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Accident Analysis and Prevention 40 (2008) 894–904

Modeling violation of Hawaii’s crosswalk law

Karl Kim ∗, I. Made Brunner, Eric YamashitaDepartment of Urban and Regional Planning, University of Hawaii, Saunders Hall, Room 107,

2424 Maile Way, Honolulu, HI 96822, United States

Received 3 June 2007; received in revised form 12 October 2007; accepted 15 October 2007

bstract

In 2005, Hawaii strengthened its pedestrian crosswalk laws. Previously, motorists had the option yielding or slowing down at a crosswalk andad to stop “only when necessary.” The new law requires drivers to stop and yield to pedestrians at crosswalks. The purpose of this study is toxamine patterns of violation and compliance with the law among both pedestrians and drivers. Observational studies at crosswalks were done inhe Spring of 2006. In addition to reporting on overall rates of compliance, the characteristics of both pedestrians and drivers who either violate oromply with the law are described. While pedestrian compliance is higher than that of drivers, there are interesting differences to report in terms ofge, gender, type of intersection, land use, and other factors. In addition to the results of a descriptive analysis, logistic regression models predictinghe likelihood of violation of the crosswalk laws by either pedestrians or drivers, as a function of their characteristics, the type of intersection,

nd other factors are presented. The study finds that drivers tend to commit proportionately more violations than pedestrians, and violations areommitted by a broader range of drivers than pedestrians. These results suggest that education and enforcement should be directed towards drivers.uture directions for research and enhancing the safety of pedestrians are described in a concluding section.2007 Elsevier Ltd. All rights reserved.

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eywords: Pedestrian safety; Observational studies; Logistic regression; Hawa

. Introduction

Pedestrian safety has become one of the top priorities for thetate of Hawaii. In 2005, the Hawaii State Legislature passed atricter law and penalties for violation of crosswalk laws. Theew law, Hawaii Revised Statute (HRS) §291C-72, states thatthe driver of a vehicle shall stop and yield the right of way,o a pedestrian crossing the roadway within a crosswalk whenhe pedestrian is upon the half of the roadway upon which theehicle is traveling, or when the pedestrian is approaching solosely from the opposite half of the roadway as to be in danger.”n both cases, drivers are not to continue until the pedestrianas left the roadway. Previously, instead of requiring motoristso stop and yield to pedestrian on crosswalk, drivers had theiscretion to “yield the right of way, slowing down or stoppingf need be to so yield.” The law set a $97 fine for drivers who

o not stop for pedestrians and a $70 fine for pedestrians whollegally cross a street (jaywalk) or cross against the pedestrianignal.

∗ Corresponding author. Tel.: +1 808 956 7381; fax: +1 808 956 6870.E-mail address: karlk@hawaii.edu (K. Kim).

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001-4575/$ – see front matter © 2007 Elsevier Ltd. All rights reserved.oi:10.1016/j.aap.2007.10.004

The purpose of this study is to examine the characteristicsf drivers and pedestrians who violate Hawaii’s crosswalk laws.bservations of drivers and pedestrians were conducted to deter-ine compliance with the law and to understand how personal

haracteristics, vehicle type, intersection type, vehicle move-ents, land use, and other factors affect behavior at crosswalks

Kim et al., 1998; Kim and Yamashita, 2002). In addition toescriptive statistics, logistic regression models are used to mea-ure the relative contribution of driver, pedestrian, vehicle, andnvironmental factors to safety at crosswalks. Following discus-ion of findings, the implications for improving pedestrian safetyn Hawaii are described. Future research needs are provided inconcluding section.

. Method and approach

This research is based on the idea that the behavior of driversnd pedestrians can be directly observed, recorded, and ana-yzed to determine the causes of pedestrian accidents. There

re challenges with designing a study to capture the behaviornd characteristics of pedestrians and drivers. An observationrotocol was developed and tested in terms of its functionalitynd reliability. The process involved translating the new cross-

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K. Kim et al. / Accident Analysi

alk law into a series of diagrams showing types of violationsnd developing data recording instruments for violations andersonal, vehicle, roadway, and site characteristics. A traininganual was developed to ensure consistency among observers

nd data recorders. Techniques were pre-tested and refinedefore submitting them to outside review. The method andnstruments were reviewed by traffic safety and law enforce-

ent professionals. Based on input and comments received, thebservation methods and data recording forms were modified.ome personal characteristics, such as gender, are more reli-bly observed than others, especially for pedestrians comparedo drivers. Some contributing factors associated with pedes-rian and motorist risk of accident (such as alcohol or drug use)ere not directly observable. Attitudes towards risk taking andersonality characteristics were only observable to the extenthat these characteristics were manifested in illegal actions.he limitations of this study are described in the discussionection.

Another issue involved the selection of sites. Using crashatabases for 1985–1995 and 2002–2003, the top locations foredestrian accidents in the state were identified. Using datarom different time periods provided a historical context aso where the problematic crosswalks were located, as well asroviding a means to determine the trend of accidents at theseocations. The location of crosswalks at intersections were deter-

ined by using historical crash data, assuming that locationsf pedestrian-related crashes at intersections were located atrosswalks. Since the crash data contained information regard-ng the types of intersections (i.e. signalized and unsignalized),he types of crosswalks at intersections could also be deter-

ined. Fifteen observation sites were allocated at signalizedntersections, and 15 at unsignalized intersections. The loca-ion of crosswalks at midblock locations were determined bysing the historical crash data also, assuming that locations ofedestrian-related crashes at non-intersection sites were locatedt midblock crosswalks. Since the crash data from the Honoluluolice Department contained information regarding the locationf crashes at approximate addresses, the location of crosswalkt midblock were also determined. Nonetheless, site visits weretill needed to determine the type of midblock crosswalk (signal-zed and unsignalized). Ten observation sites were then allocatedt unsignalized midblock crosswalks, and five other at signalizedidblock crosswalks. Other sites were based on traffic volumes

highway and freeway locations were excluded) and residen-ial population density. The sites were all ranked according toedestrian accidents, vehicle volumes, and residential popula-ion densities. Other features of each site such as the land use,roximity to parks and schools, and environmental factors wereoted and were considered as activity generators, hence theyere used as a proxy to assist in determining potential pedes-

rian volumes. Observation sites were also categorized by theype of crossing: (1) signalized intersection, (2) unsignalizedntersection, (3) signalized midblock, and (4) unsignalized mid-

lock.

Data were collected at 45 sites in the City and County ofonolulu, which covers the entire island of Oahu. Honolulu is

he most populated of the four counties in the State of Hawaii.

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Prevention 40 (2008) 894–904 895

n addition to having more than 70% of the state’s population,7.4% of the state’s pedestrian fatalities occurred on this island.t is the most urbanized as well as having a mix of land uses andctivity generators. This research builds upon other on-goingesearch conducted at the University of Hawaii using obser-ational studies (Kim and Yamashita, 2003), spatial analysisf pedestrian accidents (Kim and Yamashita, 2007), and trafficafety modeling of the interactions between land use, activitiesnd accidents (Kim et al., 2006). Besides the historic crash datao identify the worst locations for pedestrian accidents and otheractors, the authors used spatial methods of cluster analysis todentify statistically significant regions or areas where crashesave occurred (Kim and Yamashita, 2007; MacQueen, 1967).ased on this information, both the general regions and specific

ocations for pedestrian accidents were identified. Graduate stu-ents collected data for one hour at each site. Pedestrian andehicle volumes were measured. One person observed behaviorshile the other recorded data on pre-prepared forms. Observers

ecorded pedestrians crossing on crosswalk(s) and jaywalkersithin 200 ft of the crosswalk(s). They recorded information onehicles only when there were pedestrians. Pedestrian charac-eristics (age group and gender) and vehicle descriptions (driverge group and gender, type of vehicle, and vehicle movement)ere also recorded. Observers also recorded pedestrian cross-

ng behavior. Possible violations included crossing against theedestrian signal, entering the crosswalk when the signal waslinking, and crossing while not in the crosswalk. Possible viola-ions by a driver include not stopping for a pedestrian, stopping,ut continuing to proceed through the crosswalk before theedestrian has left the half of the roadway upon which the vehicleas traveling, and/or blocking the crosswalk. When pedestri-

ns or drivers committed multiple violations, observers recordednly the first violation.

. Findings

The findings are organized into three sections. First, the fre-uencies showing rates of violation for categories of drivers,edestrians, vehicles, and intersections are reported. A bivariatenalysis was conducted, comparing the characteristics of driversnd pedestrians in violation of the crosswalk law to those whoomplied with the law. To assess the effects of factors on the like-ihood of violation, logistic regression models were developednd tested. Using the logged odds (logit) of being a violator orot, separate models for drivers and pedestrians were estimatedor types of intersections. Logistic regression and categoricalata analysis are widely used techniques for modeling variousehavioral phenomena (Fienberg, 1984), including traffic safetyKim et al., 1995; Kim and Li, 1996).

.1. Violations of the crosswalk law

Who violates the crosswalk law in Hawaii? Out of 6181

edestrians and 1473 drivers, there were a total of 1807 pedestri-ns and 839 drivers who were observed to be in violation of theaw. Based on the observations, a higher proportion of pedestri-ns, 70.8%, comply with the law than violate it. The observations

896 K. Kim et al. / Accident Analysis and Prevention 40 (2008) 894–904

Table 1Characteristics of pedestrian violations

Variables Total number of pedestrianscrossing in violation of thelaw

Number of pedestrianscrossing when signal isblinking red

Number of pedestrianscrossing when signal issteady red

Number of pedestrianscrossing not oncrosswalk

N % of total n % of N n % of N n % of N

Observed pedestrian 1807 100 307 17.0 387 21.4 1108 61.3

GenderMale 948 52.5 146 15.4 223 23.5 577 60.9Female 859 47.5 161 18.7 164 19.1 531 61.8

Age groupChild (less than 12 years) 51 2.8 17 33.3 2 3.9 32 62.7Teen (12–17 years) 58 3.2 17 29.3 17 29.3 23 39.7Young adult (17–25 years) 273 15.1 63 23.1 76 27.8 134 49.1Adult (25–60 years) 1097 60.7 161 14.7 237 21.6 695 63.4Senior (60 years and above) 328 18.2 49 14.9 55 16.8 224 68.3

Type of crosswalkSignalized intersection 760 42.1 178 23.4 284 37.4 296 38.9Unsignalized intersection 407 22.5 0 0.0 0 0.0 404 99.3Signalized midblock 401 22.2 129 32.2 103 25.7 169 42.1Unsignalized midblock 239 13.2 0 0.0 0 0.0 239 100.0

Land useMixed commercial residential 308 17.0 42 13.6 69 22.4 196 63.6Commercial 464 25.7 94 20.3 103 22.2 265 57.1Hotel 680 37.6 84 12.4 145 21.3 449 66.0Office 264 14.6 75 28.4 47 17.8 142 53.8Residential 91 5.0 12 13.2 23 25.3 56 61.5

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lso revealed that more drivers, 57%, violate the new law thanomply (43%) with it. For pedestrians, the most common type ofiolation, involving 1108 pedestrians, (Table 1) involved cross-ng while not in the crosswalk or “jaywalking.” The next mostommon type of pedestrian violation involves walking against aed light (387), followed by entering a crosswalk with a blink-ng light (307). For drivers, (Table 2) the most common typef violation involves not stopping while pedestrians are on therosswalk (444), followed by stopping but continuing to pro-eed before the pedestrian has left the crosswalk (323). Anotheriolation involved 69 drivers who were blocking the crosswalkrea.

.2. Differences between the genders and ages ofedestrians and drivers

Table 1 shows that proportionately females were more likelyo “jaywalk” or enter a crosswalk against a blinking light than

ales. However, there was a higher percentage of males crossinggainst a red light than females. Among drivers, the percent-ge of males in violation of the law was higher than females.pproximately, 52.8% of male drivers compared to 53.1% of

emale drivers did not stop for pedestrians on the crosswalk;8.6% of male drivers compared to 38.4% of female drivers

ho stopped but continued to proceed before pedestrians left

he crosswalk; and 8.0% of male drivers compared to 8.6% ofemale drivers who were observed to be blocking the crosswalkrea.

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Tables 1 and 2 also show the violations by age group for bothedestrians and drivers. Note that there are more age categoriesor pedestrians than for drivers. Adult pedestrians (25–60 yearsld) were observed to violate the crosswalk law more than anyther age group. Table 1 also shows a relatively high incidencef “jaywalking” among seniors (60 years and above). Similarly,dult drivers (25–60 years old) as opposed to either young drivers17–25 years old) or senior drivers (60 years and above) werebserved to violate the crosswalk law most often.

.3. Vehicle types and movements

Table 2 shows the characteristics of driver that violated therosswalk law. The most common vehicle types to violate therosswalk law were cars, followed by SUVs, pickups, and vans.otorcycles comprised only a small percentage of the observed

ehicles. The most common vehicular movement in violationf the crosswalk law involved forward movements through therosswalk. The terminology “forward moving vehicles” is onehat is used on the accident report form used by Hawaii’s Policeepartment, which is defined as those vehicles that were mov-

ng forward through the crosswalk at the time of the violation.here were 284 forward moving vehicles observed not stopping

or pedestrians on the crosswalk, 151 forward moving vehicles

hich stopped but continued to proceed before the pedestrian left

he crosswalk area, and 47 forward moving vehicles observedlocking the crosswalk. There were 92 right-turning vehicleshat stopped but then continued to proceed before the pedestrian

K. Kim et al. / Accident Analysis and Prevention 40 (2008) 894–904 897

Table 2Characteristics of driver violations

Variables Total number ofdrivers violating thelaws

Number of driversnot stopping forpedestrian oncrosswalk

Number of drivers stopping forpedestrian but continue to movebefore pedestrian leaving thecrosswalk

Number of driversblocking thecrosswalk

N % of total n % of N n % of N n % of N

Observed drivers 839 100 444 52.9 323 38.5 69 8.2

GenderMale 547 65.2 289 52.8 211 38.6 44 8.0Female 292 34.8 155 53.1 112 38.4 25 8.6

Age groupYoung adult (17–25 years) 53 6.3 27 50.9 26 49.1 0 0.0Adult (25–60 years) 712 84.9 381 53.5 267 37.5 62 8.7Senior (60 years and above) 74 8.8 36 48.6 30 40.5 7 9.5

Type of vehicleCar 466 55.5 234 50.2 193 41.4 36 7.7SUV 148 17.6 93 62.8 46 31.1 9 6.1Truck 105 12.5 57 54.3 37 35.2 11 10.5Van 100 11.9 48 48.0 40 40.0 12 12.0Motorcycle 20 2.4 12 60.0 7 35.0 1 5.0

Type of originTurning right from behind thecrosswalk

63 7.5 24 38.1 22 34.9 17 27.0

Turning right approaching thecrosswalk

175 20.9 80 45.7 92 52.6 3 1.7

Turning left approaching thecrosswalk

116 13.8 56 48.3 58 50.0 2 1.7

Moving forward 485 57.8 284 58.6 151 31.1 47 9.7

Type of crosswalkSignalized intersection 254 30.3 120 47.2 110 43.3 23 9.1Unsignalized intersection 301 35.9 136 45.2 140 46.5 25 8.3Signalized midblock 25 3.0 18 72.0 2 8.0 3 12.0Unsignalized midblock 259 30.9 170 65.6 71 27.4 18 6.9

Land useMixed commercial residential 282 33.6 205 72.7 64 22.7 13 4.6Commercial 269 32.1 84 31.2 144 53.5 40 14.9

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Hotel 124 14.8 60 4Office 107 12.8 59 5Residential 57 6.8 36 6

eft the crosswalk and 80 right-turning vehicles that did not stopor pedestrians on the crosswalk.

.4. Type of crosswalk

Table 1 also shows pedestrian violations by the type of cross-alk. There were 404 pedestrians jaywalking at unsignalized

ntersections, 296 jaywalkers at signalized intersections, 239 atnsignalized midblocks, and 169 at signalized midblock loca-ions. At signalized intersections, there were 38.9% jaywalkersompared to 23.4% crossing against the red light and 37.4%ntering the crosswalk against the blinking signal. At signalizedidblocks, the percentage of jaywalkers was 42.1% compared

o 32.2% crossing against the red light and 25.7% entering the

rosswalk against the blinking signal.

The pattern of driver violations by type of crosswalk is alsohown in Table 2. Overall the fewest driver violations occurredt signalized midblocks. The most frequent type of violation,

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rivers not stopping for pedestrians on the crosswalk, occurredt unsignalized midblock crossings. Another significant problemnvolved drivers stopping, but continuing to proceed before theedestrian has left the crosswalk at unsignalized intersections.t unsignalized intersections, there was a problem of drivers not

topping at all for pedestrians on the crosswalk. Table 2 revealshat the problem of either not stopping (120) or stopping thenroceeding before the pedestrian has left the crosswalk (110)as also a common occurrence at signalized intersections.

.5. Land use

Jaywalking was common across all types of land use, espe-ially in hotel (resort) districts where there were 449 pedestrians

bserved violating this aspect of the law. There were also 265nstances of jaywalking in commercial areas, and 196 cases inommercial districts. In office districts, there were 142 cases ofaywalking, while in residential areas there were only 56 cases.

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here were also 145 cases of pedestrians crossing against a redight in the resort areas and 84 cases of pedestrians entering

crosswalk against the blinking lights in these areas. Signifi-ant counts of pedestrian violations occurred in the commercialnd mixed use areas. In general, single family residential areasad lower instances of pedestrian violation of the crosswalkaw.

Table 2 also contains data on driver violations of the cross-alk law by land use. Here the most common violation involvesrivers not stopping for pedestrians on the crosswalk in mixedse areas. The second most common problem involves driverstopping but then proceeding before pedestrians have left therosswalk area. Interestingly, while the problem of pedestrianiolations was greatest in the hotel and resort districts, the driveriolations tend to be most frequent in mixed use and commercialistricts.

.6. Differences between violators and non-violators

In this section, the differences between violators and non-iolators of the crosswalk law in Hawaii are analyzed. Usingdifference of proportions test at the 95% level of confidence,

he differences between pedestrian violators and non-violatorsTable 3) and driver violators and non-violators (Table 4) ofawaii’s crosswalk law are presented. The Z-test for propor-

ions determines the statistical significance of the differenceetween violators and non-violators within one variable (i.e.,ales, signalized intersections, etc.).Table 3 shows that there are statistically significant differ-

nces between violators and non-violators in terms of gender,ge, crosswalk type, time of day, weather, land use, proxim-ty to parks, and residential density. Among male pedestrians, atatistically significant proportion (52.5%) are violators of theaw, compared to male non-violators (46.3%) of the law. Whilemong female pedestrians, the opposite is true, a larger pro-ortion are non-violators of the law (53.8%), and 47.5% areiolators of the law. While for most age groups, there are notatistically significant differences between violators and non-iolators; children are less likely to be violators. With regardo intersection type, pedestrians are more likely to be viola-ors at signalized intersections. There are, notably, statisticallyignificant differences between violators and non-violators atnsignalized intersections and signalized midblock locations. Aigher proportion of violations occur during early morning andidday times than other times of the day. Compliance tends to

e greatest during the afternoon and early evening. Interestingly,loudy or rainy days are statistically associated with higher ratesf pedestrian violation. For the most part, land use is not associ-ted with statistically significant differences between violationnd non-violation of the crosswalk law, with the exception ofotel areas. Pedestrians in hotel zones tend to have higher ratesf violation while pedestrians in office areas tend to have higherates of compliance. Proximity to parks also produces a statis-

ically significant effect in terms of pedestrian violations, yetroximity to schools has no statistically significant effect. Also,he proportion of violators was greater in low-density residentialreas than other areas.

ssct

Prevention 40 (2008) 894–904

Table 4 provides information on the significant differencesetween drivers who violate and those who don’t violate therosswalk law. Among drivers, there are fewer statistically sig-ificant differences between violators and non-violators thanor pedestrians. Gender, age (young adult and adult), most timesf the day, vehicle type, and proximity to parks and schoolsere not statistically significant factors explaining the differenceetween violators and non-violators. Senior drivers, however,ere statistically less likely to be classified as violators thanther age groups. Drivers at signalized intersections were alsoore likely to be in compliance, while drivers at midblock loca-

ions were more likely to be classified as violators than at otherocations. Time of day, had a different effect than for pedestri-ns. Drivers were more likely to violate the law during afternooneak periods. Drivers were more likely to comply with the lawn sunny days compared to cloudy days. Vehicle type was not aignificant factor, with the exception of motorcycles which werebserved to violate the crosswalk law proportionately more thanther vehicle types. In terms of vehicle movements, forwardovements were statistically associated with driver violations.here were also statistically significant differences with hotelnd office areas, as were high-density residential areas whichended to produce more driver violations than in other areas.

.7. Logistic regression model results

To understand the relationships between pedestrian and driverharacteristics, and other factors associated with violations ofhe crosswalk law, logistic regression models were developednd tested. The log odds of either the driver or the pedes-rian violating the crosswalk law was modeled as a functionf various personal characteristics (gender, age), intersectionharacteristics, and other factors. In general, the driver modelserformed better than the pedestrian models. The driver modelsill be presented first, followed by the pedestrian models. In

ddition to the overall model (for both drivers and pedestrians),eparate models for the different types of crosswalks will beescribed.

An odds ratio (OR) of greater than one signifies a positiveffect. The larger the value, the greater the effect. On the otherand, an odds ratio close to 1 implies a weak effect, but still pos-tive. Odds ratios of less than one suggest a negative relationshipetween the attribute or background characteristic of the driverr pedestrian and the likelihood of a violation. Only statisticallyignificant effects and models at the .05 confidence level or 95%onfidence interval are reported.

.8. Driver violation models

The results for the overall driver violators, at signalizedntersections driver violators, and at unsignalized intersectionriver violator models are contained in Table 5. The signal-zed and unsignalized midblock driver models are not shown

ince they had insignificant results. The overall model shows thatenior drivers have lower odds of committing violations of therosswalk law. None of the other driver characteristics were sta-istically significant, nor was vehicle type. Vehicle movements,

K. Kim et al. / Accident Analysis and Prevention 40 (2008) 894–904 899

Table 3Analysis of pedestrian violations

Variables Pedestrians violatingthe law

Percentage (%) Pedestrians in compliancewith the law

Percentage (%) Z-test Value

Overall observations 1807 100.00 4374 100.00

GenderMale 948 52.46 2023 46.25 4.4461 <0.001Female 859 47.54 2351 53.75 −4.4461 <0.001

Age groupChild (less than 12 years) 51 2.82 171 3.91 −2.0891 0.0366Teen (12–17 years) 58 3.21 171 3.91 −1.3247 InsignificantYoung adult (17–25 years) 273 15.11 656 15.00 0.1103 InsignificantAdult (25–60 years) 1097 60.71 2552 58.34 1.7187 InsignificantSenior (60 years and above) 328 18.15 824 18.84 −0.6308 Insignificant

Type of crosswalkSignalized intersection 760 42.06 1780 40.70 0.9911 InsignificantUnsignalized intersection 407 22.52 1284 29.36 −5.4801 <0.001Signalized midblock 401 22.19 651 14.88 6.9539 <0.001Unsignalized midblock 239 13.23 659 15.07 −1.8671 Insignificant

Time of day7 a.m.–9 a.m. 62 3.43 61 1.39 5.2145 <0.0019 a.m.–11 a.m. 206 11.40 554 12.67 −1.3782 Insignificant11 a.m.–1 p.m. 1030 57.00 1904 43.53 9.6464 <0.0011 p.m.–3 p.m. 381 21.08 1185 27.09 −4.9390 <0.0013 p.m.–5 p.m. 117 6.47 658 15.04 −9.2527 <0.0015 p.m.–7 p.m. 11 0.61 12 0.27 1.9639 0.0500

WeatherSunny 592 32.76 1878 42.94 −7.4276 <0.001Cloudy 1167 64.58 2472 56.52 5.8620 <0.001Rainy 48 2.66 24 0.55 7.0241 <0.001

Land useMixed commercial residential 308 17.04 821 18.77 −1.5966 InsignificantCommercial 464 25.68 1228 28.07 −1.9224 InsignificantHotel 680 37.63 1360 31.09 4.9724 <0.001Office 264 14.61 812 18.56 −3.7293 <0.001Residential 91 5.04 153 3.50 2.8244 Insignificant

ParkNear park 543 30.05 925 21.15 7.4805 <0.001No park 1264 69.95 3449 78.85 −7.4805 <0.001

SchoolNear school 150 8.30 361 8.25 0.0620 InsignificantNo school 1657 91.70 4013 91.75 −0.0620 Insignificant

Residential densityHigh density 1322 73.16 3418 78.14 −4.2148 <0.001

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owever, were statistically significant with left-turning vehicles.6 times more likely to be in violation of the crosswalk law.orward moving vehicles were 3.2 times more likely and right-

urning vehicles were 2.9 times more likely to be in violation ofhe crosswalk law than other vehicle movements. Notably, hotelOR = 3.0) and office areas (OR = 2.0) were strongly associatedith driver violations.The signalized intersection driver model shows seniors

xhibiting lower odds of violation, but cars emerging as hav-ng a higher odds of violation than other vehicle types. Alsot signalized intersections, vehicles making turning movementsave a lower odds of being involved in violations (OR < 1). Hotel

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956 21.86 4.2148 <0.001

and use is more strongly associated with driver violations of therosswalk law.

The unsignalized intersection model for drivers shows thatany of the variables which are significant in the other models

rop out of this model. Drivers making left turns, however, havehigh odds (OR = 2.6) of violating the crosswalk law than other

urning movements. Hotel land use remains a significant fac-or, but residential land uses show a significant increased effect

OR = 4.6). These results suggest that there are significant differ-nces between unsignalized and other types of intersections inerms of factors affecting the driver violations of the crosswalkaw.

900 K. Kim et al. / Accident Analysis and Prevention 40 (2008) 894–904

Table 4Analysis of driver violations

Variables Drivers violatingthe law

Percentage (%) Drivers in compliancewith the law

Percentage (%) Z-test Value

Overall observations 839 100.00 634 100.00

GenderMale 547 65.20 392 61.83 1.3310 InsignificantFemale 292 34.80 242 38.17 −1.3310 Insignificant

Age groupYoung adult (17–25 years) 53 6.32 28 4.42 1.5844 InsignificantAdult (25–60 years) 712 84.86 526 82.97 0.9848 InsignificantSenior (60 years and above) 74 8.82 80 12.62 −2.3590 0.0182

Type of crosswalkSignalized intersection 254 30.27 247 38.96 −3.4837 <0.001Unsignalized intersection 301 35.88 255 40.22 −1.7032 InsignificantSignalized midblock 25 2.98 0 0.00 4.3838 <0.001Unsignalized midblock 259 30.87 132 20.82 4.3250 <0.001

Time of day7 a.m.–9 a.m. 32 3.81 18 2.84 1.0231 Insignificant9 a.m.–11 a.m. 135 16.09 99 15.62 0.2472 Insignificant11 a.m.–1 p.m. 246 29.32 214 33.75 −1.8179 Insignificant1 p.m.–3 p.m. 200 23.84 170 26.81 −1.3040 Insignificant3 p.m.–5 p.m. 225 26.82 131 20.66 2.7322 0.00645 p.m.–7 p.m. 1 0.12 2 0.32 −0.8273 Insignificant

WeatherSunny 348 41.48 337 53.15 −4.4487 <0.001Cloudy 485 57.81 286 45.11 4.8308 <0.001Rainy 6 0.72 11 1.74 −1.8146 Insignificant

Type of vehicleCar 466 55.54 334 52.68 1.0914 InsignificantSUV 148 17.64 130 20.50 −1.3912 InsignificantTruck 105 12.51 81 12.78 −0.1494 InsignificantVan 100 11.92 85 13.41 −0.8533 InsignificantMotorcycle 20 2.38 4 0.63 2.6311 0.0086

Vehicle movementTurning right from behind the crosswalk 63 7.51 117 18.45 −6.3507 <0.001Turning right approaching the crosswalk 175 20.86 130 20.50 0.1658 InsignificantTurning left approaching the crosswalk 116 13.83 75 11.83 1.1293 InsignificantMoving forward 485 57.81 312 49.21 3.2779 <0.001

Land useMixed commercial residential 282 33.61 234 36.91 −1.3133 InsignificantCommercial 269 32.06 267 42.11 −3.9702 <0.001Hotel 124 14.78 44 6.94 4.6865 <0.001Office 107 12.75 44 6.94 3.6420 <0.001Residential 57 6.79 45 7.10 −0.2275 Insignificant

ParkNear park 103 12.28 60 9.46 1.7039 InsignificantNo park 736 87.72 574 90.54 −1.7039 Insignificant

SchoolNear school 80 9.54 74 11.67 −1.3271 InsignificantNo school 759 90.46 560 88.33 1.3271 Insignificant

Residential densityHigh density 681 81.17 488 76.97 1.9704 0.0488Low density 158 18.83 146 23.03 −1.9704 0.0488

Note: Z-test is a 2-tailed test of proportions.

K. Kim et al. / Accident Analysis and Prevention 40 (2008) 894–904 901

Table 5Driver violations logistic model results

Parameter Overall model Signalized intersection model Unsignalized intersection model

Estimate Standard error Odds ratio Estimate Standard error Odds ratio Estimate Standard error Odds ratio

Intercept −0.8746 0.1680 1.6574 0.5673 −0.2054 0.1038

Driver’s characteristicsDriver senior vs. non-senior −0.4055 0.1754 0.667 −1.1694 0.3217 0.311

Vehicle typeCar vs. other 0.5576 0.1981 1.746

Vehicle movementMoving forward vs. other 1.1619 0.1805 3.196Turning right from behind

crosswalk vs. other−3.0263 0.5986 0.048

Turning right approachingcrosswalk vs. other

1.0488 0.2010 2.854 −1.7214 0.5706 0.179

Turning left approachingcrosswalk vs. other

1.2817 0.2228 3.603 −1.6392 0.5812 0.194 0.9608 0.3131 2.614

Land use typeHotel vs. other 1.1071 0.1937 3.025 1.3662 0.4288 3.920 1.0746 0.2378 2.929

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.9. Pedestrian violation models

Table 6 contains the logistic regression results for pedes-rian violations for signalized, unsignalized, and all types ofrosswalks. Similarly to the driver models, pedestrian violationodels at midblock crossing are not statistically significant. In

he overall pedestrian model, males have a much higher oddsf violation (OR = 1.3) than females. Children have a lowerdds (OR = 0.71) of violating the crosswalk law than other ageroups. Pedestrians in hotel districts have higher odds of viola-ion (OR = 1.3) as do pedestrians in residential areas (OR = 1.5).otably, in this combined intersection model, pedestrians inffice areas have a lower odds (OR = 0.85) of violating the cross-alk law.

The signalized intersection model shows that male pedestri-

ns have a higher odds of violation (OR = 1.37) and children havelower odds ratio (OR = 0.47). Hotel districts (OR = 2.4) and

esidential areas (OR = 2.3) are in the model but office areas fall

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able 6edestrian violations logistic model results

arameter Overall model Sign

Estimate Standard error Odds ratio Estim

ntercept −1.0746 0.0511 −1.43

edestrian’s characteristicsPedestrian male vs. female 0.2418 0.0563 1.274 0.31Pedestrian child vs. non-child −0.3391 0.1633 0.712 −0.75Pedestrian youth vs. non-youthPedestrian adult vs. non-adult

and use typeHotel vs. other 0.2673 0.0634 1.306 0.89Residential vs. other 0.4337 0.1393 1.543 0.83Office vs. other −0.1664 0.0829 0.847

ote: Confidence level at 95%.

1.5176 0.4383 4.561

ut as statistically significant factors associated with pedestrianiolations.

The background characteristics of pedestrians associatedith violations of the crosswalk law change quite significantly

n the unsignalized intersection model. With this model, youthOR = 1.8) and adults (OR = 1.8) emerge as statistically signif-cant factors. Hotel districts (OR = 1.6) stay in the model, buthe direction of the effect for office zoned areas changes fromegative to positive (OR = 1.8).

. Discussion

The findings in this paper demonstrate the complexities asso-iated with pedestrian safety. Based on the frequency analysis,

roportionately more drivers than pedestrians violate the cross-alk law. The most common driver violations involved not

topping for pedestrians who are on the crosswalk, followedy stopping but continuing to proceed before the pedestrian has

alized intersection model Unsignalized intersection model

ate Standard error Odds ratio Estimate Standard error Odds ratio

88 0.0804 1.7265 0.1363

11 0.0890 1.36549 0.3122 0.470

0.6297 0.2061 1.8770.3786 0.1345 1.460

05 0.0926 2.436 0.4575 0.1212 1.58039 0.1718 2.302

0.5877 0.2491 1.800

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02 K. Kim et al. / Accident Analysi

eft the crosswalk. These are, potentially, the most dangerousriver actions. Vehicles which have stopped on the crosswalk,locking the path of pedestrians is less common, but nonethe-ess hazardous for pedestrians as they may be required to walkround vehicles or leave the crosswalk area. Among pedestrians,he most common violation involves “jaywalking” or crossinghile not in the crosswalk. A large proportion of pedestriansere also observed crossing against a red light or entering the

rosswalk against a blinking warning. More male drivers andedestrians were observed in violation of the law than females.n general, all pedestrian age groups were found to be in violationf the law, including a relatively high incidence of jaywalk-ng among children, adults, and seniors. While proportionately

ore young drivers as compared to adult or older drivers wereound to violate the law, there were nonetheless, violations byll types of drivers. The most common vehicle movement forrivers violating the crosswalk law was a forward movement,ollowed by left turns and then right turns approaching a cross-alk. The frequency analysis shows that the greatest number ofriver violations occurs at unsignalized midblock crosswalks.mong pedestrians, there is a higher incidence of jaywalk-

ng at unsignalized intersections and midblock crossings. Thereere also significant numbers of pedestrians observed crossing

gainst the red light and entering crosswalk against blinkingights at various signalized intersections. The frequency analy-is shows that there are more pedestrian violations, especiallyaywalking in resort districts, but also a high incidence in allistricts. While the pedestrian problem seems more pronouncedn the tourist and residential areas, the driver violations, in termsf frequencies were greatest in the hotel and office districts.

In re-examining the statistical analysis of the differencesetween violators and non-violators, it is apparent that therere not just statistically different differences between pedes-rians and drivers, but also differences between violators andon-violators, especially for pedestrians. The pedestrian viola-or is statistically more likely to be male, crossing at signalizedntersections, especially during the morning and midday time ofay, and in the hotel districts. Pedestrian violators also tend toe in areas close to parks, yet proximity to schools had no sta-istically significant effect. Bad weather is also associated withigher rates of violation. Among drivers, there are few statisti-ally significant discriminators of violators and non-violators.ender, age (young and adult), most times of the day, vehicle

ype, and proximity to parks and schools were not statisticallyignificantly different for violating and non-violating drivers.enior drivers were more likely to be in compliance with the

aw than other age groups. Driving violations were more likelyo occur at midblock locations than at intersections. Drivers were

ore likely to violate the law during the afternoon peak anduring cloudy days. Vehicle type was not a significant variablexplaining the differences between violators and non-violators.iolations were also associated with hotel districts and officereas and high-density residential areas.

In reviewing the logistic regression model results, differentactors affect the odds of violation for drivers and pedestrians.n general, the driver violation models performed better than theedestrian models. There were also significant differences asso-

pcas

Prevention 40 (2008) 894–904

iated with the types of intersections. While it is understandablehat drivers might be different from pedestrians, it was surprisingo find the extent to which driver and pedestrian behavior variesy intersection type. Environmental and intersection characteris-ics affect pedestrian and driver behavior. This means that it maye necessary to develop localized enforcement, education, andngineering solutions. A one size fits all approach, evidently, willot be as effective as a more customized approach to addressingarticular locations or at least types of locations.

Based on the logistic regression driver models, it would berudent to develop different safety programs for signalized andnsignalized intersections, particularly in the hotel district loca-ions. At unsignalized intersections, there is a particular need tonvestigate turning movements, particularly for vehicles mak-ng left turns. With the pedestrian violation models, overall itay be appropriate to further investigate and develop programs

or male pedestrians, in both hotel and high-density residentialreas. At unsignalized intersections, there may be need for morettention to youths as well as adult pedestrians.

The results found in this study generally concur with find-ngs in other national studies (Campbell et al., 2004) as well ashose focused on particular states (Baltes, 1998). Campbell etl. (2004) suggest that more pedestrian crashes occurred duringaylight and more male pedestrians were killed compared toemales (Campbell et al., 2004). Baltes (1998) found that moreales were involved in pedestrian crashes in Florida (Baltes,

998). This study finds that more violations by pedestriansccurred during early morning and midday times, and moreales violated the law compared to females. Both Campbell

t al. (2004) and Baltes (1998) studies found that more adults25–60 years old) were involved in pedestrian crashes relativelyo other age groups. Similarly, this paper also found that adultsere most likely to violate the crosswalk law.The safety of pedestrians and the threats associated with left-

urning vehicles have been well documented (Lord, 1996). Lord1996) found that left-turning vehicles contribute to 20–30% ofedestrian accidents at intersections (Lord, 1996). The driverogistic model suggests that left-turning vehicle were 3.6 times

ore likely to be in violation of the law compared to otherovements.There is need for additional research related to the behavior

f pedestrians such as focusing on waiting time (Hamed, 2001)nd gap selection (Oxley et al., 2005). Hamed (2001) found outhat different groups of people have different attitudes towardsrossing wait time. Some people are apparently more patienthen others at signalized crosswalks. Oxley et al. (2005) sug-ests that the elderly made risky crossing decisions because theyended to overestimate their walk speed at crosswalks (Oxley etl., 2005).

.1. Limitations of the study

There are a number of limitations with this study. First, the

rocedures involved conducting observational data. There arehallenges with not only collecting accurate information, butlso ascribing meaning to the results. While the procedures wereimplified in order to identify clear pedestrian or driver viola-

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ions of the law, there is some uncertainty as to knowledge ofhe law itself. Drivers and pedestrian may not have known thathe law changed. They also may have difficulty interpreting theaw. Of course, old habits may die hard. It would be useful toonduct attitudinal surveys to better determine if drivers andedestrian understood changes in the law and if they could cor-ectly describe violations. A second limitation of the study ishat the presence of two observers may have affected the behav-or of some pedestrians and drivers. While the observers wereiscretely positioned, it is possible that some of those observeday have altered their behavior because they knew they were

eing watched (the so called “Hawthorne effect”). This may beore so for pedestrians than drivers. Yet, given the large number

f violations that were observed, the confounding effect seemso be minor, particularly at busy intersections where the largemount of activity would mask the collection of data. Many fac-ors associated with unsafe pedestrian or driver behaviors suchs alcohol or drug use, or personality traits related to risk taking,r, whether or not the individual has received a traffic citation,ould not be adequately determined by these procedures. Givenhe fact that Hawaii has stepped up enforcement, not just ofhe seat belt law, but also of the newly enacted crosswalk law,he effects of various enforcement and public education cam-aigns on the behavior of pedestrians and drivers could not bessessed by this single point in time study. Finally, this was atudy focusing on the general pedestrian and driver population.t might be appropriate to focus more narrowly on vulnerableroups – examining the behaviors and attitudes of elderly oroung pedestrians or others at heightened risk of accident andnjuries resulting from street crossings. Socio-economic factors

ay be particularly relevant to pedestrian accidents (LaFlammend Diderichsen, 2000).

. Conclusions

The research reported in this paper suggests a number ofirections to enhance the safety of pedestrians in Hawaii. Basedn the results in this study, it is evident that more emphasishould be directed towards changing driver behavior for twoeasons. First, drivers commit proportionately more violationshan pedestrians. Second, as the analysis of differences betweeniolators and non-violators and the logistic regression modelingffort suggests, violations are committed by a broader range ofrivers than pedestrians. Since the law does emphasize giving theright-of-way” to legally crossing pedestrians and that driversave been put on notice in terms of being responsible for theafety of pedestrians, it is clear that more education and enforce-ent should be directed to drivers. This is not to suggest that

edestrians are without fault. Indeed, there is need for more edu-ation and enforcement of the crosswalk law among pedestrianss well. There may be need for customized programs in the hotelistricts as well as in other mixed use and commercial areas.iving citations to tourists and workers scurrying to their jobs

ay be an unpopular, yet necessary strategy to increase compli-

nce. The visitor industry creates special problems. In additiono the huge number of visitors to Hawaii (more than 7 millionnnually), reaching tourists and changing their behaviors is com-

K

Prevention 40 (2008) 894–904 903

licated because of the fact that they come from diverse placesnd because they stay for a relatively short period of time inawaii. Yet, there may be opportunities because the hotel dis-

ricts are readily identifiable. The research in this paper pointso the need for more attention to particular locations, types ofntersections, and other site-specific characteristics. This sort ofpproach lends itself to various engineering and traffic calmingolutions (Zaidel and Hochman, 1987) for enhancing the safetyf pedestrians.

Three different directions for further research arise from thistudy. First, there is need for additional longitudinal studies ofompliance with the new crosswalk law. This particular studyas conducted during the Spring of 2006 and it is necessary to

stablish longer term time trends and whether or not the pat-erns identified in this study remain stable over time. Second,here is need for additional comparative research – not just inerms of more locations and jurisdictions in Hawaii, but also inther places with similar crosswalk laws. This would help toetter understand the universality of certain driver and pedes-rian behaviors. Finally, there is need to focus on key groups ofnterest. In addition to tourists, a more focused effort might beirected towards office workers, students and senior citizens asell as others at-risk of injury and death from pedestrian acci-ents. While additional observational studies would be useful,here may be strategic opportunities to examine the traffic cita-ion database in terms of understanding more about the driversnd pedestrians who violate crosswalk laws.

cknowledgements

The authors acknowledge the support of Mr. Gordon Hongnd the Safe Communities Program of the Department of Trans-ortation, State of Hawaii.

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