A METHODOLOGY TO IDENTIFY HIGH PEDESTRIAN CRASH LOCATIONS: AN ILLUSTRATION USING THE LAS VEGAS METRO AREA

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1 A METHODOLOGY TO IDENTIFY HIGH PEDESTRIAN CRASH LOCATIONS: AN ILLUSTRATION USING THE LAS VEGAS METRO AREA Srinivas S. Pulugurtha Transportation Research Center University of Nevada, Las Vegas 4505 Maryland Parkway, PO Box Las Vegas, NV Telephone: (702) ; Fax: (702) Shashi S. Nambisan Transportation Research Center University of Nevada, Las Vegas 4505 Maryland Parkway, PO Box Las Vegas, NV Telephone: (702) ; Fax: (702) Word Count: Text (5,221 + Figures/Tables 7 250) = 6,721 total words 82 nd Transportation Research Board Annual Meeting Washington, DC. January 2003

2 A Methodology to Identify High Pedestrian Crash Locations: An Illustration Using the Las Vegas Metro Area Srinivas S. Pulugurtha 1 Shashi S. Nambisan 2 ABSTRACT In recent years, Las Vegas metro area in Clark County, Nevada has experienced one of the highest fatal pedestrian crash rates and the pedestrian injury crash rates among urban counties with similar populations in United States. Common notions attribute these crashes to high numbers of visitor volumes, large amount of pedestrian movements, and the free-flowing alcohol provided to customers at major resorts and casinos. However, facts dispel the myth that a vast majority of pedestrians involved in crashes and drivers involved in crashes with pedestrians are residents of Las Vegas. Thus, the pedestrian safety problem in Las Vegas metro area warranted immediate attention. A methodology is developed and implemented to identify pedestrian high crash locations in the Las Vegas metro area. Crash data were collected for a five year period (1996 to 2000) and address matched using the street name / reference street name location referencing system and a Geographic Information System software program. Pedestrian crash rates were computed using zipcodes as the basis for zones. Two indices were developed: one was to determine crash exposure rates, and the other was to compute the proportion of crashes at locations with respect to the total pedestrian crashes in the study area. These indices were used to identify the zones with pedestrian crash rates higher than the average pedestrian crash rates for the entire study area. From within these zones, 34 high pedestrian crash locations were identified that accounted for about one-third of all pedestrian crashes in the study area. These locations are ranked based on the computed crash indices to help prioritize identification and implementation of pedestrian safety treatments. INTRODUCTION The Las Vegas metro area has experienced more than 85 percent growth in population during the last decade. Over 30 million tourists visit Las Vegas each year. These high numbers of population and tourism have had a direct impact on mobility and travel demand, construction and maintenance of various transportation related infrastructure facilities, and traffic safety in the Las Vegas metro area. Common notions attribute the safety problem to the large number of visitors and pedestrian movement along the resort corridor. However, data show that over 85 percent of pedestrians involved in crashes are Las Vegas metro area residents, and a similar proportion of drivers of motor vehicles involved in crashes with pedestrians are also Las Vegas metro area residents. Further, pedestrian crashes are not limited to or concentrated in the resort corridor. Stakeholders in the Las Vegas metro area recognize the need to study the causes of these pedestrian crashes and identify appropriate pedestrian safety treatments. A pedestrian safety program to research, evaluate and implement various engineering, Intelligent Transportation Systems-based, education and enforcement strategies to enhance pedestrian safety was initiated in the Las Vegas metro area in October The program 1 Transportation Research Center, University of Nevada, Las Vegas, 4505 Maryland Parkway, Box , Las Vegas, NV , pss@trc.unlv.edu 2 Transportation Research Center, University of Nevada, Las Vegas, 4505 Maryland Parkway, Box , Las Vegas, NV , shashi@ce.unlv.edu

3 involves two phases. The first phase is to primarily identify high pedestrian crash locations in the Las Vegas metro area. The second phase will implement the selected treatments at the selected locations, and evaluate their performance. The focus of this paper is on the development and implementation of a methodology to identify high pedestrian crash locations. Data from the Las Vegas metro area are used to illustrate the application of the methodology. The investigation of pedestrian crashes is based on data maintained by Nevada Department of Transportation (NDOT). The study considered pedestrian crashes from January 1996 to December A methodology was developed by utilizing capabilities afforded by a Geographic Information System (GIS) software program (ARC/Info), and spreadsheet software such as Microsoft Excel. Overview of Pedestrian Crashes in Clark County, Nevada About 71% of the pedestrian fatalities and 75% of the pedestrian injuries in Nevada occur in Clark County [1]. Figures 1(a) and 1(b) illustrate that during 1997, 1998 and 1999 Clark County, Nevada has experienced the highest fatal pedestrian crash rates per 100,000 population and the pedestrian injury crash rates per 100,000 populations among urban counties with similar populations. These rates were compared using online reports based on crash data collected and developed by / for the responsible agencies. A few of these reports are provided in the reference section. A majority of these pedestrian crashes happen in the city of Las Vegas and the more densely populated portions of Clark County adjacent to the city. Thus, the pedestrian safety problem in Las Vegas metro area warrants immediate attention. Common notions attributes these rates to the high levels of visitors and the free-flowing alcohol provided to customers at major hotel casinos. Data show that only 12 percent of crashes involve pedestrians under the influence of alcohol and drugs (which is less than the national average). Aside from this, pedestrian crashes are not only concentrated along the resort corridor (Figure 2) but are also geographically distributed over the metro area (Figure 3). Investigations show that the proportions of pedestrian crashes are high on minor and principal arterial streets. These pedestrian crashes in the Las Vegas metro area are primarily due to motorist failure to yield at intersections and pedestrians failure to yield at mid-block locations. Approximately 46 percent of pedestrian crashes involved children and elderly. Males were involved in a higher number of pedestrian crashes than females. Pedestrian crashes were the highest between 3:00 PM to 6:00 PM, which is a peak period for vehicular traffic. However, pedestrian crashes are distributed relatively uniformly on all days of week. An analysis of the pedestrian action field in all the pedestrian crash records shows the following to be the top five causal factors for the period from January 1996 to December 2000, 1. Crossing not at intersection - no pedestrian crosswalk 2. Crossing at intersection with signal 3. Crossing at intersection - no signal, marked crosswalk 4. Ran into roadway, and 5. Not in roadway. Motorist failure to yield was the primary cause of pedestrian crashes at intersection whereas pedestrian failure to yield was the primary cause of pedestrian crashes at mid-block locations. The high incidence of pedestrian crashes warrants further analysis in order to identify high pedestrian crash locations. A methodology using Geographic Information Systems (GIS) software was developed and implemented to help identify high pedestrian crash locations. The results can be used to implement treatments based on potential underlying explanatory factors in order to enhance pedestrian safety at the subject locations.

4 Literature Review Crash studies are generally based on reported crash records. However, such data may not be good predictors of future crash locations, especially for infrequently occurring pedestrian crashes [7]. The problem could be partially solved by combining crash data with a perception survey method. The study concluded that perception survey data help improve site selection and recommendations for pedestrian safety treatments. However, the survey method might not be appropriate for large study areas such as metropolitan areas as it requires lot of resources and time. Also, unless carefully analyzed, it could result in biased results (note that driver s perceptions are also essential to analyze the reasons for pedestrian crashes and conflicts). A key component of identifying high pedestrian crash locations involves accurately coding the location of such crashes on digital maps. One technique that can be used for this purpose is to address match pedestrian crash locations to the relevant street centerline (or network) coverage in a GIS program [8, 9]. In order to ensure a reasonably stable measure, a minimum of one year's data or at least 100 crash records should be available for establishing pedestrian safety zones [10]. The National Highway Traffic Safety Administration (NHTSA) has developed a guide to identify study zones for pedestrian safety [11]. The zone process provides a systematic method for targeting pedestrian safety improvements in a cost effective manner. Zoning identifies a sub area of a jurisdiction or study area containing as much of the pedestrian problem of interest in as little land area as possible. The first step is to select an initial shape for the zones and to define the target rate, i.e., the number of crashes that must fall in an area for it to be defined as a priority zone. The approach suggested is to search for circular zones, then to search for linear zones, and then to determine their final shape. The initial circular zones could be created by using one mile radius as pedestrian activity is typically within one mile of an individual s home or work place. High risk zones could be those with a target rate of 10 pedestrian crashes per zone compared to a total annual size of 200 pedestrian crashes in the study area. For linear zones, it could be determined for the segments where six or more crashes occur in a two miles. In addition, if total crash data that are used in analysis are higher, the target rate should be adjusted upward as necessary. The final step is to identify the final zone shape, as it may be useful to combine zones, increase radii, change zones shape, or reduce zones size. The methodology is more suitable if pedestrian crashes show some clustering and some dispersion throughout a study area. However, it may not appropriate in cases where pedestrian crashes are not clustered and are distributed randomly throughout the study area. Apart from this, identifying zones and their size or clustering pedestrian crashes depends a lot on the judgment of the analyst. Two high pedestrian crash locations which account for 30 percent of all pedestrian crashes were identified in Hartford County, Connecticut based on address matched crash data for analysis [9]. In a different context, three-mile buffer zones were created around 3 clustered areas using GIS to study moped safety in Hawaii. The temporal variations, environmental characteristics, and crash characteristics of these spatially distributed moped crashes were then studied [12]. A simple method, called nearest neighborhood analysis, was used to identify hot spot locations in a mid-block pedestrian safety study [13]. The analysis used grid cells with a dimension of 100 feet per site and a circular radius of 500 feet. The resultant scores were grouped and ranked based on the distribution of number of pedestrian crashes. Most engineering and intelligent transportation systems (ITS) based pedestrian safety treatments are appropriate for implementation at specific point locations. Hence, the size of the

5 zone should be such that pedestrian crashes at a location represent pedestrian crashes in the vicinity of a location. The width of a buffer in such cases should be relatively small (say, 100 to 300 feet). METHODOLOGY A GIS based methodology to identify high pedestrian crash locations is presented in this section. The methodology involves several steps. They are discussed next. Step 1: Identify Pedestrian Crash Problems The focus of this step is to identify crash problems based on pedestrian crash characteristics in the study area. This is the first step of the zoning methodology recommended by NHTSA to identify high pedestrian crash locations. An overall analysis has to be conducted in order to identify pedestrian crash problems. As an example, pedestrian crashes at signalized intersections, pedestrian crashes at mid-block locations, pedestrian crashes involving children, and pedestrian crashes involving senior citizens are significant pedestrian crash problems in Las Vegas metro area. Step 2: Identify Zones for Analyses Zones have to be defined for analysis. A zone could be a zip code, a traffic analysis zone (TAZ) that is typically used in travel demand forecasting, a census block, or a census tract. Using zones such as a TAZ or a census block could lead to erroneous results in places like Las Vegas where large activity centers may have very little resident population. For example, a large hotel casino in Las Vegas may be a TAZ by itself with significant number of pedestrian crashes occurring in the TAZ. However, the resident population in such a TAZ would be very small because the census data do not consider guests or employees at hotels in resident population counts. Thus, the computed crash rates (discussed in Step 5) would be high indicating that the zone is a high pedestrian risk area which is not true. If it were possible to obtain accurate estimates of the number of people in TAZs (accounting for non-resident population), TAZs would serve as excellent candidates to identify zones. Considering such issues, a zip code was deemed appropriate for analyses. Step 3: Address Match Pedestrian Crash Data Until recently, NDOT used to store crash location related information in crash database based on one of the 3 reference systems: street name / reference street name, mile post, or street address. The street name / reference street name location referencing system is mostly used in urban areas whereas mile post referencing system is used in rural areas. Mid-block locations are sometimes referenced using street address. The type of reference system, thus, depends on the focused study area. If the focus is to identify locations in a metropolitan area, then street name / reference street name location reference system is suitable. Pedestrian crash locations could be address matched using the addressmatch feature available in commonly used GIS software programs. Step 4: Overlay Pedestrian Crash Data on the Zones Coverage The address matched pedestrian crash data is then overlaid on the selected zones (zip code) coverage. The number of pedestrian crashes in each zone is then estimated. The overlay process also facilitates incorporating data characterizing the zone. Examples of such data include population, demographic, socio-economic, and land use characteristics.

6 Step 5: Compute Pedestrian Crash Rates and Rank the Zones The pedestrian crash rate is calculated by dividing the number of pedestrian crashes estimated in the previous step by the total population in the zone. In fact, the pedestrian crash rates should be based on the nature of the safety problem being evaluated. For example, if pedestrian crashes involving senior citizens is the problem, pedestrian crash rates should be calculated by dividing the total number of pedestrian crashes involving senior citizens in the zone by the total number of senior citizens in the same zone. The zones are then ranked based on their pedestrian crash rates. Step 6: Identify High Crash Locations in the Selected Zones Zones with pedestrian crash rates greater than the average pedestrian crash rate for the entire study area are identified next. Locations are selected within these zones to conduct detailed analysis. The locations could be small circular areas or linear corridors based on clustering of pedestrian crashes. As most pedestrian treatments are implemented at specific point locations, it is felt that the radius of the circular area should be as small as possible. 100 feet was agreed upon as a reasonable value. However, no such limits were used to identify linear corridors. Step 7: Rank High Crash Locations A detailed analysis of pedestrian crashes at each selected location is conducted. Pedestrian crash indices are developed to help such analyses. These look at aspects such as pedestrian crashes in the vicinity of a location, the population at risk, the relative significance of crashes at the location when compared to all crashes in the study area. Two different types of crash indices were developed in this study.. The first index, Crash Index 1, is computed by dividing the percent of pedestrian crashes in the vicinity of a location by the percent of population at risk in the proximity of the location. The following equations show estimation of percent of pedestrian crashes in the vicinity of a location, percent of population at risk, and Crash Index 1. % of Ped Crashes Location = Ped CrashesLocation Ped Crashes in Study Area *100 % of Pop in Vicinity Crash Index 1 = Location Location Pop in VicinityLocation = Pop of Study Area *100 % of Ped CrashesLocation % of Pop in Vicinity Location A Crash Index value that is greater than 1.0 indicates that this location has a greater than proportional crash risk. A value less than 1.0 indicates that the location has a less than proportional crash risk. The second measure, Crash Index 2, is the percent of pedestrian crashes in the vicinity of the location. Crash Index 2 is a scaled measure of the proportion of crashes at a location when compared with all the crashes in the entire study area. Larger the value of Crash Index 2, the greater is the significance of crash problems at the location. The maximum value of Crash Index 2 is 100.

7 Crash Index 2 Location = Ped CrashesLocation Ped Crashes in Study Area *100 Pedestrian crashes within less than 100 feet are considered as occurring in the vicinity of the location. Pedestrian volume count by age group in the proximity of a location is an ideal measure to compute potential population at risk. However, obtaining such data is difficult. Hence, population statistics were used to estimate potential population at risk. The potential population at risk was estimated by generating buffers (using a GIS program) of width equal to accessible walking distance (say, one-half-mile) around each location, and estimating the population residing in the vicinity of each location. Pedestrian crash indices were computed and locations were ranked to prioritize implementation of pedestrian safety treatments. DISCUSSIONS Investigations show that pedestrian crashes at signalized intersections, pedestrian crashes at midblock locations, pedestrian crashes involving children, and pedestrian crashes involving senior citizens are significant pedestrian crash problems of concern, which need immediate attention. However, all pedestrian crashes were given equal weight in this study. The zonal analysis was done at zip code level. Demographic characteristics at zip code level were compiled using Census Bureau 2000 data [14]. Since the study area primarily comprises of the Las Vegas metro area, the pedestrian crash data were address matched using the street name and reference street name of crashes in the NDOT database and street centerline (SCL) coverage developed by the Clark County Geographic Information System Managers Office (GISMO). Approximately 95 percent of pedestrian crashes were address matched. The pedestrian crash coverage is then overlaid over the SCL coverage to estimate the number of pedestrian crashes in each zone. Crash rates were computed for each pedestrian crash problem. For illustrative purposes, only computed crash rates per 1,000 populations obtained from dividing the total number of pedestrian crashes in a zip code with the total zip code population multiplied by 1,000 is discussed in this paper. Figure 4 shows computed pedestrian crash rates by zip code. The computed average pedestrian crash rate 1,000 population for the Las Vegas metro area is Shaded zones indicate those zones that have pedestrian crash rates higher than the average pedestrian crash rate per 1,000 populations for the entire study area. 34 high crash locations with clustered pedestrian crashes in the zones with high pedestrian crash rate were then identified. These 34 locations were selected such that they represent about one-third of total number of pedestrian crashes in the Las Vegas metro area. Considerations were also given to see that a minimum of 2, but not more than 3 locations have same land-use characteristics, traffic characteristics and geometric conditions. This was done so as to evaluate the performance of various pedestrian safety treatments under different location conditions. Figure 5 shows the 34 locations selected for detailed analysis. Pedestrian crashes within 100 feet of each location and the potential population at risk in the vicinity of each location were identified using spatial analyses capabilities of ARC/INFO, a GIS program. Crash Index 1 and Crash Index 2 were estimated for each location (Table 1). The table shows the location (name), number of crashes in the proximity of each location, population residing in the vicinity of each location, Crash Index 1, Rank 1 (based on Crash Index 1), Crash Index 2, and Rank 2 (based on Crash Index 2). The percent of pedestrian crashes was computed

8 based on 3,710 crashes in the Las Vegas metro area during the years 1996 to census data were used to compute the percent of population in the vicinity of each location. The estimated Las Vegas metro population during 2000 was 1,375,765. Note that though pedestrian crash data were considered for 5 years, population statistics were considered for only the year This was due to the non-availability of accurate demographic data for the other years. The drawback of such an assumption is that effects of growth trends during these 5 years would not be considered. As can be seen in Table 1, ranks based on Crash Index 1 and Crash Index 2 is different for each location. It would be appropriate to use ranks based on Crash Index 2 if minimizing number of crashes in the study area is the objective. Crash Index 1 could be used when the objective is to minimize pedestrian crashes by providing pedestrian treatments considering the population at risk. However, Crash Index 1 should be used carefully in places such as Las Vegas. For example, at Location 22: Las Vegas Boulevard. / Fremont Street only 14 pedestrian crashes were recorded during the period 1996 to The location is ranked 1 based on Crash Index 1 because of low resident population in the vicinity of the location. For the pedestrian safety program in the Las Vegas metro area, it was agreed that Crash Index 2 would be used to rank the pedestrian crash locations. Table 2 shows location, number of pedestrian crashes and Crash Index 2 sorted by Rank 2 of the location. However, crashes occurring in the proximity of each location have to be carefully analyzed to study possible causes of these crashes. Appropriate countermeasures for each location should then be identified based on explanatory factors. Examples of such countermeasures include countdown timers, enlarged pedestrian signal heads, animated eyes for pedestrian signal heads, animated eyes warning signs that show driver the direction pedestrians are crossing, smart lighting to warn drivers that pedestrians are crossing under dark light conditions, regulatory signs alerting drivers and pedestrians of potential conflict movements, advance stop / yield lines, elimination of permissive left turn movements, flashing beacons activated by speeding vehicles, dynamic signs restricting right-turn-on-red when pedestrians are present, automatic detection of pedestrians, inpavement lighting, median refuge islands, Danish offsets, and portable speed trailers with fine information. CONCLUSIONS The high incidence of pedestrian crashes in Las Vegas metro area warranted investigation of the causes of these pedestrian crashes and development of a methodology to identify high pedestrian crash locations to implement pedestrian safety treatments. 95 percent of pedestrian crash data collected from the Nevada Department of Transportation are address matched using the street name / reference street name location referencing system. The size of zones and their characteristics play a key role in identification of high risk areas. Zip codes were used to define zones in the Las Vegas metro area. Zones with high pedestrian crash rates were identified. 34 high pedestrian crash locations were identified in the zones with pedestrian crash rates greater than the study area s average pedestrian crash rate. These locations were ranked based on two computed crash indices. These indices depend on the percent of crashes in the vicinity of each location and population at risk in the vicinity of each location. This serves as a basis to prioritize implementation of pedestrian safety treatments. However, such suitable safety treatments for each location have to be identified based on explanatory factors. This warrants further research and analysis.

9 REFERENCES 1. NDOT, Nevada Department of Transportation Nevada Traffic Crashes California Highway Patrol (1998) 1997 Annual Report of Fatal and Injury Motor Vehicle Traffic Collisions. Website: 3. California Highway Patrol (1999) 1998 Annual Report of Fatal and Injury Motor Vehicle Traffic Collisions. Website: 4. California Highway Patrol (2000) 1999 Annual Report of Fatal and Injury Motor Vehicle Traffic Collisions. Website: 5. NDOT (1999) Nevada Traffic Crashes Prepared by the Nevada Department of Transportation Safety Engineering Division in Cooperation with Nevada Department of Motor Vehicles and Public Safety, and State and Local Law Enforcement Agencies. 6. NDOT (2000) Nevada Traffic Crashes Prepared by the Nevada Department of Transportation Safety Engineering Division in Cooperation with Nevada Department of Motor Vehicles and Public Safety, and State and Local Law Enforcement Agencies. 7. Schneider, R. J., A. Khattak, and R. M. Ryznar. Factors Associated with Pedestrian Crash Risk: Integrating Risk Perceptions and Police-Reported Crashes (Paper No. TRB ). Annual Transportation Research Board Meeting, Pre-print CD-ROM, Andaluz, D., T. Robers, and S. Siddall. GIS Add: A New Dimension to Crash Analysis. Journal of the Urban and Regional Information Systems Association, Vol. 9, No. 1, 1997 pp Braddock, M., G. Lapidus, E. Cromley, R. Cromley, G. Burke, and L. Banco. Using a Geographic Information System to Understand Child Pedestrian Injury. American Journal of Public Health, Vol. 84, No. 7, 1994, pp NHTSA, National Highway Traffic Safety Administration. Zone Guide for Pedestrian Safety Shows How to Make Systematic Improvements. Traffic Tech, Issue 181, Report No: HS , NHTSA, National Highway Traffic Safety Administration. Statistical Methods in Highway Safety Analysis. A Synthesis of Highway Practice. Transportation Research Board, National Research Council, NCHRP Synthesis 295, Kim, K, D. Takeyama, and L. Nitz. Moped Safety in Honolulu Hawaii. Journal of Safety Research, Vol. 26, No. 3, 1995, pp Cui, Z. GIS-based Evaluation of Midblock Pedestrian Crossing Safety. M.S. Thesis, Department of Civil & Environmental Engineering, University of Nevada, Las Vegas, Census Bureau. United States Census 2000 Population Tables and Reports. U.S. Census Bureau Population Division Website ACKNOWLEDGMENTS The authors acknowledge Federal Highway Administration, City of Las Vegas, Clark County Department of Public Works, Nevada Department of Transportation, Nevada Office of Traffic Safety and Regional Transportation Commission of Southern Nevada for their financial support which made possible this work. Further, staff from these agencies is thanked for their help and guidance. The authors also thank Leverson Boodlal of Federal Highway Administration, Richard T. Romer of Orth-Rodger & Associates, Natachai Wongchavalidkil, Vinod Vasudevan, Maggie

10 Saunders and Erin Breen of the University of Nevada, Las Vegas Transportation Research Center for their technical support and suggestions throughout the duration of the project.

11 4.0 Fatal Crash Rate per 100,000 Population Alameda County, CA Broward County, FL Clark County, NV King County, WA Maricopa County, AZ County Orange County, CA San Bernardino, CA San Diego County, CA Santa Clara County, CA FIGURE 1(a) Comparison of Pedestrian Fatal Crash Rates ( ) 70.0 Injury Crash Rate per 100,000 Population Alameda County, CA Clark County, NV Maricopa County, AZ Orange County, CA County San Bernardino, CA San Diego County, CA Santa Clara County, CA FIGURE 1(b) Comparison of Pedestrian Injury Crash Rates ( )

12 LAS VEGAS SAHARA DECATUR DESERT INN TWAIN VEGAS VAL VALLEY VIEW Tropicana Flamingo Pedestrian Crashes Hotel Casinos Major Streets N E W S FIGURE 2 Pedestrian Crashes and Hotel Casinos along the Resort Corridor in Las Vegas Metro Area

13 I 15 US 95 LAS VEGAS CRAIG SAHARA I 215 EASTERN LAMB BOULDER NELLIS JONES RAINBOW CAREY RANCHO UP RR HEND SPUR VEGAS CHEYENNE INDUSTRIAL DURANGO LAKE ME WASHINGTON SUNSET CHARLESTON FLAMINGO DECATUR DESERT INN 4TH TWAIN WIGWAM STEWART CENTENNIAL VEGAS VALLEY SWENSON ANN FORT APACHE SMOKE RANCH ANN PECOS PECOS I VALLEY VIEW Pedestrian Crashes Hotel Casinos Major Streets N E W S FIGURE 3 Pedestrian Crashes and Hotel Casinos in Las Vegas Metro Area

14 Pedestrian Crash Rate per 1,000 Population W N S E FIGURE 4 Pedestrian Crash Rates by Zip code in Las Vegas Metro Area

15 FORT APACHE DURANGO RAINBOW JONES DECATUR INDUSTRIAL LAS VEGAS SWENSON EASTERN LAMB VALLEY VIEW PECOS PECOS NELLIS I US 95 CENTENNIAL I 15 ANN ANN CRAIG CHEYENNE SMOKE RANCH CHARLESTON DESERT INN FLAMINGO VEGAS RANCHO $ 10 $ 9 SAHARA WASHINGTON $ 32 $ 33 CAREY 4TH $ $ $ $ 21 $ 3 $ 22 $ 12 $ 5$ 6 $ 13 $ 4 $ 7 $ $ 24 $ $ $ 29 $ $ 17 $ $ $ 18 $ $ TWAIN 19 STEWART 8 VEGAS VALLEY LAKE ME SUNSET BOULDER UP RR HEND SPUR WIGWAM I 215 $ High Crash Points N High Crash Corridors Major Streets W E S FIGURE 5 Selected High Pedestrian Crash Locations in Las Vegas Metro Area

16 TABLE 1 Ranking High Pedestrian Crash Locations Loc. Location Ped Crashes Proximate Pop % of Ped Crashes % of Pop Crash Index 1 Rank 1 Crash Index 2 Rank 2 1 Bonanza Rd / D St Bonanza Rd / F St Bonanza Rd / Las Vegas Bl Charleston Bl / Lamb Bl 6 2, Charleston Bl / Las Vegas Bl Charleston Bl / Maryland Pkwy Charleston Bl / Nellis Bl 13 3, Charleston Bl: Lucerne St to Lamont St 13 1, Decatur Bl / Meadows Ln 6 1, Decatur Bl / Washington Av 7 1, Desert Inn Rd / Boulder Hwy 6 2, Eastern Av / Bonanza Rd 10 3, Eastern Av / Charleston Bl 10 2, Flamingo Rd / Boulder Hwy 7 6, Flamingo Rd / Koval Ln 29 3, Flamingo Rd / Nellis Bl 9 8, Flamingo Rd / Paradise Rd 9 7, Harmon Av / Paradise Rd 12 5, Lake Mead Bl from McCarran St to Belmont St 12 2, Lake Mead Bl / McDaniel St 10 1, Lake Mead Bl / Pecos Rd 11 1, Las Vegas Bl / Fremont St Las Vegas Bl / Lake Mead Dr 12 1, Las Vegas Bl / Riviera Bl 25 1, Maryland Pkwy / Desert Inn Rd 36 5, Maryland Pkwy / Flamingo Rd 21 6, Maryland Pkwy / Karen Av 9 4, Maryland Pkwy / Sahara Av 7 3, Maryland Pkwy / Sierra Vista Dr 42 6, Maryland Pkwy / Tropicana Av 14 4, Maryland Pkwy / Twain Av 19 6, Sahara Av / Decatur Bl 11 5, Sahara Av / Valley View Rd 8 4, Tropicana Av / Spencer St 8 5,

17 TABLE 2 Ranking High Pedestrian Crash Locations Loc. Location Ped Crashes Crash Index 2 Rank 2 29 Maryland Pkwy / Sierra Vista Dr Maryland Pkwy / Desert Inn Rd Flamingo Rd / Koval Ln Las Vegas Bl / Riviera Bl Maryland Pkwy / Flamingo Rd Maryland Pkwy / Twain Av Las Vegas Bl / Fremont St Maryland Pkwy / Tropicana Av Charleston Bl / Nellis Bl Charleston Bl: Lucerne St to Lamont St Bonanza Rd / F St Harmon Av / Paradise Rd Lake Mead Bl from McCarran St to Belmont St Las Vegas Bl / Lake Mead Dr Lake Mead Bl / Pecos Rd Sahara Av / Decatur Bl Charleston Bl / Las Vegas Bl Eastern Av / Bonanza Rd Eastern Av / Charleston Bl Lake Mead Bl / McDaniel St Flamingo Rd / Nellis Bl Flamingo Rd / Paradise Rd Maryland Pkwy / Karen Av Sahara Av / Valley View Rd Tropicana Av / Spencer St Decatur Bl / Washington Av Flamingo Rd / Boulder Hwy Maryland Pkwy / Sahara Av Bonanza Rd / D St Bonanza Rd / Las Vegas Bl Charleston Bl / Lamb Bl Decatur Bl / Meadows Ln Desert Inn Rd / Boulder Hwy Charleston Bl / Maryland Pkwy