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16 Safety Analysisâ Database Development The prime objective of this project was to determine the safety effectiveness of the MPS. This section describes the identification of potential study sites and the building of the database that will be used in the safety evaluation. The database includes roadway characteristics, volume for both vehicles and pedestrians, and crash data. Site Identification The key for project success was identifying a sufficient number of treated sites. Members of the panel and the research team reached out to several groups to aid in site identification. Sources for recommended sites included members of the AASHTO Committee on Traffic Engineering and the ITE community. The request noted that NCHRP had recently started a new project on MPSs (NCHRP Project 3-141) and that the research team was looking for potential study sites. The MPS treatment was briefly described. The request ended with contact information and acknowledgment that participantsâ help was appreciated. Initial correspondence with members of the panel revealed that Los Angeles, CA, and Florida could have several study sites. The region of the country with the most MPSs is the city of Los Angeles. The city provided the research team a list of more than 150 installations. The Florida sites were planned rather than existing sites and were removed from consideration. The research team was informed about sites in Delaware, Kansas, and Hawaii. With only one or a few sites in each of these states, these sites were also removed. During site selection, the research team learned of several sites in Utah and San Antonio, TX. The team reviewed these sites and added the locations to the database when they met the study criteria. Study criteria included: â¢ A traffic control signal that included typical green, yellow, and red phases located at a two-leg crossing was considered an MPS. The MPS could have either a green arrow or a steady green ball indication. The two-leg description refers to the number of vehicle approaches rather than pedestrian approaches. â¢ The MPS must have been in place for at least 12Â months prior to the end of the available crash data. Initially, crash data were assumed to be available for all the sites up to DecemberÂ 2020. Later, the end date of DecemberÂ 2020 was revised with consideration of the pandemic, and FebruaryÂ 28, 2020, was used. â¢ For MPSs, the number of legs at the crossing was two. For control sites, the number of legs at the crossing could be two, three, or four. â¢ State must have at least 10 MPSs to be included in the study. â¢ MPS sites represent a two-leg condition. If a driveway was believed to have hourly activity (e.g., a driveway to a business, nongated, two-way operation, and/or a stop sign present), the site was considered to be a control site as a three- or four-leg intersection. C H A P T E R 4
Safety AnalysisâDatabase Development 17 â¢ Sites were removed if atypical intersection geometry was present, such as a large skew or a nearby frontage road that would affect pedestrian movement. Treated sites in Los Angeles, CA, San Antonio, TX, and several cities in Utah were identified. The research team then identified control sites near the treated sites. The ideal control site would have a marked crosswalk with or without supplemental pedestrian-related traffic control devices except a traffic control signal, two legs, and similar vehicle and pedestrian volume. The ideal control site was very rare as it would be odd to have a site with similar characteristics (including vehicle and pedestrian volume) to the nearby MPS but not signalized. The research team also identified several nearby three- and four-leg signalized intersections as control sites. To increase the pool of two-leg control sites, the research team had to identify sites in nearby cities. Vehicle and Pedestrian Volume The research team obtained vehicle and pedestrian volume data from various sources and in different formats. For most sites, a vehicle count was identified. In a few cases, the vehicle volume was not available at the specific location of interest; for example, a segment volume was not available at a midblock pedestrian crossing. In those cases, the research team used nearby counts. In the example of a midblock pedestrian crossing, the research team averaged the approach counts for the signalized intersections on either side of the midblock crossing. With regard to pedestrians, the preference was to use a 24-hour count of pedestrians at a site; however, that type of count was extremely rare. Several sources were used to provide the number of pedestrians at a crossing, as discussed in the sections covering each region in the database. When the count data reflected several hours within a day for the leg, the counts were expanded to represent a daily count and then a typical annual value for both vehicles and pedestrians. This section presents an overview of how vehicle and pedestrian volume data were collected for the various study areas. Vehicle and Pedestrian Volume Count Data for California Los Angeles The city of Los Angeles provided several traffic volume data sources that are available to the public. Traffic volume details are in a map-based application, with one application giving the volumes for links (26) and the other for intersections (27). The available volume data represent 6 to 24Â hours of data and were collected between 1999 and 2021. Details include the traffic count date, traffic count by direction, and traffic count by hour. In some cases, the morning and evening peak hour and their associated traffic counts are reported. A source for pedestrian counts provided by the city is in the NavigateLA system (28); the system has several layers, including a traffic data layer that shows the location of pedestrian counts. The research team searched for each Los Angeles site to determine if pedestrian count data existed. When data existed, the research team copied the counts per leg and summed to represent an intersection total. Short-term counts were adjusted using factors described below in the âExpanding Short-Term Pedestrian Volume Counts to Daily Valuesâ section. When pedestrian counts were not available, pedestrian volumes were estimated based on a model developed using census data (see the âEstimating Pedestrian Volume Using Census Dataâ section). Long Beach Some of the Long Beach traffic volume counts were available from the Los Angeles system (26, 27, 28). The research team obtained additional PDF-based traffic volume data collected in
18 Safety at Midblock Pedestrian Signals 2014 by the Traffic Engineering Division of the City of Long Beach (29). The traffic volume data provided covered only major arterials and were in increments of 100. The traffic volumes in the PDF-based data were provided by link/leg, not by direction. San Francisco The traffic volume dataset for San Francisco was obtained from the San Francisco Municipal Transportation Agency website (30), downloaded in spreadsheet format. The data represented 24Â hours and were collected between 2010 and 2019 from various sources. The data for inter- section and for midblock were available. In addition to the daily traffic counts, the morning and evening peak hour counts by direction of travel were present. San Jose The traffic volume dataset for San Jose was collected from the cityâs website under the Enterprise GIS department (31). Both shapefile and comma-separated values (CSV) formats were available. The data collected are dated as far back as 2005 and were updated annually. The average daily traffic (ADT) counts denoted as âADT Oneâ and âADT Twoâ are given by the corresponding streets. The sum of traffic count on the first and second streets is the total traffic volume for an intersection, which is denoted as âADT.â The direction of streets and the travel direction are provided. Furthermore, the actual date when the traffic count was performed is given. Expanding Short-Term Pedestrian Volume Counts to Daily Values A portion of the California sites had short-term vehicle and pedestrian counts. The research team adjusted the available short-term counts to a daily count using the same procedure used in a recent FHWA project (32) that developed the corner radius crash modification factor for pedestrian crashes at signalized intersections. Several resources were reviewed and considered when developing the adjustment factors. The two sources that influenced the adjustment factors used in this project were NCHRP Research Report 841 (33) for pedestrians and the 2019 Urban Mobility Report (34) for vehicles. An average of the morning and afternoon data for nonfreeway moderate congestion in the 2019 Urban Mobility Report was used to obtain the hourly adjust- ments for vehicles. TableÂ 11 provides the adjustment factor determined and used for the short- term vehicle counts. For the pedestrian counts, data from NCHRP Research Report 841 based on counts made in Charlotte, VA, were used. TableÂ 11 lists the pedestrian count adjustment factors used in this project. FigureÂ 3 illustrates the distribution for both vehicle and pedestrian counts assumed for NCHRP Project 3-141. Estimating Pedestrian Volume Using Census Data Pedestrian counts were only available for 205 of the 469 California sites, with most of those being the control sites. The team used the 205 locations with a count to develop a statistical model to predict pedestrian volume. The model was then used to predict the number of pedes- trians for the remaining 264 sites. The model used demographic factors and roadway feature data to predict the number of pedestrians for a given crosswalk. The research team estimated pedestrian volume for four citiesâLos Angeles, Long Beach, San Jose, and San Franciscoâusing a set of census data variables. The selection of variables for this effort was influenced by findings from previous studies and by the availability of the variable. These variables included population density, employment density, schools and college campuses, bus stops and ridership, intersection density, and restaurant density (see TableÂ 12). The team used shapefile-based data collected from the city and county websites of San Jose (35), San Francisco (36), Los Angeles (37, 38, 39), and Long Beach (40). The research team used ArcGIS tools to determine the quantities for each variable within a certain distance from the site
Safety AnalysisâDatabase Development 19 Hour Vehicle Pedestrian % Veha 6.0 Hrb 7.5 Hr 4.0 Hr 2.0 Hr % Peda 6.0 Hr 7.5 Hr 4.5 Hr 4.0 Hr 2.0 Hr 1.0 Hr 0 0.8 na na na na 0.0 na na na na na na 1 0.4 na na na na 0.0 na na na na na na 2 0.4 na na na na 0.0 na na na na na na 3 0.4 na na na na 0.0 na na na na na na 4 0.4 na na na na 0.0 na na na na na na 5 1.0 na na na na 0.0 na na na na na na 6 3.1 na na na na 6.0 na na na na na na 7 4.6 4.6 4.6 4.6 na 7.0 7.0 7.0 7.0 7.0 na na 8 6.5 6.5 6.5 6.5 na 8.0 8.0 8.0 8.0 8.0 na na 9 5.6 5.6 5.6 na na 7.0 7.0 7.0 3.5 na na na 10 5.2 na na na na 6.0 na na na na na na 11 5.8 na na na 5.8 8.0 na na na na 8.0 na 12 6.3 na na na 6.3 9.0 na na na na 9.0 9.0 13 6.7 na na na na 9.0 na na na na na na 14 6.9 na na na na 8.0 na na na na na na 15 7.5 7.5 7.5 na na 7.0 7.0 7.0 na na na na 16 8.3 8.3 8.3 8.3 na 9.0 9.0 9.0 9.0 9.0 na na 17 8.1 8.1 8.1 8.1 na 8.0 8.0 8.0 8.0 8.0 na na 18 6.3 na 6.3 na na 8.0 na 8.0 na na na na 19 5.0 na na na na 0.0 na na na na na na 20 3.8 na na na na 0.0 na na na na na na 21 3.1 na na na na 0.0 na na na na na na 22 2.5 na na na na 0.0 na na na na na na 23 1.3 na na na na 0.0 na na na na na na % of dayc 100 40.6 46.9 27.5 12.1 100 46.0 54.0 35.5 32.0 17.0 9.0 Adj. Fac.d 1.00 2.46 2.13 3.64 8.28 1.00 2.17 1.85 2.82 3.13 5.88 11.11 a Percentage of vehicles or pedestrians in given hour. b Percentage of vehicles or pedestrians in given hour based on length of initial count in hours. c Percentage of day represented in short-term count. d Adjustment factor to apply to short-term count. na = not applicable. Table 11. Expansion factor for short-term vehicle and pedestrian counts. Figure 3. Daily vehicle and pedestrian distribution used for adjusting vehicle counts.
20 Safety at Midblock Pedestrian Signals of interest. The team used a buffer of 0.5Â mi to extract population density, employment density, schools, and college campuses. A buffer of 0.25Â mi was used to extract quantities for bus stops, bus ridership, intersections, and restaurants. The team did not find all the variables listed in TableÂ 12 for cities other than Los Angeles. Bus ridership was only available for Los Angeles; the bus stop variable was not available for San Jose or Long Beach. Intersection density was also not available for Long Beach. TableÂ 13 presents the descriptive statistics of the variables used to develop the pedestrian volume statistical model. For Los Angeles, the minimum population density is zero. This is the location close to the airport. The census block for this location had zero counts for population density. The research team evaluated various combinations of variables and developed a statistical model to predict the number of pedestrians using a crosswalk. The intention of the model is prediction, not inference, so the model with high prediction capability was selected. TableÂ 14 presents the negative binomial model results for the best-performing model. The model has a low dispersion parameter (0.44) and a low Akaike information criterion (AIC) score. The model results, similar to previous research, suggest that only higher posted speed limit is associated with a lower number of pedestrians, while increases in the following are associated with higher pedestrian volume: vehicle volume, population density, presence of signalized inter- section or more than two legs, and increase in the number of schools, restaurants, and college campuses. Vehicle and Pedestrian Volume Count Data for San Antonio, TX Vehicle Volume The Texas Department of Transportation (TxDOT) has a traffic web viewer where traffic data from all 25 districts are stored (41). The data were collected between 2001 and 2020 in urban and rural areas. The annual average daily traffic (AADT) data span 21Â years (2001â2020), while urban traffic volume data span 5Â years (2016â2020). The AADT data can be extracted by clicking on a point at a specific location. The details of the district, county, and traffic count show up for urban counts. For annual counts, the details for the district, county, and AADTs for each year show. The research team extracted the AADT data for the most recent years to match the crash data. Pedestrian Volume A previous TxDOT project (42) collected pedestrian counts at several locations. In most cases, the counts were for a few hours, and were expanded to daily counts using adjustment factors Variable Description Population density Number of people normalized per square mile within 0.5 mi Employment density Number of employments within 0.5 mi Schools Number of high schools, middle schools, and elementary schools within 0.5 mi College campuses Number of college campuses within 0.5 mi Bus stops Number of bus stops within 0.25 mi Bus ridership Yearly (2020) bus ridership at the bus stops within 0.25 mi Intersection density Number of intersections within 0.25 mi Restaurant density Number of restaurants within 0.25 mi ADT Annual daily traffic (vehicles per day) Signalized crosswalk Whether a crosswalk is signalized (e.g., MPS, PHB) Number of legs Number of intersection legs Posted speed limit Posted speed limit (mph) Table 12. Description of variables used to develop pedestrian volume model.
Safety AnalysisâDatabase Development 21 City Variable Minimum Maximum Average Standard Deviation Los Angeles Population density 0 342,263 149,430.0 82,801.0 Employment density 0 28,408 15,128.0 5,825.0 Schools 0 13 1.8 1.9 College campuses 0 4 0.6 0.9 Bus stops 0 119 52.5 34.4 Bus ridership 0 152,645 35,188.0 49,353.0 Intersection density 1 61 25.7 10.2 Restaurant density 0 61 76.8 66.1 Speed limit 25 45 29.5 5.2 ADT 3,515 127,230 2,6442.0 16,638.8 Signalized 0 1 0.9 0.3 Over two legs 0 1 0.6 0.5 San Francisco Population density 15,606 66,348 30,857.0 10,159.0 Employment density 638 31,702 5,657.2 7,947.9 Schools 0 19 4.6 4.0 College campuses 0 11 1.7 3.0 Bus stops 21 129 49.5 26.3 Intersection density 8 128 45.8 28.7 Restaurant density 1 255 62.5 72.5 Speed limit 15 35 24.9 6.2 ADT 62 28,640 9,224.0 7,168.8 Signalized 0 1 0.9 0.2 Over two legs 0 0 0.0 0.0 San Jose Population density 6,631 32,909 16,145.0 5,897.0 Employment density 1 56 19.7 17.2 Schools 0 8 2.6 2.1 College campuses 0 1 0.3 0.4 Intersection density 1 45 20.3 8.7 Restaurant density 0 38 18.9 12.7 Speed limit 20 40 27.9 5.4 ADT 310 15,094 4,480.0 3,761.7 Signalized 1 1 1.0 1.0 Over two legs 0 0 0.0 0.0 Long Beach Population density 61 24,549 14,232.8 6,219.5 Employment density 0 8 5.7 2.7 Schools 0 8 3.2 2.1 College campuses 0 2 0.5 0.8 Restaurant density 0 117 51.2 43.7 Speed limit 25 40 29.6 3.8 ADT 2,021 27,500 8,419.0 8,418.6 Signalized 0 1 0.7 0.5 Over two legs 0 0 0.0 0.0 Variable Estimate Std. Error Z-Statistic P-Value Intercept 3.231 1.002 3.23 0.001 ln(ADT) 0.246 0.107 2.29 0.022 Posted speed limit (mph) â0.034 0.013 â2.63 0.009 Signalized intersection (Yes) 1.206 0.216 5.58 < 0.001 Over two legs (Yes) 0.719 0.243 2.96 0.003 Population density (in 1,000s) 0.046 0.008 5.45 < 0.001 Number of schools 0.104 0.028 3.74 < 0.001 Number of restaurants 0.069 0.012 5.99 < 0.001 Number of college campuses 0.264 0.114 2.32 0.020 Model summary: number of observations = 205, dispersion parameter = 0.44, and AIC = 3,743. Table 13. Descriptive statistics of census-based and roadway variables used in pedestrian volume model. Table 14. Negative binomial regression results for pedestrian volume model.
22 Safety at Midblock Pedestrian Signals developed by the research team based on nearby counts conducted for 12Â hours. These locations, along with their estimated daily pedestrian counts, were included in this NCHRP 3-141 study. Of the 292 Texas sites, 40 (14%) did not have pedestrian counts. The pedestrian volume for these 40 sites was estimated using local knowledge (see TableÂ 10). Vehicle and Pedestrian Volume Count Data for Utah Vehicle Volume The AADT data in Utah are collected and stored by the Utah Department of Transportation (UDOT) in web-based format on its open data portal (43). The data are available in multiple formats including CSV and shapefile. The AADT can be extracted by clicking the link of interest. Overall, the AADT data for Utah cover over 30Â years (1981â2019); however, in most cases 5Â years of AADT data (2015â2019) are available. Each link has the route name, starting mile- post, ending milepost, and AADT counts for each year in descending order. The research team extracted the AADT data for the most recent years to match the crash data available. Pedestrian Volume The team collected pedestrian volume data from two geographic information system (GIS)- based maps: one with estimated pedestrian counts for about 1,400 signalized intersections (44) and the other with about 62,000 intersections (45). The data for 1,400 signalized intersections were estimated based on a full year of push-button event data. The data for 62,000 intersections were predicted based on a model developed using the surrounding built environment at the intersections and the pedestrian activities obtained in the 1,400 intersections. The research team overlaid the Utah treated (i.e., pedestrian crossing) and control sites on the GIS-based pedestrian counts file. A buffer of 50Â ft was used to identify potential pedestrian counts for the 138 sites. First, the data for 1,400 signalized intersections were used, which facilitated obtaining pedestrian counts for 28 sites. The data from the 1,400 signalized intersections layer were of more interest to the team because they had fewer mathematical models/estimations involved. The data from the 62,000 intersections layer then facilitated obtaining pedestrian volume for an additional 38 sites. Since the remaining 72 sites did not match any estimated pedestrian volumes for either available 1,400-signalized-intersections layer or 62,000-intersection layer, the research team estimated the volume based on pedestrian volumes from two adjacent inter- sections using the 62,000 intersections layer. If the site of interest was an intersection, the average pedestrian volume of the two adjacent intersections was considered. If the site of interest was a midblock crosswalk, half of the average pedestrian volume of the two adjacent intersections was considered. Roadway Characteristics Data Collection The research team assembled a spreadsheet with one record for each intersection or midblock pedestrian crossing. The team used aerial and street-level photography sources available online to extract the following observations to describe each site. â¢ Location (latitude and longitude coordinates of the intersectionâs center or midblock cross- walkâs center) â¢ Type of traffic control present at the site (see TableÂ 15) â¢ Number of legs (two, three, or four) â¢ Main street posted speed limit â¢ Main street traffic configuration (two-way or one-way)
Safety AnalysisâDatabase Development 23 â¢ Main street presence of bicycle lane (yes or no) â¢ Main street presence of on-street parking â¢ Main street number of lanes on each leg â¢ Main street total crossing distance (includes the width of the median when present) â¢ Main street type of median treatment â¢ Main street presence of advance stop or yield lines (yes, no, or NA if at a signal or stop-control approach) â¢ Driveway within 100Â ft of the site on the main street (yes or no) Data Refinements The team reviewed historical aerial views to determine the earliest date pedestrian-related traffic control was present, back to JanuaryÂ 1, 2014, which was the earliest date for crash data the team anticipated using. Also gathered was the date for the end of the evaluation period, which is the most recent date that the pedestrian-related traffic control was present, up to DecemberÂ 31, 2020. Part of the process for developing the database was the decision of whether a site should be considered a midblock or an intersection. If a traffic control signal head was present on the minor approach (either a street or a driveway), the site was considered an intersection. The variables in the initial list of roadway characteristics were refined during the preliminary analyses to develop the variables used in the final analysis. TableÂ 15 provides the description of the pedestrian treatments considered and how they were later grouped; TableÂ 16 provides the descriptions of the other variables considered in the analyses. Nearby Driveways For the potential midblock (i.e., two-leg) sites, the distances between the marked crosswalk and the nearest driveway in both directions were gathered. A note was made if the distance was greater than 100Â ft. For the two-leg sites with less than 100Â ft between a driveway and the marked crosswalk, the research team conducted an additional review to decide if the signalized location should be considered an intersection with three or four legs or a midblock (two-leg) location. As a reminder, all these sites had signal heads on the main street only; no signal head was present for the driveway. When the driveway did not have a signal head and was anticipated Traffic Control Pedestrian Treatment at Crossing Group for Type of Control Description CBoverhead-24/7 Yellow Device Yellow circular beacon overhead, flashing 24/7 CBoverhead-PedAct Yellow Device Yellow circular beacon overhead, pedestrian activated CBroadside-24/7 Yellow Device Yellow circular beacon roadside, flashing 24/7 CBroadside-PedAct Yellow Device Yellow circular beacon roadside, pedestrian activated CW&Sign Grey Device Crosswalk pavement markings and crossing warning sign CW_only Grey Device Crosswalk pavement markings only LED-Em Yellow Device Yellow LEDs embedded in the sign LED-Em & Flags Yellow Device Yellow LEDs embedded in the sign with flags MPS Red Device Midblock pedestrian signal NoPedTCD Grey Device No pedestrian traffic control devices (no crosswalk markings or sign, etc.) PHB Red Device Pedestrian hybrid beacon RRFB Yellow Device Rectangular rapid flashing beacon located on roadside RRFB-Overhead Yellow Device Rectangular rapid flashing beacon located overhead Signal Red Device Traditional traffic control signal (green, yellow, and red indications) Stop-AllWay Red Device All approaches have stop control Stop-Cont Red Device Stop sign at crosswalk Stop-ContwCB Red Device Stop sign at crosswalk with circular beacon Stop-OneWayTraffic Red Device Stop control at crosswalk with one-way traffic Table 15. Description of traffic control devices used at treated and control sites.
24 Safety at Midblock Pedestrian Signals to have minimal volume, it was believed to have nominal influence on operations and safety at that location and was kept in the database as a midblock signal. Additional characteristics of the driveway used to make the decision were if the driveway had one-way operation, did not have a stop sign, or was gated. For the 193 MPS sites included in the database, the distance by groups (generally 25-ft increments with the smallest group being 15Â ft and the largest group being all sites with more than 100Â ft) to the south or west and north or east direction of the marked cross- walk is shown in FigureÂ 4. The majority of the MPS sites in the database did not have a driveway within 100Â ft of the marked crosswalk. Number of Study Sites A total of 899 sites were initially available for the statistical analysis. This number decreased to 892 because sites were removed due to issues identified, for example, being so close to a free- way that it was difficult to identify the nonfreeway crashes (these sites could be on a street that was under- or overpassing a freeway, or next to a freeway). TableÂ 17 provides the descriptive statistics for those variables with a dimension, and TableÂ 18 shows the number of intersections for variables with specified levels. Variable Name Description I:Legs Intersection: number of legs at the intersection (2, 3, or 4) I:TreatOrCon Intersection: treated or control site Main:#Ln Main Street: count of number of lanes being crossed by a pedestrian Main:AdvLines Main Street: are advance stop lines present (yes or no or NA for signals) Main:BikeLn Main Street: is a bike lane present (yes or no) Main:Driveway Main Street: is there a driveway within 100 ft of the MPS (yes or no) Main:MedType Main Street: type of median treatment (not applicable because one-way street [NA_OWS], none, raised, RRtracks/flush, RRtracks/raised, or two- way left-turn lane [TWLTL]) Main:OW-TW Main Street: one-way (OW) or two-way (TW) traffic (OW or TW) Main:ParkLn Main Street: is a parking lane present (yes or no) Main:PSL Main Street: posted speed limit (mph) Main:PSLGroups Main Street: posted speed limit groups of 25 and less, or 30 and more Main:TotCrossDis Main Street: curb to curb distance (include median) (ft) V:IntersecEnterVol(ADT) Volume: average daily volume of vehicles entering the intersection or pedestrian crossing from all approaches V:Ped (ADP) Volume: average daily pedestrian count (or estimated pedestrian count when pedestrian count is not available) Table 16. Geometric variable descriptions. Figure 4. Number of MPS sites by distance between marked crosswalk and nearest driveway.
Safety AnalysisâDatabase Development 25 T or Ca Variable Nameb Values CA TX UT All States T Count Number 150 11 32 193 Main:TotCrossDis (ft) Min. 30 40 39 30 Average 62 49 75 63 Max. 130 66 113 130 V:IntersecEnterVol (ADT) Min. 2,900 3,999 2,500 2,500 Average 15,890 11,012 14,713 15,417 Max. 51,166 13,128 42,000 51,166 V:Ped (ADP) Min. 15 59 25 15 Average 2,420 341 165 1,927 Max. 22,523 652 501 22,523 C Count Number 313 280 106 699 Main:TotCrossDis (ft) Min. 20 22 24 20 Average 60 63 79 64 Max. 130 163 127 163 V:IntersecEnterVol (ADT) Min. 62 309 1,000 62 Average 25,035 21,691 20,292 22,976 Max. 127,230 66,990 81,000 127,230 V:Ped (ADP) Min. 28 9 10 9 Average 4,028 637 483 2,132 Max. 25,851 14,907 4,519 25,851 a T = treated sites, C = control sites. b See description in Table 16. Table 17. Descriptive statistics of sites for database with 892 sites. Variable Namea Level Tb-CA T-TX T-UT T C-CA C-TX C-UT C I:Legs 2 150 11 32 193 89 60 40 189 3 0 0 0 0 17 19 6 42 4 0 0 0 0 207 201 60 468 Main:AdvLine NA 150 11 32 193 212 220 61 493 No 0 0 0 0 39 52 27 118 Yes 0 0 0 0 62 8 18 88 Main:BikeLn No 90 10 10 110 185 234 48 467Yes 60 1 22 83 128 46 58 232 Main:MedType NA_OWS 27 4 0 31 57 3 0 60 None 78 6 4 88 221 153 44 418 Raised 25 1 8 34 24 41 23 88 RRtracks/flush 0 0 1 1 0 0 10 10 RRtracks/raised 6 0 11 17 0 0 17 17 TWLTL 14 0 8 22 11 83 12 106 Main:OW-TW OW 27 4 0 31 57 24 0 81TW 123 7 32 162 256 256 106 618 Main:ParkLn No 33 8 16 57 110 257 42 409Yes 117 3 16 136 203 23 64 290 Main:PSL 15 0 0 1 1 9 0 2 11 20 2 0 8 10 6 0 25 31 25 76 0 3 79 148 0 31 179 30 27 5 7 39 56 89 19 164 35 36 5 9 50 77 95 19 191 40 8 1 2 11 15 63 5 83 45 1 0 2 3 2 31 4 37 50 0 0 0 0 0 2 1 3 Main:PSLGroups 25andLess 78 0 12 90 163 0 58 22130andMore 72 11 20 103 150 280 48 478 Grand Total All 150 11 32 193 313 280 106 699 a See description in Table 16. b T = treated sites, C = control sites. Table 18. Number of intersections by level within variable for database with 892 sites.
26 Safety at Midblock Pedestrian Signals Crash Data The research team acquired crash data from databases available in each state. Because one of the state databases only had fatal and injury (FI) crashes available, the analysis did not use property-damage-only (PDO) crashes. This section provides detailed explanations of the data- bases and key variables of interest for this study. Californiaâs Transportation Injury Mapping System Database The data for all California cities included were from the Transportation Injury Mapping System (TIMS) website hosted by the University of California, Berkeley. TIMS only includes injury crashes in the database, no PDO crashes. All crashes in the Statewide Integrated Traffic Records System are geocoded, making it easy to map crashes. Access to this web-based database requires an account. This database facilitated the acquisition of crash data for Los Angeles, Long Beach, San Jose, and San Francisco. To get crashes for a specific city and time and the participants involved, the research team used features on the web-based database to query for these attributes. The obtained dataset was exported in CSV format. Among the variables of interest were crash date, crash time, and geocoordinates. Also of interest were the variables that indicated pedestrian- or bicyclist-involved crashes and injury severity. Furthermore, information related to collision type, pedestrian action, and lighting condition were extracted from the crash data. Utahâs Numetric Database The Numetric database is hosted by UDOT and contains crash data covering over 11Â years (2010â2020). Access to this web-based database was granted by UDOT personnel. The research team created an account and was able to enter the website and make queries to obtain crash data of interest. The team extracted the variables listed in TableÂ 19. Each crash observation contains the geocoordinates of the crash so all crashes could be easily mapped. Variable Description Crash ID Unique crash identifier Crash Date Time Crash date and time Year Crash year Full Route Name Route name where crash occurred Milepoint Milepost where crash occurred Crash Severity Severity outcome of a crash Manner of Collision Manner of crash collision, which includes rear end, single vehicle, angle, sideswipe, etc. Roadway Junction Type The roadway junction type, which includes 3- and 4-leg intersections, ramps, bridges, etc. Light Condition The lighting condition, including daylight, dark-lighted, dark unlighted, dusk, etc. Weather Condition Weather conditions (rain, snow, fog, etc.) Roadway Surface Condition Roadway surface conditions (dry, wet, ice/frost, etc.) Number of Vehicles Involved Number of vehicles involved in a crash Route Type State route, local road, federal road, or unknown Region Regions as defined by UDOT (Region 1, Region 2, Region 3, or Region 4) County County where a crash occurred City City where a crash occurred Pedestrian Involved Whether a crash involved pedestrian (Y for yes and N for no) Bicycle Involved Whether a crash involved bicyclist (Y for yes and N for no) Latitude Latitude coordinates Longitude Longitude coordinates Table 19. Variables from UDOT crash database.
Safety AnalysisâDatabase Development 27 TxDOTâs Crash Records Information System Database Crash data for San Antonio, Texas, were collected from in-house sources at the Texas A&M Transportation Institute with access to TxDOTâs Crash Records Information System (CRIS). From CRIS, the research team selected information related to the date and time of the crash, location (latitude and longitude), pedestriansâ and bicyclistsâ involvement, weather and lighting conditions, manner of collision, and injury severity for further analysis. Period for Crash Data Because pedestrian crashes are rare, the research team obtained 7Â years of crash data. Crash data for 2014 to 2020 were pulled from each stateâs database. Upon discussions with city staff familiar with the sites, along with ongoing research into the impacts of the pandemic, the research team decided to focus on a prepandemic time frame. The crash data represented JanuaryÂ 2014 to MarchÂ 2020. Dates for a site were adjusted if historical aerial or street views identified major changes at the site (e.g., treatment installed or construction). Identifying Crashes for a Given Location/ Database Cleaning To identify crashes associated with the site of interest, the team used a buffer distance of 250Â ft around the intersection or around the marked crosswalk for the two-leg sites. The 250-ft distance has traditionally been used to screen intersection-related crashes. Distances greater than 250 ft can yield better crash modification factor (CMF) estimates but are likely to include nonintersection crashes and crashes that occurred at adjacent intersections. On the other hand, distances less than 250Â ft are likely to underrepresent the intersection crashes (46). Thus, any crash that occurred within 250Â ft of the site of interest (intersection and midblock) was initially associated with that site. FigureÂ 5 illustrates crashes associated with an intersection, showing the crash pattern along the approaches and away from the center of the intersection. If the sites identified in this study were within 500Â ft of each other, there was a possibility of assigning the same crash to both sites. To avoid this duplication, the team calculated the distance between the crash and sites based on the geocoordinates and assigned it to the nearest site. This assignment could not be implemented at the sites where adjacent intersections were within 500Â ft but were not included in the study database, mainly because the distance could not be calculated due to unavailability of location information. Other steps were taken to help address this concern. The crashes at the study sites were reviewed to identify general trends and whether more detailed cleaning was needed. The review identified sites familiar to the research team with a higher-than-expected number of crashes. Because of the nature of assigning crashes within 250Â ft of the latitude and longitude coordinates, crashes on close roads could be included. For example, the site shown in FigureÂ 6 had several freeway crashes assigned to the midblock crossing using the 250-ft radius. The distance of each crash from the center of the intersection, or the pedestrian crosswalk, was determined. The research team visually reviewed all sites where most of the crashes were more than 200Â ft from the intersection. For these sites, the research team then determined if the 250-ft radius should be adjusted to better reflect the conditions present at the site. A 150-ft radius rather than the 250-ft radius was used for about 3% of the sites (26 of the 899 sites).
28 Safety at Midblock Pedestrian Signals Figure 5. Example of crashes at an intersection. Figure 6. Example of crashes on a freeway being assigned to a midblock crossing.