evaluation of pedestrian safety

104

Transportation Research Record: Journal of the Transportation Research Board, No. 2393, Transportation Research Board of the National Academies, Washington, D.C., 2013, pp. 104–116.DOI: 10.3141/2393-12

Pedestrian-involved crashes that occurred in the city of San Francisco, California, over 6 years from 2002 to 2007 were analyzed to evaluate two key aspects of pedestrian safety: occurrence and severity. This analysis was done to identify locations with frequent occurrences of pedestrian-involved crashes and to examine various risk factors for the injury severity of pedestrian-involved crashes. A geographical information system analysis for hot spot identification showed that the frequency of pedestrian crashes was greater in the vicinity of the central business dis-trict but that the crash rate (the number of crashes per walking trip) was higher in the periphery of the city. For injury analysis, an ordered probit model was specified to evaluate risk factors that increased the probabil-ity of severe injury and fatality. Those factors were age (<15 and >–65), alcohol consumption, and cell phone use among pedestrian characteris-tics; nighttime, weekends, and rainy weather among environmental char-acteristics; and, among crash characteristics, the influence of alcohol, larger vehicles (pickups, buses, and trucks), and vehicles proceeding straight and striking a pedestrian. The methods discussed are readily applicable to the evaluation of safety performance in other regions where pedestrian crash data are available.

Walking is the most basic and common form of transportation asso-ciated with daily life. It provides health benefits as long as injuries caused by traffic crashes are avoided. In 2007, 4,652 pedestrians were killed and approximately 70,000 were injured in traffic crashes, accounting for 11% of total traffic fatalities and 3% of total traffic injuries in the United States (1). Though continuously diminish-ing over the past decade, pedestrian-involved crashes still remain a serious public health problem. Pedestrians are more vulnerable to

traffic crashes than those using other transportation modes because the human body is directly exposed to the forces involved. Pucher and Dijkstra reported that pedestrians were 23 times more likely to be killed than vehicle drivers (2).

Such a high risk of pedestrian injuries and fatalities in the United States has garnered increased attention in recent years and extensive efforts have been devoted to enhance pedestrian safety in various aspects. Two common principles for enhancing pedestrian safety include (a) diminishing the occurrence of pedestrian-involved crashes and (b) reducing the level of injury severity when pedestrians are exposed to traffic crashes. Preceding conditions for these principles include identifying locales where pedestrian-involved crashes are concentrated and evaluation of risk factors that increase the level of injury severity. In this regard, the current study applied two approaches to pedestrian safety evaluation: analysis of crash hot spots with high pedestrian involvement and analysis of pedestrian injury severity. The former performed spatial analysis by using a geographical infor-mation system (GIS) to detect clustered pedestrian-involved crashes, and the latter applied an ordered probit model to evaluate various risk factors for pedestrian injuries.

Literature review

identification of Hot Spots of Pedestrian-involved Crashes

An initial step for improving traffic safety, including pedestrian safety, is to identify locations or areas where safety problems exist. Because of the importance of improving roadway safety perfor-mance, extensive research efforts have been made to identify crash hot spots purely on the basis of various statistical models (3–16). However, these methods are not readily applicable to pedestrian crashes because key input variables (e.g., traffic and pedestrian volumes) for those models are difficult to collect so they are often unavailable in a satisfactory degree of detail. Accurate traffic vol-umes are often unavailable for local streets, collector roads, and arterials where pedestrian crashes frequently occur. Furthermore, it is more difficult to collect pedestrian volumes for the entire street network (17–20).

As an alternative solution for this issue, GIS analysis has been considered when geocoded crashes are available (21–24). The methods used in these studies examined the clustering patterns of pedestrian crashes and identified locations where clustering was more pronounced. However, these studies did not consider the pos-sible influence on the estimated outcome of excluding exposure

Evaluation of Pedestrian SafetyPedestrian Crash Hot Spots and risk Factors for injury Severity

Kitae Jang, Shin Hyoung Park, Sanghyeok Kang, Ki Han Song, Seungmo Kang, and Sungbong Chung

K. Jang, Cho Chun Shik Graduate School for Green Transportation, Korea Advanced Institute of Science and Technology, N7-5, 291 Daehak-Ro, Yuseong-Gu, Daejeon 305-701, South Korea. S. H. Park, Department of Transportation Engineering, Keimyung University, 2800 Dalgubeol-Daero, Dalseo-Gu, Daegu 704-701, South Korea. Sa. Kang, Construction and Economy Research Institute of Korea, Construction Building, 11th Floor, 711 Eonjuro, Kangnam-Gu, Seoul 135-701, South Korea. K. H. Song, Department of Aviation Research, Korea Transport Institute, 1160 Simindaero Ilsanseo-Gu, Goyang-Si, Gyeonggi-Do 411-701, South Korea. Se. Kang, School of Civil, Environmental and Architectural Engineering, Korea University, Anam-Dong 5-1, Sung-Buk Gu, Seoul 136-701, South Korea. S. Chung, Graduate School of Railroads, Seoul National Univer-sity of Science and Technology, 138 Gongneung-Gil, Nowon-Gu, Seoul 139-743, South Korea. Corresponding author: S. Chung, [email protected].

Jang, Park, Kang, Song, Kang, and Chung 105

(i.e., pedestrian volume). The current study estimates walking trips per traffic analysis zone (TAZ) based on travel survey data and com-pares outcomes with and without including exposure in hot spot analysis.

Furthermore, geocoded locations of crashes are often unavailable because crash information is manually recorded in textual form by police officers at the crash scenes (25, 26), as is the case with the data in this study. The method by Bigham et al. was adopted to geo-code locations of pedestrian-involved crashes in the city of San Fran-cisco, California, for 6 years, from 2002 to 2007 (25). The geocoded pedestrian-involved crash data were then used for kernel estimation cluster analysis over the study region. This method can visualize crash occurrences and densities to help engineers and planners focus on safety improvements at intersections, corridors, or neighborhoods where pedestrian crashes occur frequently.

Models evaluating risk Factors for Pedestrian injuries

In previous research examining risk factors affecting the level of pedestrian injury in traffic crashes, Roudsari et al. and Sze and Wong conducted multivariate binary logistic regression analysis to evaluate the injury risk of pedestrian casualties in traffic crashes in relation to the contributory factors to severe injuries and fatalities (27, 28). This research reported that light trucks were associated with two times higher risk of pedestrian fatalities and three times higher risk of severe pedestrian injuries compared with passenger vehicles. However, the binary measure of injury severity is overly aggregated to properly reflect variations in injury severity. In addition, the model used by Roudsari et al. did not include other factors that had potentially influenced injury severity and thus possible confounding factors in the outcomes of the model were induced (27).

Davis used both logistic and ordered probit models to relate pedes-trians’ injury severity to the impact speed of the vehicle for three age groups: children (ages 0 to 14), adults (ages 15 to 59), and elderly (ages 60+) (29). The results indicated that elderly pedestrians were more likely to be involved with severe injury than other age groups when exposed to the same level of impact speed. Again, the model in this study only considered two variables, age and impact speed, so the impacts of other characteristics that are likely to influence injury severity were overlooked.

Eluru et al. developed a mixed generalized ordered response logit model and applied it to the level of injuries in nonmotorized traffic crashes with pedestrians and bicycles (30). The study reported that the most important variables influencing nonmotorist injury sever-ity were age of the nonmotorist, the speed limit of the roadway, the location of the crash, and time of the crash. Since the study attempted to estimate risk factors in two different transportation modes together that have different characteristics, only characteristics common in both modes were included in the analysis.

Zajac and Ivan and Lee and Abdel-Aty used an ordered probit model to investigate the influence of various features on level of injury in pedestrian crashes on rural two-lane highways and at inter-sections, respectively (31, 32). Both studies identified some common features that significantly influenced pedestrian injury such as type of vehicle, driver, pedestrian alcohol involvement, and pedestrians older than 65. The models of these studies were limited to specific roadway conditions.

Siddiqui et al. specified an ordered probit model to assess the impacts of crossing locations and lighting conditions on pedestrian

injury severity while controlling for other factors that may also have influenced pedestrian injury severity (33). The factors included attributes of pedestrians, drivers, and the environment. Although the study estimated the effect of various features on pedestrian injury severity, there was a lack of consideration of the characteristics regarding the crash itself.

Despite all of these extensive research efforts to reveal the impact of risk factors on pedestrian injury severity, unanswered questions remain.

Data DeSCriPtion

In California, the California Highway Patrol enters data from their reports, as well as those from local law enforcement agencies, into an integrated records system named the Statewide Integrated Traffic Records System (SWITRS). Data from approximately 4,000 fatal and 190,000 non-fatal-injury crashes are added to the system annu-ally, as well as data from more than 200,000 property damage only (PDO) crashes.

In this research, to investigate the influence of risk factors on injury severity in pedestrian crashes, data on all pedestrian crashes on public roadways in the city of San Francisco from 2002 to 2007 were obtained from SWITRS. The city of San Francisco was cho-sen for this study because compared with other jurisdictions, San Francisco records PDO crashes relatively well and a wide range of pedestrian trips is observable. Each record in the database has detailed information on crashes. As shown in Table 1, a total of 5,084 pedestrian crashes, including PDO crashes, were recorded in San Francisco over the 6-year period (2002 to 2007). These crashes are projected onto a map for five levels of injury severity: PDO, slight injury (complaint of pain), visible injury (other visible), severe injury (extended hospitalization), and fatality (Figure 1). The maps show that the pedestrian crashes tend to be clustered in the vicin-ity of the central business district (CBD) in the San Francisco area (upper right-hand corner of the map), where many trips are made by walking.

Table 1 furnishes descriptive statistics for the variables. Slight and visible injuries composed more than 85% of total crashes: 53.42% for slight injuries and 32.83% for visible injuries. PDO and fatal crashes composed only 1.91% and 2.85%, respectively. An additional 25 explanatory variables were classified into four categories describing the characteristics of pedestrian, driver, environment, and crash for each recorded crash, as summarized in Table 1. The reference case is presented in italics in Table 1.

iDentiFying Hot SPotS oF PeDeStrian-invoLveD CraSHeS

Kernel Density analysis

This section describes spatial kernel density estimation (34, 35, 22), which was used to identify areas of concentrated pedestrian-involved crashes. Kernel density measures the density of points of inter-est (pedestrian crashes in the current case) in the vicinity of each reference point within a space. The details of the procedure are as follows:

1. Determine a point as a reference and then set the symmetrical surface [i.e., bandwidth (h)] surrounding the reference point.

TABLE 1 Model Characteristics

Characteristic Variable Description Number Percent

Dependent Level of pedestrian Property damage only 97 1.91 variables injury Slight injury (complaint of pain) 2,716 53.42

Visible injury (other visible) 1,669 32.83Severe injury (extended

hospitalization)457 8.99

Fatal 145 2.85

Pedestrian PFAULT Pedestrian at fault 1,652 32.49Otherwise 3,432 67.51

PSEX Female 2,374 46.70Male 2,653 52.18Unknown 57 1.12

PAGE Younger than 15 407 8.01Older than 65 653 12.84Between 15 and 65 3,831 75.35Unknown 193 3.80

PUI Pedestrian had been drinking 186 3.66Otherwise 4,898 96.34

PCELL Pedestrian in cell phone use 31 0.61Otherwise 5,053 99.39

PRACE Asian 1,146 22.54Black 755 14.85White 1,942 38.20Hispanic 702 13.81Other 539 10.60

Driver DFAULT Driver at fault 3,113 61.23Otherwise 1,971 38.77

DSEX Female 1,371 26.97Male 3,369 66.27Unknown 344 6.77

DAGE Younger than 15 7 0.14Older than 65 365 7.18Between 15 and 65 3,979 78.27Unknown 733 14.42

DUI Driver had been drinking 81 1.59Otherwise 5,003 98.41

DCELL Driver in cell phone use 24 0.47Otherwise 5,060 99.53

DRACE Asian 898 17.66Black 621 12.21White 2,098 41.27Hispanic 580 11.41Other 887 17.45

Environment Year 2002 951 18.712003 892 17.552004 784 15.422005 806 15.852006 781 15.362007 870 17.11

Time Midnight to 6:00 a.m. 380 7.476:00 a.m. to noon 1,308 25.73Noon to 6:00 p.m. 1,975 38.856:00 p.m. to midnight 1,421 27.95

Weekend Weekdays 3,918 77.06Weekends 1,166 22.94

Intersect Intersection crash 1,548 30.45Otherwise 3,536 69.55

Weather Clear 3,978 78.25Raining 591 11.62Other 515 10.13

Crosswalk Crash when pedestrian crossing crosswalk

2,891 56.86

Otherwise 2,193 43.14NCROSSWALK Crash when pedestrian crossing

noncrosswalk1,184 23.29

Otherwise 3,900 76.71Lighting Daylight 3,215 63.24

Dusk–dawn 190 3.74Dark–light 1,587 31.22Dark–no light 65 1.28Unknown 27 0.53

(continued on next page)


TABLE 1 (continued) Model Characteristics

Characteristic Variable Description Number Percent

Crash Primary crash factor

Influence of alcohol Unsafe speed

59 286

1.16 5.63

Improper passing 79 1.55Improper turning 56 1.10Automobile right-of-way 68 1.34Pedestrian right-of-way 1,870 36.78Pedestrian violation 1,657 32.59Traffic signals and signs 236 4.64Other hazardous violation 91 1.79Unsafe starting or backing 241 4.74Other 145 2.85Unknown 296 5.82

HITRUN Hit-and-run crash 694 13.65Otherwise 4,390 86.35

DMOVE Proceeding straight 1,741 34.24Making right turn 379 7.45Making left turn 731 14.38Backing 186 3.66Other 366 7.20na 1,681 33.07

DVEHTYPE Passenger car 3,173 62.41Motorcycle–scooter 94 1.85Pickup 421 8.28Truck 66 1.30Bus 192 3.78Bicycle 108 2.12Other 1,030 20.26

Parties (other than pedestrian)

0 1

4 4,717

0.08 92.78

2 279 5.493 49 0.964 23 0.455 8 0.166 2 0.047 2 0.04

Note: Reference case is in italics; na = not applicable.

(a) (b) (c)

FIGURE 1 Locations of pedestrian-involved crashes for different levels of injury: (a) fatality, (b) severe injury and (c) visible injury.(continued on next page)

108 Transportation Research Record 2393

destination matrix provides the number of daily trips by different transportation modes. In general, walking trips are interzonal (trips within the origin) or connected to the neighboring zones (Figure 4a); there was a negligible number of walking trips across zones. There-fore, the pedestrian traffic volume can be estimated for the ith TAZ by summing all walking trips originating from TAZ i with a desti-nation in TAZ i. These estimates are provided in Figure 4b. Visual inspection of Figure 4b shows that pedestrian-involved crashes are observed in the TAZs where pedestrian volumes are greater, as is evident from the similarity between Figure 3f and Figure 4a.

As a next step, total pedestrian-involved crashes were normalized by the number of walk trips to investigate the impact of exposure, since more walk trips are very likely to result in more pedestrian-involved crashes. Figure 4b shows almost the opposite pattern: higher pedestrian-involved crash rates in the periphery of the study area. This finding means that likelihood of pedestrian crash occur-rence for the same exposure measured by the number of walk trips is higher in the periphery.

A substantial difference between the two measures—count and rate—is observed in the analysis. Both have pros and cons in mea-suring the risk of pedestrian crashes. Count-based hot spot identi-fication does not consider exposure and therefore it is more likely to select areas with higher exposure. Meanwhile, the crash rate can vary widely when the denominator (i.e., exposure) is small; this finding means that the measure can be more influenced by statis-tical instability. The attributes in these two measures imply that results may be biased in the detection of hot spots when only a single measure is considered.

AnAlysis of PedestriAn injury severity

In this section the relationship between the level of pedestrian injury and risk factors when pedestrians are exposed to traffic crashes is evaluated. A pedestrian injury in a traffic crash is categorized into a discrete and ordinal level according to how severely the pedestrian is injured, given a latent and continuous injury descriptor underly-ing the categories. Though the level of a pedestrian injury is cate-gorical, multinomial logit and probit models do not account for the ordinal nature inherent in the level of injury and, as such, they would not be appropriate in evaluating pedestrian injuries. Thus, the

2. Calculate the distance between the reference point and each data point within the bandwidth (di).

3. Input h and di into the kernel function, [K(•)], which is asym-metric. The form of this function can be in different shapes such as uniform, triangular, quadratic, and Gaussian.

4. Sum K(•) for all the data points (N), within the bandwidth and calculate the kernel density:

iNh

Kdh

i

i

N

∑ ( )=

12

1

5. Iterate this procedure for successive points within the study area [more details are given by Fotheringham et al. (36)].

Figure 2 presents kernel density estimates using this procedure for different injury levels. The shading corresponds to the magni-tude of kernel density: the darker the shade, the higher the density (i.e., pedestrian-involved crashes are more concentrated). Darker shaded areas are concentrated near CBD areas. Patterns across dif-ferent injury levels are quite similar although some variations exist; for example, fatalities tend to be more widespread.

Aggregate Analysis

Since pedestrian traffic volumes, a key exposure variable for pedestrian-involved crashes, are only available at an aggregated level (i.e., TAZ, the geographical unit that is constructed on the basis of census block information and commonly used for transportation planning purposes), the number of pedestrian-involved crashes within each TAZ was counted and is presented in Figure 3. The spa-tial distributions of different injury levels in Figure 3 show compara-ble patterns with those observed in Figure 2. Since spatial distribution patterns across different injury levels also exhibit similar patterns, an exposure analysis was conducted with the data aggregated across different injury levels.

To estimate pedestrian volume, an origin–destination matrix was used that was estimated on the basis of the Bay Area Travel Survey by the Metropolitan Transportation Commission (37). The origin–

FIGURE 1 (continued) Locations of pedestrian-involved crashes for different levels of injury: (d) slight injury, (e) PDO, and (f) total crashes.

(d) (e) (f)

(a) (b) (c)

(d) (e) (f)

FIGURE 2 Kernel density of pedestrian-involved crashes for different levels of injury: (a) fatality, (b) severe injury, (c) visible injury, (d) slight injury, (e) PDO, and (f) total crashes.

(a) (b) (c)

(d) (e) (f)

FIGURE 3 Map of pedestrian-involved crashes aggregated by TAZ: (a) fatality, (b) severe injury, (c) visible injury, (d) slight injury, (e) PDO, and (f) total crashes.


where thresholds ψi are unknown parameters to be estimated along with β.

To solve for those unknowns,

ψ < ≤ψ ⇔ ψ < ′β + ε ≤ψ ⇔ ψ − ′β <ε ≤ψ − ′β− − −*1 1 1I X X Xi p i i p p i i p p i p

Then, since εp is assumed to follow a standard normal distribution,

( ) ( )( )= = Φ < ψ − ′β − Φ ≤ ψ − ′β−Pr **1I i I X I Xp p i p p i p

where

I X

I Xp p

p p

( )( )

Φ < ψ − ′β =Φ ≤ ψ − ′β =

0*

* 10

5

Pr(Ip = i) = probability that pth pedestrian experiences i level of injury (i = 1, 2, . . . , 5), and Φ( ) is the standard nor-mal cumulative distribution function.

The maximum likelihood estimation was used to obtain estimators of parameters in the model: ψ1, ψ2, ψ3, ψ4, β0, β1, . . . , βn. This pro-cedure can be performed by using commercially available statistical software (e.g., Limdep and STATA). Since the derivation is outside the current research scope, the full derivation is not described here [more details are given by McKelvey and Zavoina (38)].

Marginal Effects

In the ordered probit model, the parameters are not directly inter-preted to the marginal effects of xp,n on the probabilities. Since there are five categories of injury in the pedestrian crash, the marginal effect of a variable, xn,p, on each of the ordinal categories can be computed as Δ(Ip = i | xn,p) = Pr(Ip = i | xn,p = i) − Pr(Ip = i | xn,p = i − 1).

While all the others are held constant, one unit change in a vari-able, xp,n, shifts the distribution toward the direction of the sign of

ordered probit model was used in the current research. An ordered probit model was specified and applied to the collected data in the next subsection. The estimated results and their interpretation are furnished after that.

ordered Probit Model

Model Specification

The ordered probit model has been widely used to analyze ordinal and categorical responses. The model is specified as follows:

= ′β + ε*I Xp p p

where

I*p = latent and continuous variable measuring injury severity of pth pedestrian;

β = vector of unknown parameters to be estimated; Xp = vector of observed variables describing pedestrian, driver,

environment, and crash involved with pth pedestrian; and εp = random error term, assumed to be normally distributed

with zero mean and unit variance (i.e., a standard normal distribution).

I*p cannot be directly observed in any given pedestrian crash. Only a discrete level of injury severity, Ip, is observed and determined from the model in a form of censoring:

I

I

I

I

I

I

p

p

p

p

p

p

( )( )

( )

( )

( )

( )

( )

=

−∞ < ≤ ψ

ψ < ≤ ψ

ψ < ≤ ψ

ψ < ≤ ψ

ψ < ≤ ∞

1 if property damage only PDO

2 if injury complaints of pain Injury

3 if injury other visible Injury

4 if injury severe Injury

5 if fatal*

*

*

*

*1

1 2 1

2 3 2

3 4 3

4

(a) (b)

FIGURE 4 Map of pedestrian-involved crash rate aggregated by TAZ: (a) walk trips and (b) pedestrian crash counts normalized by number of walk trips.


trian characteristics, was estimated. The outcomes from a compara-tive study were summarized in an earlier study (39). In the current study, interpretation is made on the outcomes of the selected model.

Model Evaluation and Estimates

To evaluate the estimated results, the robust standard errors were examined first. Robust standard errors can consistently estimate the true standard errors and provide a basis for valid inferences about the parameter estimates, even when the usual standard errors are biased. However, if the model is nearly correct, the usual standard errors are likely to be valid and equivalent to the robust standard errors [more details and proofs are given by Huber (40), White (41), and Freedman (42)]. As shown in Table 2, the values of the robust

β. The increase in variables, xp,n, associated with the parameters β with positive signs shifts the distribution toward the right. Thus, this shift results in increasing the probability of the rightmost cate-gory (i.e., Ip = 5, fatal) and diminishing the probability of the left-most category (i.e., Ip = 1, PDO). Meanwhile, the negative signs are conversely interpreted. However, the marginal effects for the categories in between depend on the shifted amount of probability densities.

estimated results and Marginal effects

Since the pedestrian is the subject directly exposed to the crashes (as well as the primary interest in this study), the ordered probit model with every combination of characteristics, including pedes-

TABLE 2 Ordered Probit Estimates for Pedestrian Injuries

Characteristic Variable Description Coefficient SE Robust SE p-Value

Pedestrian PFAULT Pedestrian at fault 0.138 0.093 0.094 .141PSEX Male −0.015 0.034 0.033 .654

Unknown −0.169 0.164 0.194 .384PAGE Older than 65 0.203*** 0.072 0.074 .006

Between 15 and 65 −0.193*** 0.06 0.06 .001Unknown 0.354*** 0.103 0.13 .006

PUI Pedestrian had been drinking 0.400*** 0.087 0.097 0PCELL Pedestrian in cell phone use 0.422** 0.2 0.167 .011PRACE Black −0.341*** 0.057 0.056 0

White 0.06 0.044 0.044 .17Hispanic −0.025 0.056 0.056 .661Other −0.067 0.061 0.061 .268

Driver DFAULT Driver at fault −0.01 0.081 0.082 .901DSEX Male 0.003 0.037 0.037 .933

Unknown 0.344 0.096 0.102 .001DAGE Older than 65 0.233 0.444 0.321 .469

Between 15 and 65 0.167 0.441 0.317 .598Unknown 0.037 0.447 0.326 .91

DUI Driver had been drinking 0.093 0.234 0.232 .687DCELL Driver in cell phone use 0.178 0.233 0.205 .384DRACE Black 0.042 0.06 0.062 .491

White −0.06 0.046 0.046 .189Hispanic −0.097 0.062 0.063 .127Other −0.075 0.061 0.062 .23

Environment Year 2003 0.048 0.053 0.052 .3622004 0.068 0.056 0.054 .2092005 0.047 0.055 0.054 .392006 0.1 0.077 0.081 .222007 0.051 0.055 0.056 .364

Time 6:00 a.m. to noon −0.258*** 0.087 0.094 .006Noon to 6:00 p.m. −0.270*** 0.083 0.091 .0036:00 p.m. to midnight −0.254*** 0.068 0.076 .001

Weekend Saturday and Sunday 0.083** 0.039 0.039 .035Intersect Intersection crash −0.051 0.037 0.037 .163Clear Clear weather 0.072 0.057 0.056 .193Raining Raining 0.173** 0.071 0.071 .014Crosswalk Crash when pedestrian

crossing crosswalk0.012 0.052 0.053 .825

NCROSSWALK Crash when pedestrian crossing noncrosswalk

0.103* 0.056 0.057 .072

Lighting Dusk–dawn 0.012 0.087 0.079 .878Dark–light 0.083 0.058 0.059 .159Dark–no light 0.195 0.146 0.185 .292Unknown 0.374* 0.218 0.194 .054

Crash PCF Unsafe speed −0.739*** 0.275 0.284 .009Improper passing −0.668** 0.298 0.309 .031Improper turning −0.812** 0.311 0.334 .015Automobile right-of-way −0.401 0.303 0.314 .202



TABLE 2 (continued) Ordered Probit Estimates for Pedestrian Injuries

Characteristic Variable Description Coefficient SE Robust SE p-Value

Note: SE = standard error.Severity Coefficient SEΨ1 (between PDO and Injury1) −2.111 0.7281Ψ2 (between Injury1 and Injury2) 0.181 0.7277Ψ3 (between Injury2 and Injury3) 1.301 0.7277Ψ4 (between Injury3 and fatal) 2.108 0.7283

*significance level = 10%; **significance level = 5%; ***significance level = 1%.

Pedestrian right-of-way −0.705** 0.272 0.28 .012Pedestrian violation −0.809*** 0.281 0.291 .006Traffic signals and signs −0.552* 0.278 0.29 .057Other hazardous violation −0.875*** 0.296 0.307 .004Unsafe starting or backing −0.784*** 0.284 0.288 .007Other −0.760*** 0.285 0.293 .009Unknown −0.728** 0.281 0.289 .012

HITRUN Hit-and-run crash 0.039 0.079 0.088 .654DMOVE Making right turn −0.242*** 0.062 0.058 0

Making left turn −0.167*** 0.05 0.049 .001Backing −0.312*** 0.099 0.089 0Other −0.161** 0.062 0.067 .017

DVEHTYPE Motorcycle–scooter 0.3 0.116 0.122 .014Pickup 0.145** 0.06 0.059 .014Truck 0.444*** 0.141 0.169 .009Bus 0.299*** 0.085 0.093 .001Bicycle 0.284*** 0.11 0.096 .003Other −0.079* 0.043 0.043 .069

Parties 1 0.856 0.562 0.861 .322 0.963 0.566 0.866 .2663 1.238 0.584 0.876 .1584 1.482* 0.609 0.9 .15 1.866* 0.683 0.96 .0526 1.311 0.946 1.469 .3727 1.983** 1.044 0.941 .035

standard errors were comparable with those of the usual standard errors; this finding signifies that the parameters were properly esti-mated. To draw a valid interpretation, robust standard errors were used to calculate the p-value for each estimated parameter; p-values are presented in the last column of Table 2. The interpretation in the following section focuses on the variables with higher statistical significance.

Marginal Effects of Estimates

The marginal effects of each parameter are shown in Table 3. The marginal effects are the substantive effects of the explanatory vari-ables on the changes in the probability of having a certain level of pedestrian injury in a traffic crash. These are a relative measure to a reference case, which was the model with all dummy variables equal to zero (the reference case is presented in italics in Table 1).

In the assessment of the marginal effects, variables with higher statistical significance (identified in the previous section) were inter-preted because these variables were more likely to have statistically significant effects on pedestrian injury severity.

Pedestrian Characteristics

Compared with young pedestrians (younger than 15), older pedes-trians (older than 65) tend to have increased injury levels, but pedes-trians between the ages of 15 and 65 were more likely to have diminished injury levels. A higher probability of severe injury and

fatality in older and younger age groups can be explained by the fact that pedestrians in those age groups were more vulnerable to the impacts, less responsive to the risks, and other factors. As expected, pedestrians who had been drinking were more likely to have higher injury levels, though this factor was not the major cause of the crash. Alcohol is well known to incapacitate people’s physical abilities (slower reaction time, blurred vision, inaccurate motion tracking, lack of concentration, etc.) and as such, alcohol consumption by a pedestrian may increase the risk of severe injury. In addition, pedes-trians engaged in cell phone use appeared to have increased injury severity possibly because of lack of concentration.

Driver Characteristics

It is noticeable that none of the coefficients among driver character-istics was statistically significant. Although various categorizations were examined, none of the categorizations result in a significant outcome. In pedestrian crashes, drivers are not directly exposed to the crash, and so driver characteristics are less influential on the level of pedestrian injury.

Environmental Characteristics

Compared with crashes that occurred from midnight to 6:00 a.m., pedestrian crashes that occurred in other time periods appeared to present a diminished risk of severe injury and fatality. The short visible range at night, faster vehicle speeds under light traffic condi-


TABLE 3 Marginal Effects

Injury

Characteristic Variable Description PDOComplaint of Pain

Other Visible Severe Fatality

Pedestrian PFAULT Pedestrian at fault −0.005 −0.05 0.029 0.019 0.007PSEX Male 0.001 0.005 −0.003 −0.002 −0.001

Unknown 0.008 0.058 −0.038 −0.021 −0.007PAGE Older than 65 −0.007 −0.074 0.04 0.029 0.011

Between 15 and 65 0.007 0.07 −0.039 −0.027 −0.01Unknown −0.01 −0.131 0.062 0.054 0.024

PUI Pedestrian had been drinking −0.011 −0.148 0.068 0.062 0.028PCELL Pedestrian in cell phone use −0.011 −0.156 0.069 0.067 0.031PRACE Black 0.017 0.114 −0.078 −0.04 −0.013

White −0.002 −0.021 0.013 0.008 0.003Hispanic 0.001 0.009 −0.005 −0.003 −0.001Other 0.003 0.024 −0.015 −0.009 −0.003

Driver DFAULT Driver at fault 0 0.004 −0.002 −0.001 0DSEX Male 0 −0.001 0.001 0 0

Unknown −0.01 −0.127 0.062 0.052 0.022DAGE Older than 65 −0.007 −0.085 0.045 0.034 0.014

Between 15 and 65 −0.007 −0.058 0.037 0.021 0.007Unknown −0.001 −0.013 0.008 0.005 0.002

DUI Driver had been drinking −0.003 −0.034 0.019 0.013 0.005DCELL Driver in cell phone use −0.006 −0.065 0.035 0.026 0.01DRACE Black −0.002 −0.015 0.009 0.006 0.002

White 0.002 0.021 −0.013 −0.008 −0.003Hispanic 0.004 0.034 −0.021 −0.013 −0.004Other 0.003 0.026 −0.016 −0.01 −0.003

Environment Year 2003 −0.002 −0.017 0.01 0.006 0.0022004 −0.003 −0.024 0.014 0.009 0.0032005 −0.002 −0.017 0.01 0.006 0.0022006 −0.004 −0.036 0.021 0.014 0.0052007 −0.002 −0.018 0.011 0.007 0.003

Time 6:00 a.m. to noon 0.012 0.089 −0.057 −0.032 −0.0116:00 p.m. to midnight 0.011 0.088 −0.056 −0.032 −0.011Noon to 6:00 p.m. 0.011 0.095 −0.058 −0.035 −0.012

Weekend Saturday and Sunday −0.003 −0.03 0.017 0.011 0.004Intersect Intersection crash 0.002 0.018 −0.011 −0.007 −0.002Clear Clear weather −0.003 −0.026 0.016 0.01 0.003Raining Raining −0.006 −0.063 0.035 0.025 0.01Crosswalk Crash when pedestrian crossing

crosswalk0 −0.004 0.002 0.002 0.001

NCROSSWALK Crash when pedestrian crossing noncrosswalk

−0.004 −0.037 0.021 0.014 0.005

Lighting Dusk–dawn 0 −0.004 0.003 0.002 0.001Dark–light −0.003 −0.03 0.017 0.011 0.004Dark–no light −0.006 −0.071 0.038 0.028 0.011Unknown −0.01 −0.138 0.064 0.058 0.026

Crash PCF Unsafe speed 0.058 0.203 −0.173 −0.069 −0.019Improper passing 0.051 0.185 −0.158 −0.062 −0.017Improper turning 0.072 0.204 −0.189 −0.069 −0.018Automobile right-of-way 0.024 0.127 −0.094 −0.044 −0.013Pedestrian right-of-way 0.035 0.233 −0.153 −0.086 −0.03Pedestrian violation 0.045 0.258 −0.178 −0.094 −0.032Traffic signals and signs 0.037 0.166 −0.13 −0.056 −0.016Other hazardous violation 0.082 0.211 −0.202 −0.072 −0.019Unsafe starting or backing 0.065 0.208 −0.183 −0.07 −0.019Others 0.063 0.201 −0.178 −0.068 −0.018Unknown 0.056 0.201 −0.171 −0.068 −0.019

HITRUN Hit-and-run crash −0.001 −0.014 0.008 0.005 0.002DMOVE Making right turn 0.012 0.082 −0.055 −0.029 −0.01

Making left turn 0.007 0.058 −0.037 −0.021 −0.007Backing 0.016 0.103 −0.072 −0.036 −0.011Other 0.007 0.056 −0.035 −0.021 −0.007

DVEHTYPE Motorcycle–scooter −0.009 −0.111 0.054 0.045 0.019Pickup −0.005 −0.053 0.029 0.021 0.008Truck −0.011 −0.164 0.071 0.07 0.033Bus −0.009 −0.11 0.055 0.045 0.019Bicycle −0.008 −0.105 0.052 0.043 0.018Other 0.003 0.028 −0.017 −0.01 −0.004



ConCLuSionS

Two major aspects of pedestrian safety are evaluated by identifying pedestrian crash hot spots and assessing risk factors for injury severity. The former aims to detect locations where more crashes are concen-trated, and the latter evaluates risk factors that tend to increase the level of injury severity when pedestrians are exposed to traffic crashes. These two aspects are fundamental for enhancing pedestrian safety because they provide information on locations and factors for further investigation. In this study, pedestrian crashes that occurred in the city of San Francisco from 2002 to 2007 were obtained from the SWITRS database and used for these analyses.

For the first analysis, a spatial kernel density estimation method in GIS was used to measure the concentrated density of pedestrian crashes. The measured density was then projected onto a map to display geographically how pedestrian crashes are clustered. The patterns show that pedestrian crashes are clustered in the vicinity of the CBD and the clustering patterns are somewhat comparable across different injury levels. However, when pedestrian volume (as an exposure variable) is reflected, locations of pedestrian crash hot spots exhibit opposite patterns; for example, hot spots in the fringe of the city appeared to be more pronounced. These outcomes suggest that hot spots identified by two measures—with and with-out pedestrian volume—are worth investigating to improve pedes-trian safety. The method used in this study is readily applicable to any region where information on location of crash occurrences is available.

The second analysis evaluated the impact of various risk factors on the severity of pedestrian injury involved in traffic crashes. Vari-ables in the crash database were categorized into four groups of characteristics—pedestrian, driver, environment, and crash—and entered into an ordered probit model as explanatory variables. The parameters and marginal effects of significant variables were inter-preted to examine the influence of characteristics on pedestrian injury severity. Characteristics that increased pedestrian injury severity were alcohol involvement (even if it was not a primary crash factor), cell phone use, and age (younger than 15 years or older than 65 years). Environmental characteristics such as night-time, weekends, and rainy weather were associated with an increased probability of severe injury and fatality. Among crash characteris-tics, primary crash factors, vehicle movement, and type of vehicle were shown to be significant. The primary crash factor that resulted in the most severe injuries appeared to be the presence of alcohol. Among all vehicle movements resulting in striking a pedestrian, the probability of severe injury and fatality was increased when pedestrians were hit by a vehicle proceeding straight. Compared with passenger vehicles, larger vehicles such as pickups, trucks, and

tions, and other factors may contribute to the propensity for severe injury and fatality at night. Pedestrian crashes during weekends were associated with an increased risk of severe injury and fatality, probably because of the difference in travel patterns between week-ends and weekdays (since travel during weekdays is more likely to be work-related and along familiar routes than weekend travel).

As reported in previous research, precipitation is also shown as a factor for higher risk of severe injury and fatality among pedes-trians. When and where a pedestrian crash occurred (intersection, crosswalk, or not at crosswalk) were not statistically significant or were marginally significant. In previous research [e.g., work by Zegeer et al. (43)], crosswalks appeared to be associated with a higher risk of pedestrian crashes (not level of injury, but frequency) and this finding has drawn great attention to crosswalk design. However, since the model in the current study estimated the level of pedestrian injury given that the crash has already occurred, it can-not be determined, on the basis of the current model, whether these variables are contributing to the level of pedestrian injury.

Crash Characteristics

In crash characteristics, coefficients of the primary crash factor, movement of vehicle, and vehicle type were statistically significant. Since the primary crash factor was identified and recorded in the database on the basis of the police officer’s direct observation of the crash scene, it delivers information (in the form of categorical data) about the qualitative measures of primary causes associated with the crash. Compared with crashes due to the influence of alcohol, other primary crash factors were associated with a lower probability of severe injury and fatality. In other words, crashes caused by the influ-ence of alcohol are most likely to result in the higher level of injury. Coefficients in vehicle movement when the crash occurred indicate that proceeding straight was found to have a higher probability of severe injury and fatality.

Pedestrians struck by larger vehicles such as pickups, trucks, and buses were more likely to be severely injured or killed. This find-ing may be explained primarily by the heavier weights of larger vehicles. Unexpectedly, it appeared that bicyclists experienced higher levels of injury. However, this result should be cautiously interpreted because samples of bicycle-involved crashes might be overrepresenting the population because of the small sample size, and also the distribution of bicycle-involved injury risk might dif-fer from that of other motorized vehicle–involved injuries. Future studies using a larger sample size and focusing on groups of crashes between pedestrians and nonmotorized vehicles could shed further light on this issue.

TABLE 3 (continued) Marginal Effects

Injury

Characteristic Variable Description PDOComplaint of Pain

Other Visible Severe Fatality

Parties 1 −0.073 −0.222 0.198 0.076 0.0212 −0.016 −0.339 0.085 0.162 0.1083 −0.015 −0.405 0.034 0.203 0.1834 −0.016 −0.45 −0.019 0.224 0.2615 −0.015 −0.496 −0.113 0.222 0.4036 −0.015 −0.418 0.015 0.211 0.2077 −0.015 −0.505 −0.142 0.212 0.45


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aCKnowLeDgMentS

The work described in this paper was supported by the Khalifa University of Science Technology and Research (Abu Dhabi, United Arab Emirates) and the Korea Advanced Institute of Science and Technology. The authors are grateful to David Ragland and John Big-ham of the Safe Transportation Research and Education Center at the University of California, Berkeley, for their support and constructive comments on the paper.

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