.35 4.42 28.76
185 43 228
5.21 1.21 6.42
2728 825 3553
76.78 23.22 100
514 53 567
14.47 1.49 15.96
406 26 432
11.43 0.73 12.16
1249 55 1304
35.15 1.55 36.7
979 43 1022
27.55 1.21 28.76
215 13 228
6.05 0.37 6.42
3363 190 3553
94.65 5.35 100
364 5 107 44 2 45 567
10.24 0.14 3.01 1.24 0.06 1.27 15.96
321 5 69 23 2 12 432
9.03 0.14 1.94 0.65 0.06 0.34 12.16
944 6 240 89 3 22 1304
26.57 0.17 6.75 2.5 0.08 0.62 36.7
744 4 186 72 8 8 1022
20.94 0.11 5.24 2.03 0.23 0.23 28.76
170 1 29 18 2 8 228
4.78 0.03 0.82 0.51 0.06 0.23 6.42
2543 21 631 246 17 95 3553
71.57 0.59 17.76 6.92 0.48 2.67 100
128 380 59 567
3.6 10.7 1.66 15.96
149 254 29 432
4.19 7.15 0.82 12.16
539 694 71 1304
15.17 19.53 2 36.7
377 592 53 1022
10.61 16.66 1.49 28.76
71 135 22 228
2 3.8 0.62 6.42
1264 2055 234 3553
35.58 57.84 6.59 100
496 65 4 0 2 567
13.96 1.83 0.11 0 0.06 15.96
366 64 1 0 1 432
10.3 1.8 0.03 0 0.03 12.16
1123 173 1 5 2 1304
31.61 4.87 0.03 0.14 0.06 36.7
890 126 4 1 1 1022
25.05 3.55 0.11 0.03 0.03 28.76
206 22 0 0 0 228
5.8 0.62 0 0 0 6.42
3081 450 10 6 6 3553
86.72 12.67 0.28 0.17 0.17 100
544 23 567
15.31 0.65 15.96
410 22 432
11.54 0.62 12.16
1232 72 1304
34.67 2.03 36.7
939 83 1022
26.43 2.34 28.76
213 15 228
5.99 0.42 6.42
3338 215 3553
93.95 6.05 100
515 52 567
14.49 1.46 15.96
390 42 432
10.98 1.18 12.16
1230 74 1304
34.62 2.08 36.7
979 43 1022
27.55 1.21 28.76
217 11 228
6.11 0.31 6.42
3331 222 3553
93.75 6.25 100
51 160 245
1.44 4.5 6.9
24 87 192
0.68 2.45 5.4
23 210 497
0.65 5.91 13.99
10 134 358
0.28 3.77 10.08
10 43 67
0.28 1.21 1.89
118 634 1359
3.32 17.84 38.25
6
Variable Local street Total Pavement Type Concrete Asphalt Others Total Terrain Flat Rolling Mountainous Total Speed Limit Under 35mph 35-50 mph Above 50 mph Total AADT Low Medium High Total Time of Day (TOD) Morning peak Daytime off peak Afternoon peak Night Total Day of Week (DOW) Weekday Weekend Total Rural/Urban Area Rural Urban Total Weather Condition Good condition Bad condition Total Light Condition Daylight Dusk/Dawn Dark Total Work Zone Not Work zone Work zone Total
Fatality count ratio 111 3.12 567 15.96
Injury class 1 count ratio 129 3.63 432 12.16
Severity level Injury class 2 Injury class 3 count ratio count ratio 574 16.16 520 14.64 1304 36.7 1022 28.76
No Injury count ratio 108 3.04 228 6.42
Total count 1442 3553
ratio 40.59 100
20 546 1 567
0.56 15.37 0.03 15.96
13 419 0 432
0.37 11.79 0 12.16
20 1278 6 1304
0.56 35.97 0.17 36.7
26 993 3 1022
0.73 27.95 0.08 28.76
6 221 1 228
0.17 6.22 0.03 6.42
85 3457 11 3553
2.39 97.3 0.31 100
203 335 29 567
5.71 9.43 0.82 15.96
115 291 26 432
3.24 8.19 0.73 12.16
324 916 64 1304
9.12 25.78 1.8 36.7
268 705 49 1022
7.54 19.84 1.38 28.76
46 167 15 228
1.29 4.7 0.42 6.42
956 2414 183 3553
26.91 67.94 5.15 100
93 140 334 567
2.62 3.94 9.4 15.96
127 134 171 432
3.57 3.77 4.81 12.16
489 374 441 1304
13.76 10.53 12.41 36.7
446 283 293 1022
12.55 7.97 8.25 28.76
111 67 50 228
3.12 1.89 1.41 6.42
1266 998 1289 3553
35.63 28.09 36.28 100
328 198 41 567
9.23 5.57 1.15 15.96
252 152 28 432
7.09 4.28 0.79 12.16
759 499 46 1304
21.36 14.04 1.29 36.7
578 420 24 1022
16.27 11.82 0.68 28.76
122 95 11 228
3.43 2.67 0.31 6.42
2039 1364 150 3553
57.39 38.39 4.22 100
38 45 131 353 567
1.07 1.27 3.69 9.94 15.96
62 70 113 187 432
1.75 1.97 3.18 5.26 12.16
156 287 396 465 1304
4.39 8.08 11.15 13.09 36.7
126 284 318 294 1022
3.55 7.99 8.95 8.27 28.76
17 62 81 68 228
0.48 1.75 2.28 1.91 6.42
399 748 1039 1367 3553
11.23 21.05 29.24 38.47 100
381 186 567
10.72 5.23 15.96
318 114 432
8.94 3.21 12.16
987 317 1304
27.78 8.92 36.7
803 219 1022
22.61 6.16 28.76
175 53 228
4.94 1.49 6.42
2664 889 3553
74.97 25.03 100
352 215 567
9.91 6.05 15.96
213 219 432
5.99 6.16 12.16
548 756 1304
15.42 21.28 36.7
380 642 1022
10.7 18.07 28.76
77 151 228
2.17 4.25 6.42
1570 1983 3553
44.19 55.81 100
519 48 567
14.61 1.35 15.96
394 38 432
11.09 1.07 12.16
1193 111 1304
33.58 3.12 36.7
942 80 1022
26.51 2.25 28.76
208 20 228
5.85 0.56 6.42
3256 297 3553
91.64 8.36 100
100 19 448 567
2.81 0.53 12.61 15.96
148 13 271 432
4.17 0.37 7.63 12.16
589 74 641 1304
16.58 2.08 18.04 36.7
550 34 438 1022
15.48 0.96 12.33 28.76
122 11 95 228
3.43 0.31 2.67 6.42
1509 151 1893 3553
42.47 4.25 53.28 100
555 12 567
15.62 0.34 15.96
426 6 432
11.99 0.17 12.16
1267 37 1304
35.66 1.04 36.7
1006 16 1022
28.31 0.45 28.76
223 5 228
6.28 0.14 6.42
3477 76 3553
97.86 2.14 100
7
5. Numerical results and discussions Many variables from the HSIS database are selected in the MNL model development. However, based on the results obtained by using the SAS PROC LOGISTIC procedure, the pvalues of some of the variables are larger than 0.1, which means that these variables are found to be insignificant and hence are removed from the list of significant variables. Table 2 and 3 show the results obtained from the study. Five vehicle-pedestrian crash severity levels (fatal crashes, injury class 1 crashes, injury class 2 crashes, injury class 3 crashes and no injury crashes) are considered as the dependent variable and no injury crashes are considered as the base case among these five crash severity levels. Therefore, all the estimated coefficients for the selected variables represent the effect of the variables on the specific fatal/injury level compared with no injury level. Table 2 Estimated Coefficients of Each Variable Variables
Fatal P-value 0.32
Injury class 1 Coef. P-value 0.97 0.00
Injury class 2 Coef. P-value 0.95 0.01
Injury class 3 Coef. P-value 0.19 0.65
Intercept
Coef. -0.28
Driver age (26-65)
-0.36
0.01
-0.29
0.03
-0.17
0.08
-
-
Driver age (over 65)
-0.60
0.01
-0.65
0.00
-0.26
0.08
-
-
0.46
0.05
-
-
-0.40
0.07
-0.40
0.09
1.18
0.04
1.03
0.06
-
-
-
-
-
-
-
-
-1.29
0.05
-
-
1.12
0.00
-
-
-
-
-0.97
0.01
0.52
0.00
-
-
-0.23
0.00
-
-
1.12
0.00
-
-
-0.63
0.00
-0.71
0.00
-0.87
0.00
-1.06
0.00
-0.66
0.01
-0.54
0.03
-0.55
0.00
-1.19
0.00
-0.71
0.00
-0.74
0.00
0.19
0.09
-0.90 -
0.00 -
-0.34 -
0.08 -
-0.62 -
0.00 -
0.81
0.01
-
-
0.49
0.01
-
-
0.98
0.00
0.63
0.00
-
-
-
-
-0.22
0.05
-
-
-
-
-0.16
0.07
-0.76
0.00
-
-
-
-
0.52
0.00
0.40
0.04
0.52
0.01
-
-
-
-
-0.23 -0.28
0.09 0.07
-
-
0.63 -
0.01 -
-
-
Physical condition (bad condition) Vehicle type (motorcycle) Vehicle type (bus) Vehicle type (heavy truck) Pedestrian age (2665) Pedestrian age (over 65) TOD (daytime offpeak) TOD (afternoon peak) TOD (night) DOW (weekend) Light condition (dusk and dawn) Light condition (dark) Terrain (rolling) Crash location (outside roadway) Roadway characteristics (curve) Work-zone Area type (urban) Road surface (wet)
8
Variables Road surface (water) Roadway class (US route) Roadway class (NC route) Roadway class (local street) Speed limit (35-50 mph) Speed limit (above 50 mph)
Coef.
Fatal P-value
Injury class 1 Coef. P-value
Injury class 2 Coef. P-value
Injury class 3 Coef. P-value
1.25
0.09
-
-
-
-
-
-
-
-
-
-
1.01
0.00
1.37
0.00
-
-
0.30
0.03
1.32
0.00
1.80
0.00
-
-
-
-
1.54
0.00
2.18
0.00
0.72
0.00
0.40
0.01
0.17
0.08
-
-
1.65
0.00
0.79
0.00
0.86
0.00
0.69
0.00
With the application of the MNL models, 26 variables of 15 categories are found to be significant in predicting the severity level of the crashes. The detailed MNL model can be written as follows (eqs. 6-9). (6)
= -0.28 -0.36 -0.87 +0.40
(7)
+0.52
= 0.95 -0.17 0.71 +0.17
(9)
-0.55 -0.23
-0.65 +0.30 -0.26
-0.34 +0.49 +0.86
= 0.19 -0.40 0.16
+0.46
+0.19 -0.28
= 0.97 -0.29 +0.63
(8)
-0.60
+0.52
-0.97 +1.37
+1.18
+0.81 +1.25
+1.12
-0.40
-0.22 +1.65
-0.76
-1.06
-1.19
-0.90
-0.71 +1.80
+0.79
-1.29
+0.63
+1.12
+0.98 +0.72
+1.03 +0.40
+0.52
-0.23
-0.63
+1.01
+1.32
-0.54
-0.74
+2.18
+0.69
-0.66
-
+1.54
-0.62
-
Where: X1 = Driver age indicator (1 if driver age is 26-65, 0 otherwise) X2 = Driver age indicator (1 if driver age is >65, 0 otherwise) X3 = Driver physical condition indicator (1 if condition is not normal, 0 otherwise) X4 = Vehicle type indicator (1 if vehicle is motorcycle, 0 otherwise) X5 = Vehicle type indicator (1 if vehicle is bus, 0 otherwise) X6 = Vehicle type indicator (1 if vehicle is heave truck, 0 otherwise) X7 = Pedestrian age indicator (1 if pedestrian age is 26-65, 0 otherwise) X8 = Pedestrian age indicator (1 if pedestrian age is >65, 0 otherwise) X9 = Time of day indicator (1 if time period is daytime off-peak (10AM-15PM), 0 otherwise) X10 = Time of day indicator (1 if time period is afternoon peak (16PM-19PM), 0 otherwise) X11 = Time of day indicator (1 if time period is night (20PM-5AM), 0 otherwise) X12 = Day of week indicator (1 if weekend, 0 otherwise) 9
X13 = Light condition indicator (1 if light condition is dusk and dawn, 0 otherwise) X14 = Light condition indicator (1 if light condition is dark, 0 otherwise) X15 = Terrain indicator (1 if terrain is rolling, 0 otherwise) X16 = Crash location indicator (1 if outside roadway, 0 otherwise) X17 = Roadway characteristic indicator (1 if location is curve, 0 otherwise) X18 = Work zone indicator (1 if location is work-zone, 0 otherwise) X19 = Area type indicator (1 if urban area, 0 otherwise) X20 = Road surface indicator (1 if wet surface, 0 otherwise) X21 = Road surface indicator (1 if water surface, 0 otherwise) X22 = Roadway class indicator (1 if US route, 0 otherwise) X23 = Roadway class indicator (1 if NC route, 0 otherwise) X24 = Roadway class indicator (1 if Local street, 0 otherwise) X25 = Road speed limit indicator (1 if speed limit between 35mph to 50mph, 0 otherwise) X26 = Road speed limit indicator (1 if speed limit above 50mph, 0 otherwise) Based on the developed equations (eqs.6-9) above, the marginal effects of the model are also calculated and presented in Table 3. Using the first variable as an example, if the driver age category changes from under 26 to 26-65, the probability of fatality, injury class 1 (disabling injury) and injury class 2 (evident injury) decreases by 0.025, 0.019 and 0.01, respectively, while the probability of injury class 3 (possible injury) and no injury increases by 0.047 and 0.007, respectively. Note that the sum of marginal effects of each variable is zero, which satisfies the overall requirement that the sum of probability is 1. Table 3 Marginal Effects Results Variable Driver age (26-65) Driver age (over 65) Physical condition (bad condition) Vehicle type (motorcycle) Vehicle type (bus) Vehicle type (heavy truck) Pedestrian age (26-65) Pedestrian age (over 65) TOD (daytime off-peak) TOD (afternoon peak) TOD (night) DOW (Weekend) Light condition (dusk and dawn) Light condition (dark) Terrain (rolling) Area type (urban) Crash location (outside roadway) Roadway characteristics (curve) Work-zone Road surface (wet)
P(Fatality) -0.025 -0.037 0.069 0.097 0.002 0.227 0.06 0.223 -0.009 0.04 0.078 0.022 0.105 0.09 -0.017 -0.024 -0.079 0.03 0.002 -0.03
P (Injury class 1) -0.019 -0.047 0.092 0.152 0.205 -0.001 -0.004 0.009 -0.037 -0.046 -0.045 -0.005 -0.019 0.054 0.016 0.005 -0.016 0.055 0.09 0.007
P (Injury class 2) -0.01 -0.013 -0.069 -0.065 -0.257 0.016 -0.061 -0.089 -0.102 -0.097 -0.071 -0.005 -0.049 -0.039 0.018 0.008 -0.041 -0.032 -0.116 0.007
P (Injury class 3) 0.047 0.084 -0.098 -0.16 0.045 -0.251 0.005 -0.154 0.094 0.045 -0.002 -0.01 -0.031 -0.091 -0.024 0.009 0.145 -0.046 0.021 0.014
P (No Injury) 0.007 0.013 0.005 -0.024 0.005 0.008 0 0.012 0.055 0.058 0.04 -0.002 -0.005 -0.014 0.006 0.002 -0.009 -0.007 0.003 0.002
10
Road surface (water) Roadway class (US route) Roadway class (NC route) Roadway class (local street) Speed limit (35-50 mph) Speed limit (above 50 mph)
0.182 -0.065 -0.1 -0.117 0.084 0.126
-0.042 -0.074 -0.071 -0.137 0.039 0.004
-0.04 -0.077 -0.062 -0.053 -0.101 -0.094
-0.087 0.248 0.281 0.377 -0.018 0.002
-0.013 -0.032 -0.048 -0.07 -0.004 -0.037
5.1.Drivers’ characteristics results With respect to drivers’ ages, previous studies usually divided ages into three groups to represent young people (under 26), mid-age people (26-65) and elderly (over 65), respectively (e.g., Tay et al., 2011). Therefore, three age groups (under 26, 26-65 and over 65) are also considered in this study. The results in Table 2 show that senior drivers (over 65 years old) and middle-aged drivers (26-65 years old) are less likely to be involved in fatality compared to young drivers. In addition, the marginal effect result as shown in Table 3 also indicates that the probability of fatal crash will reduce when the driver group changes from young driver to senior driver. This might because senior drivers tend to drive at lower speeds and therefore, the pedestrian will be less likely to be involved in a fatal crash (Shankar and Mannering, 1996; Fan et al., 2011). The result of physical condition shown in Table 2 indicates that drivers with bad physical condition are more likely to be involved in a fatal crash. The marginal effect result as shown in Table 3 also indicates that the physical condition being not normal increases the likelihood of fatal and injury class 1 crashes (disabling injury). Such results are consistent with previous studies that bad physical condition (such as fatigue) will increase the probability of fatal crashes (e.g., Desmond et al., 1998). Six vehicle categories (ranged from passenger car to heavy truck types) are considered in this study. As shown in table 2, motorcycle and heavy truck are more likely to result in fatal crashes. The marginal effect results in Table 3 also indicate motorcycle, bus and heavy truck would increase the likelihood of fatal crashes. The results are similar to previous studies that focused on the other type of crashes (e.g., Fan et al. 2011) and could be explained by the physical (and thus more dangerous) characteristics of buses and heavy trucks. 5.2.Pedestrian’s characteristics results Similar to driver age groups, three pedestrian age groups (pedestrian age under 26, between 26-65 years old, and age over 65) are also considered in this study. For the pedestrian age between 26-65 and age over 65 groups, the estimation result in Table 2 and marginal effect result in Table 3 both indicate that these two groups increase the likelihood of fatal crashes compared with young pedestrians. This result can be explained by the older pedestrian’s physiological characteristics, such as the need for more reaction times and the generally lower physical fitness level compared with younger pedestrian group. This result is consistent with findings from the previous studies (Kim et al., 2008; Tay et al., 2011). 5.3.Environmental condition characteristics results Four time of day categories (ranging from morning peak to night) are considered in this study. As shown in Table 2, daytime off-peak and afternoon peak are less likely to result in fatal 11
crashes. The marginal effect result shown in Table 3 indicates that night increases the likelihood of fatal crashes. These probably suggest that fatigue and bad visibility during night period could increase the rate of fatal crashes (Sze and Wong, 2007; Tay et al., 2011). With respect to day of week, the result in Table 2 shows that weekend is more likely to increase the crash severity. The marginal effect result as shown in Table 3 also indicates that weekend increases the likelihood of fatal crashes. This could be explained by that during weekends traffic volume is typically lower than that in weekdays, which in turn results in higher travel speeds and thus the likelihood of fatal crashes. Three light condition categories (ranged from daylight to dark) are considered in this study. As shown in Table 2, dusk, dawn and dark are more likely to result in fatal crashes. The marginal effect results shown in Table 3 also indicate these conditions increase the likelihood of fatal crashes. These might suggest that compared with day light, bad visibility of pedestrians is more likely to increase the fatal crash possibility (Kim et al., 2008). Three terrain condition categories (flat, rolling and mountainous) are considered in this study. As shown in Table 2, rolling terrain is less likely to result in fatal crashes. The marginal effect result as shown in Table 3 also indicates that changing from flat terrain category to rolling category decreases the likelihood of fatal crashes. This could be explained by that compared with flat terrain condition, the vehicle speed may become lower than that under rolling condition. With respect to crash area type, the result in Table 2 shows that rural areas are more likely to be involved in serious injury compared to urban areas. The marginal effect result as shown in Table 3 also indicates that urban area decreases the likelihood of fatal crashes. This might suggest that compared with rural areas, urban areas are more likely to have better traffic control (e.g. pedestrian signal phases), traffic infrastructures and medical facilities. This result is consistent with a previous study (Lee and Abdel-Aty, 2005). 5.4.Road condition characteristics results With respect to crash location type, the result in Table 2 shows that outside roadway is more likely to reduce the crash severity. The marginal effect result as shown in Table 3 also indicates that outside roadway decreases the likelihood of fatal crashes. This could be explained by that compared with crashes on the roads, the vehicle speed may become lower when driving outside the roadways and the damage to the pedestrian will be lower as well. The result of roadway characteristics shows that curve roads are more likely to increase the fatal crash possibility than the straight roads. The marginal effect result as shown in Table 3 also indicates that curve locations increase the likelihood of fatal crashes, perhaps due to the fact that the line of sight might become unavailable at curve locations. As shown in Table 3, work-zone areas are more likely to increase the fatal crash possibility than the non-work zone areas. On the other hand, this result indicates that non-work zone areas are less likely to lead severe injury and fatal crashes than work-zone areas. The reason of this outcome is uncertain but it is consistent with the previous study (Khattak et al., 2002). Five road surface categories (ranged from dry to snow) are considered in this study. As shown in table 2, water surface is more likely to result in fatal crashes. The marginal effect results in Table 3 indicate only wet surface decreases the likelihood of fatal crashes. The reasons behind these interesting results might suggest that the vehicles are more likely to be driven with caution under low speed on the wet surface. However, the water surface usually associated with 12
the bad weather condition and the visibility of pedestrian and driver may be influenced under that situation. Four roadway class categories (ranged from Interstate to Local Street) are accounted for in this study. The marginal effect results shown in Table 3 indicate Interstate may increase the likelihood of fatal crashes. These probably suggest that Interstates are associated with higher vehicle speed which could increase the injury severity and the result is consistent with the previous studies (e.g., Tay et al. 2011). Three speed limit categories (less than 35 mph, 35-50 mph and above 50 mph) are considered in this study. The results of speed limit shown in Table 2 indicate that speed limit between 35-50 mph and speed limit above 50 mph are significant and more likely to increase the fatal crash possibility than the speed limit less than 35 mph. This result is consistent with the previous studies (e.g., Kim et al. 2008). 6. Conclusions and future research directions Pedestrian-vehicle collision is a serious transportation issue today. This study aims to identify the significant influencing factors contributing to the severity level of vehicle-pedestrian crashes to provide policy makers with evidence-based recommendations to address this transportation issue. The results show that the factors that may significantly increase the fatal crash probability include: driver’s physical condition (bad condition); vehicle type (motorcycle and heavy truck); pedestrian age (26-65 and over 65); weekend; light condition (dawn, dusk and dark); roadway characteristic (curve), roadway surface (water), and speed limit (35-50 mph and above 50 mph). The results show that the factors that may significantly increase the injury class 1 crash (disabling injury) probability include: vehicle type (motorcycle); light condition (dark); roadway characteristic (curve); road class (NC route), and speed limit (35-50 mph and above 50 mph). The results show that the factors that may significantly increase the injury class 2 crash (evident injury) probability include: light condition (dusk and dawn); work-zone; roadway class (US route, NC route and local street), and speed limit (35-50 mph and above 50 mph). The results show that the factors that may significantly increase the injury class 3 crash (possible injury) probability include: crash location (outside roadway), roadway class (US route, NC route and local street), and speed limit (above 50 mph). The methodology and results of this study can be helpful for the identification of the contributing crash factors related work in the real world. However, the results of some factors are still uncertain and need to be further investigated. In the future, other types of model could also be developed and compared with the MNL model. Furthermore, elasticity analysis could also be conducted.
Acknowledgments The authors want to express their deepest gratitude to the financial support by the United States Department of Transportation, University Transportation Center through the Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE) at The University of North Carolina at Charlotte (Grant Number: 69A3551747133). 13
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