Changing the Future? Development and Application of Pedestrian Safety Performance Functions to Prioritize Locations in Seattle, WA

Transcription

1 Changing the Future? Development and Application of Pedestrian Safety Performance Functions to Prioritize Locations in Seattle, WA Libby Thomas* thomas@hsrc.unc.edu Highway Safety Research Center, University of North Carolina 0 Martin Luther King Jr Blvd, Suite 00 Chapel Hill, NC Bo Lan lan@hsrc.unc.edu Highway Safety Research Center, University of North Carolina 0 Martin Luther King Jr Blvd, Suite 00 Chapel Hill, NC Rebecca L. Sanders rsanders@tooledesign.com Toole Design Group 1 SW Washington Street, Suite 00 Portland, OR , ext. 1 Alexandra Frackelton afrackelton@tooledesign.com Toole Design Group 1 st Avenue, Suite 00 Seattle, WA 0..1, ext. 01 Spencer Gardner sgardner@tooledesign.com Toole Design Group 1 N. Carroll Street, Suite 00 Madison, WI , ext. 0 Michael Hintze mhintze@tooledesign.com Toole Design Group Colman Building 1 st Avenue, Suite 00 Seattle, WA 0..1, ext. 01 1

2 ABSTRACT We aimed to use robust pedestrian safety analysis methods to identify and screen locations at risk for future pedestrian crashes and injuries to help Seattle, WA broaden treatment priorities beyond only high crash locations. To accomplish this objective, we developed safety performance functions (SPF) for two high-frequency and/or high-severity pedestrian crash types at Seattle intersections: total pedestrian crashes, and crashes involving straight through motorists striking crossing pedestrians. Using data from the entire network, we tested a large number of variables measuring pedestrian activity, roadway, and the built environment. Many significant variables were similar in both models and included a number of measures of activity and intersection size and complexity. Pedestrian volume exhibited a curved relationship to crashes, demonstrating the tendency for expected crashes to begin to decline above a threshold value; however, the causes of this relationship are unknown. The SPFs were used in several ranking methods, including SPF-predicted crashes and empirical-bayes (EB)-estimated crashes, to aid in prioritization of locations that might be candidates for safety improvement. The analyses and associated ranking methods provide a means of prioritizing locations beyond crash hot spots, based on model predictions of locations that may be hazardous for pedestrians. Based on this example, this is an important, and feasible approach for jurisdictions to consider that wish be more proactive in addressing the potential for future pedestrian crashes and injuries. Jurisdictions must, however, begin routinely collecting or estimating the data, including pedestrian activity and motor vehicle traffic, needed to implement the method efficiently. INTRODUCTION Pedestrian crashes tend to predominate where more people walk, but may also occur in low numbers at many locations across a network. The often dispersed nature of pedestrian-motor vehicle crashes can impede the identification of the patterns and problem types that are associated with crash occurrence, and the prioritization of safety improvements that may yield most benefit. Regression to the mean (RTM), a predictable phenomenon, whereby crashes tend to decrease at high crash locations and increase at low crash locations, may also contribute to locations suffering rather random crashes being identified for treatment (1). Also, if only prior crash hot spots are treated, a large number of locations that can be expected to have crashes in the future will remain untreated, and locations that may have experienced crashes due largely to random events may be treated instead. For these reasons, many jurisdictions, including those committed to Vision Zero, are seeking problem identification and prioritization methods that go beyond traditional crash hot spot methods to identify locations that are at risk of future pedestrian crashes. Hazardous sites may be somewhat predictable if analysts and practitioners use systemwide data that links crashes to locations and their associated characteristics for crash frequency prediction analysis. The results of these multivariate analyses, known as Safety Performance Functions (SPFs), can then be used to predict expected crashes and rank locations based on the predictions (with or without prior crash considerations). This practice may help reduce the focus on locations apt to suffer random variation in crash frequencies (). The use of data from many locations across the network to identify and proactively treat locations with similar risks has also been called a systemic approach to road safety (). This approach, which uses data from many locations to identify risks, may be especially useful for pedestrian safety, since, as mentioned, pedestrian crashes typically occur in low numbers at any one location.

3 This paper describes the development of SPFs for two pedestrian intersection crash types using existing crash and other data compiled for Seattle, WA for Vision Zero pedestrian and bicycle safety analyses. We also describe what, as far as we are aware, is the first case example of the application of pedestrian SPF model results to rank and help prioritize locations city-wide for further safety investigation. We describe multiple ranking procedures making use of the SPFs and the rationale for their use, and how the results may be used by the City to aid in prioritizing potential improvments. PREVIOUS STUDIES AND CONTEXT OF THIS WORK Relatively few crash prediction models, also known as SPFs, exist for pedestrian facilities or crash types. These models use roadway, traffic, and sometimes built environment and neighborhood characteristics to identify elements associated with prior crashes. Pedestrian and auto traffic volumes have been identified as important predictive factors for pedestrian crashes with motor vehicles (). Accordingly, it is important to account for pedestrian activity and motorized traffic flows in safety analyses that are intended to predict and rank locations for treatment. It is also important to account for pedestrian and motorized traffic in analyses that are intended to help identify other risks related to roadway design and operations and area characteristics that may suggest appropriate remedies. The lack of pedestrian volume or count data, in particular for specific locations across a network, is likely a key reason that this approach has not been more widely adopted to date, but this appears to be changing. Traffic volume data are also not always uniformly available, particularly for non-arterial roadways. Lyon and Persaud demonstrated that, as for motor-vehicle only crashes (), the relationships between pedestrian crashes and pedestrian and motor vehicle volumes are nonlinear; thus, modeling pedestrian crash rates, which assumes a linear relationship, is inappropriate (). They also suggested that it is fine to use correlated variables to predict crashes, as long as including them improves estimation accuracy. Torbic et al. combined databases from Toronto, Canada, and Charlotte, NC, that were used to develop SPFs for the first edition of the Highway Safety Manual, and found that average annual daily traffic (AADT) and pedestrian volume (AADP) are significant factors at three to four-leg signalized intersections (). Other significant predictors included maximum number of lanes crossed by a pedestrian in any one crossing maneuver, presence of bus stops, average neighborhood income, and number of commercial structures. Schneider et al. studied 1 urban signalized and unsignalized intersections in Alameda County (Oakland) California (). The significant predictors again included pedestrian and traffic volumes as well as presence of a median on either the main road or cross street (decreased crashes), presence of dedicated right turn lanes, non-residential driveways within 0 feet, number of commercial properties within 0.1 mi, and the proportion of residents within 0. mi under age 1. Miranda-Moreno et al. studied the relationship of land use, demographic and roadway factors in terms of predicting both activity levels and pedestrian-motor vehicle crashes at 1 intersections in Montreal (). These researchers tested multiple land use and sociodemographic measures in models to predict pedestrian flows and separate models to predict crashes. The authors concluded that most built environment and socio-economic (SES) variables contribute to

4 crash risk via their associations with pedestrian activity. However, transit stops and commercial property density contributed additionally to crash prediction. Presence of schools close by was associated with reduced crashes. Strauss et al. used bivariate Bayesian Poisson models to model the effects of variables on traffic, pedestrian and bicycle flows, and pedestrian, bicyclist, and motorist safety outcomes in separate models for urban signalized, and urban non-signalized intersections in Montreal (). They used the model results to predict the expected changes in pedestrian, cyclist, and motorist injuries for given changes in flows among these groups. Significant predictors for signalized intersections included AADP and AADT, an all red phase or half-red walk phase for pedestrians, and the presence of commercial driveways. At unsignalized locations, only AADP and AADT were identified as significant predictors (). This research builds on these earlier studies and describes the development and application of SPFs for two types of pedestrian collisions conducted for the Seattle Department of Transportation (SDOT) as part of the City s Vision Zero effort. In this study, we tested a number of roadway, built environment, and socioeconomic (SES) variables in addition to pedestrian volume estimates in the SPF development process. While we lack detailed insight or a hypothetical framework about the nature of exposure risk, which could in fact may vary in different environments and situations, at present, AADP (average annual daily pedestrians) and AADT (average annual daily traffic) are the most widely used measures to account for crash risk related to these interactions. However, other types of measures of pedestrian and motor vehicle volume or activity may also account for pedestrian risk exposure to crashes. Although we included ballpark AADP estimates to account for pedestrian activity, we also tested SES and built environment measures that are highly correlated with pedestrian activity in order to: 1) provide alternatives or complements to imperfect volume estimates; and ) account for different types of pedestrian crash risk that volumes alone may not fully capture. Similarly, we used multiple measures that may account for the lack of network AADT data. Miranda-Moreno et al. () explorations of the interactions among pedestrian activity correlates, pedestrian activity and crashes and found much overlap among these measures so our team felt it was useful to consider that several measures might improve crash prediction. For both crash types, we performed aggregate analyses for all intersections across the City, which may be the first time network-wide data have been used to develop pedestrian SPFs. Risks for pedestrians may be associated with some of the conditions, such as presence of signals or arterial classes, that are frequently used to distinguish location types in analyses of collisions, and we did not want to mask these potential issues by conducting disaggregate analyses on different facility types such as arterial intersections only or signalized intersections. We also describe an example application of how these predictive models are being used to help prioritize locations for pedestrian safety improvement in a more robust, and we believe, also systemic approach to pedestrian safety screening. While these SPFs are primarily intended for prediction and screening, we also hoped that the identification of roadway and built environment predictors beyond traffic and pedestrian volumes, could aid in comprehending some of the other risks that might be relevant for countermeasures identification.

5 DATA AND METHODS The analyses were carried out in two stages: a descriptive analysis phase, and the SPF development phase. For the first phase, we developed a crash database and conducted descriptive analyses to understand where and when pedestrian crashes were occurring, what factors seemed to be over-represented in more severe (fatal and serious injury) crashes, and to identify high frequency and/or more severe crash type and location scenarios for additional analysis in the second phase. In the second phase, we developed a comprehensive intersections database and tallied frequencies of multiple types of pedestrian and bicycle crashes that occurred at each intersection. We analyzed factors related to two pedestrian crash types. For both phases, we used eight years of police-reported crashes (00-01). Data and Descriptive Analyses Seattle has a comprehensive relational crash and roadway database (collision and streets data files) that include spatial linkages among crashes, segments and intersections, with lane types, widths, signalization, and other roadway descriptor variables. For the descriptive phase of analyses, we analyzed crash variables, along with a limited set of linked roadway variables. We used eight years of reported crashes in our analyses. Typically, three to five years is recommended to reduce the chance of changes in infrastructure or other trends that may affect results. However, with orders of magnitude fewer pedestrian crashes per year compared with motor vehicle crashes, more years were desirable to increase the chances of successful model development. There were a total of, pedestrian traffic-related collisions for all eight years;,0 of these occurred at intersections. In Seattle, an average of.% () of all the collisions, or nearly 1% of those with known injury status, resulted in fatal or serious injuries to the pedestrian. While this may seem to be a low percentage of fatal and serious injury crashes to some readers, severity distributions can vary from one jurisdiction to another and over time for a variety of reasons, including severity definitions, minimum reporting thresholds, and variations in reporting practices, as well as actual crash severity outcomes. Since pedestrian crashes tend to be under-reported in general, the study team determined to use all severity of pedestrian crashes in phase analyses, even those with no reported injuries to the pedestrian. Crash Types Coded from Existing Variables We used a combination of variable relationships to crash frequency and severity to determine the focus crash types, as recommended for systemic approaches to safety (). Pedestrian crash types describing pre-crash events are frequently lacking in most crash databases (), and were unavailable for Seattle as well, so we developed unique pedestrian crash types by combining categories for variables that captured motorist collision type and pedestrian actions into descriptors of distinct crash events. We ultimately aggregated crash types into related groups linked to either intersections or segments to have sufficient crashes for multivariate analysis. Figure 1 illustrates the relationship of the subject crash locations and types, along with severity information. We focused on the following crash types in multivariate modeling:

6 Ptot_int All reported pedestrian-motor vehicle collisions at intersections. Intersection crashes represented more than two-thirds of all pedestrian crashes (%) and nearly 0 percent of fatal or serious injury (severe) crashes, and were selected for further analysis. PXing_int Motor vehicle traveling straight, pedestrian crossing at intersection. There were crashes of this type, accounting for 1.% of all pedestrian collisions and % of severe pedestrian injury crashes. This crash type was selected because of the sizable number and the large proportion of severe injuries Figure 1. Pedestrian- Motor Vehicle Crashes in Seattle Data, K = Killed, SI = Serious Injury to the pedestrian. Focus crash types for these analyses are highlighted with shading. Intersections Database For the multivariate analyses, we developed a comprehensive intersection database with all intersections of three or more legs. In addition to the crash and roadway data, we also used the following data sources to develop variables for the multivariate analyses: Generalized land use (City of Seattle) Building footprints (City of Seattle) Census blocks and demographic/employment data (US Census) National Elevation Dataset (US Geological Survey) University locations (City of Seattle) Schools (City of Seattle)

7 Short-term, quarterly and continuous user count data (City of Seattle, used in exposure estimatation only) Transit stop location and schedule data (Google Transit Feed Specification, Sound Transit (dataset includes King County Metro and any other transit providers in the region) Table 1 shows the distribution of categorical roadway and other variables developed for analysis. TABLE 1 Number of Intersections (+leg) by Categorical Variables Available for Analysis Variable Name and Description Artcls_cat Highest Arterial Class entering at intersection signal_bin Controlled by traffic signal Tcircle Mini traffic circle Biglanes_cat Total number of motor vehicle lanes on "largest" approach leg (leg with most lanes) (1) Biglanes_thru_cat Sum of thru motor vehicle lanes on largest leg (1) legs_cat Number of legs present legs_unklane_cat Number of legs that are local streets [converted to proportion of legs that are local streets for the NB models] vehlanes_cat Total number of motor vehicle travel lanes for all legs (1) vehlanes_thru_cat Sum of thru motor vehicle travel lanes on all legs (1) parking_bin Parking on any legs of intersection buslanes_bin Bus- only designated lanes on any approach Category Number of Intersections Percentage (%) Major Art- interstate,0 1 Minor Art,01 1 Collector 1, 1 Local / Access, No, 1 Yes 1,0. No, Yes 1,01. 1-,01-1, , - 1 1, 1,0, >= ,1 1, 1, -,1 -,01 -,00 1 -,0 1 -,, , 1 No, 0 Yes, 0 No 1,00 Yes.1

8 Variable Name and Description Category Number of Intersections Percentage (%) med_bin No, Painted or raised median on any approach Yes 1. vehlanes_rt_bin No,0 Right turn lanes on any approach Yes. vehlanes_lt_bin No, Presence of left turn lanes on any approach Yes. vehlanes_ct_bin No, TWLTL on any approach Yes. bikelanes_bin No,1 1 Any type bike lanes from any direction Yes 1,1.1 bike_tracksway_bin No 1,1 0 Any type of two- way cycle track Yes 0. approach_markings No,1 Bicycle shared lane markings on any approach Yes 1, 1 approach_bike_lane No,1 1 Bike lane on any approach to the intersection Yes 1,1. Schools No, 1 One or more K- 1 schools within 0. mi of intersection Yes,1 Univ No,. University campus within 0. mile of intersection Yes 00. trail_int No 1, 0 Shared- use path crossing at the intersection Yes 1 0. Total intersections, + legs used in analysis 1, 1 Undesignated or local streets did not have lane count data, but since the vast majority (%) function as two- way thoroughfares, we counted two lanes for each undesignated street entering at an intersection. Exposure Estimates Ballpark estimates of pedestrian (AADP) and bicyclist (AADB) volumes were developed for use as predictors in these models, as described in Sanders et al. (). AADP per intersection was estimated using manual intersection count data from 0 intersections provided by the City of Seattle. These largely peak period counts were annualized using factors developed from a similar city, San Francisco, and included time of day, day-of-week, and land use (). The study team gathered data equivalent to variables previously found to significantly influence pedestrian volumes and developed used Poisson regression analysis to determine which of those variables

9 were significant predictors of AADP for the 0 locations. More details are provided in Sanders et al.(). The final pedestrian model resulted in a Pseudo R of 0. (p < 0.001). The mean value for the ratio between the estimated to the annualized counts was 1. with an overall standard deviation of 0.. The model was then used to project estimates of AADP for all intersections in the database. Bicyclist exposure (AADB) was also estimated, but in this case, using bicycle counts at locations selected by SDOT to include a variety of bicycle volume conditions (). These bicycle counts were annualized by the City, based on City-developed factors that accounted for day and time period, and using data derived from short-term and long-term, automated count data from locations. Similar to the pedestrian modeling process, variables that had previously been found to significantly influence bicycle volumes were tested in models against the annualized bicycle volume estimates. Variables already calculated for the pedestrian model, modified to apply to a segment were also tested. Because SDOT had already purchased Strava data, we added it to the modeling process to examine its impact on prediction. Despite the researchers concerns about potential sample bias, using the Strava data considerably improved the model fit to the counts data. The final Strava adjusted model had a Pseudo R of 0. (p < ). The mean ratio of estimated to annualized counts from Seattle was 1., and the overall standard deviation was 1.0. The bike model was used to estimate AADB for all segments across the network. To generate intersection estimates, estimates for each segment were halved and apportioned to the intersection on either end (). Variables describing socio-demographics and the environment around each intersection that were developed for deriving the AADP and AADB estimates were also tested in the crash prediction models, in particular those that did not prove to be statistically significant predictors of pedestrian and bicycle activity. When there were measures aggregated at several scales around the intersection, we tested the measures at the spatial scale nearest the intersection as potentially being most related to crash risk. Table shows the range and mean values of AADP and AADB estimates and other scale measures that were tested as crash predictors. Crash count data are also shown in Table. There were, total pedestrian crashes linked to intersection coordinates among the 1, intersections. Intersections experienced from 0 to 1 pedestrian-motor vehicle crashes (Ptot_int), with 1, experiencing one or more such collisions and more than,000 having zero. About half as many intersections () experienced one or more (up to ) PXing_int crashes, with the remaining intersections having no recorded crashes over the eight year study period. TABLE Scale Measures of Exposure, Land Use Factors and Pedestrian Crashes for Seattle Intersections Variable Minimum Maximum Mean AADB_Int 0,1 1 Estimated average annual daily bicycle volume AADP_int 1,1,, Estimated average annual daily pedestrian volume tenth_pop 0,1 0 Total population within 0. mile

10 1 Variable Minimum Maximum Mean pct_ageplus Proportion of population age + within 0. mile (ACS 01, year estimate, blockgroup) pct_kids Proportion of population within 0. mile < 1 years (ACS 01, year estimate, blockgroup) mean_inc Mean income within 0 meters of intersection 0, 0.,1 0.1,0 quart_emp Total employment within 0. mile,0, tenth_comm Number of commercial properties within 0. mile 0. transit_stops ft Number of buses/trains stopping within ' on a typical weekday univ_dist ( unknowns) Network Distance to nearest university campus (mile) 0 0 1, avg_slope_pct_half_mi Average slope of terrain within 0. mile surrounding intersection max_slope_pct (1 unknown) Maximum percent slope on any approach bldg_vol_all_tenth All building volume (ft ) within 0.1 mile 0,,1,1, bldg_vol_comm_tenth Commercial only building volume (ft ) within 0.1 mile 0,0,0 1,0,1 Ptot_int [model outcome measure] All reported pedestrian- motor vehicle intersection collisions PXing_int [model outcome measure] Motor vehicle traveling straight, pedestrian crossing at intersection collisions AADT data were unfortunately only available for any of the study years for about % of segments in the network almost all the estimates were for principal arterials and there was insufficient information and time to develop AADT estimates for all intersections for this analysis. A prior study from Seattle had suggested that arterial classification was highly correlated with AADTs in the area (1). Although we were unable to verify this information, it was intuitively reasonable, and we used arterial classification in this study as at least a partial, though imprecise, surrogate for AADT. Other geometric and built environment measures may also be related to AADT exposure in our models, including the proportion of intersection legs that were local streets. However, we note that even AADT is in a sense a proxy measure of pedestrians exposure to crash risk related to auto traffic since details on volume, traffic density, lane position, and other events at the time of each crash are not measured. Thus, other built

11 environment and roadway measures may reasonably help to account for exposure to crash risk based on auto traffic presence. Average operating speed for a roadway has also shown to be significantly related to the risk of fatal and injury crashes of all types () while impact speed is highly related to the severity of pedestrian injuries received in a crash (1). We lacked data on actual operating speeds, and the data on speed limits were not deemed to be very useful for analysis as there was very limited variability in speed limits for most of the network where pedestrian collisions occurred. The vast majority of road sections had speed limits of or 0 mph. There was also a high correlation between speed limits and arterial classification. All local streets were at mph. Ninety-four to % of collector and minor arterial sections were at 0 mph, with a majority (0%) of principal arterials also at 0 mph. Only % of principal arterial sections had posted at speeds of mph or higher. Multivariate Analysis Methods The multivariate analysis of the intersections database to develop the SPFs included two steps: 1) Conditional Random Forest (CRF) analysis, and ) negative binomial regression (NB) analysis. Since there were many eligible crash predictors available, CRF analyses were first conducted to select the important predictors associated with the crashes for the SPF development. Conditional Random Forest Analyses CRF analysis is typically used for data mining to better understand the data, and, in this study, to help identify which of many available variables were important predictors of pedestrian crashes. CRF analysis provides an assessment of relative variable importance in predicting an outcome variable, but does not provide information about the degree to which the variables affect crashes, nor about the relationship direction or categories that are most predictive. For these reasons, CRF analysis was used to as a first step to identify the important variables that are correlated with crashes to test in the models. The CRF method entails developing crash trees by repeatedly, randomly subsetting the data, and identifying those variables that are most associated with crashes in the different subsets in a hierarchical manner. Package party in R was used for this analysis. This analysis developed 1,00 trees for each crash type; the collective output from those 1,00 trees produced the final importance ranking of the predictor variables for each crash type. We conducted conditional random forest (CRF) using all the data since CRF can handle missing values. All of the variables tested in the CRF models are described in Tables 1 and. Regression Models We used NB modeling, the recommended method to model over-dispersed crash count data (). Before modeling, we examined variable distributions, and combined levels for variables which had low numbers for some categories. If AADP or AADB were indicated by the CRF analysis to be important predictors, both the raw estimates and the log forms of each were tested in the models. We employed SAS Proc Glimmix procedure to develop the SPFs using the important variables from the CRF analysis. The SAS procedure automatically excluded the intersections

12 with missing values for the SPFs development if the variable(s) with missing values were in the SPFs. Only variables significant at % or higher significance level were retained in the models. In addition, two criteria were used to assess the goodness of fit for each model developed Akaike information criterion (AIC) and Bayesian information criterion (BIC) or the Schwarz criterion (1, 1). The criteria are expressed as below: Where: AIC = k - ln(l) BIC = kln(n) - ln(l) k = number of free parameters in the model n = sample size L = maximized value of the likelihood function A smaller value for either criterion indicates a better model fit to the data. The difference in the two criteria is the magnitude of the penalty assigned for the number of parameters in the model. The AIC uses a multiplier of.0 and does not account for sample size. The BIC accounts for the size of the sample with the multiplier ln(n). The goal is to select the model with the best fit and the fewest parameters. Thus, the BIC was chosen as the preferred criterion for comparison of models. However, the AIC was also used when there was no or little change in the BIC. The magnitude of the change in these criteria when choosing among models must also be considered. The values in Table provide general guidance on interpreting the change in value for the two criteria. TABLE General guidance for interpretation of models using AIC and BIC criteria Change in AIC Model Difference Interpretation (1) Change in BIC Model Difference Interpretation (1) 0 Indistinguishable 0 Negligible Substantial Positive > Very Strong Strong > Very Strong Ranking intersections After development of the SPFs, each intersection was ranked using four methods, three of which used the SPF predictions in estimating potential crashes. The fourth method was simply frequency-based ranking. Table defines each method and describes key benefits and limitations highlighted by the Highway Safety Manual and the other referenced sources. The key advantage of the SPF-based methods, is that, to different degrees, they help predict where crashes might occur based on characteristics of the environment found by the modeling process to be associated with crashes system-wide, not based only on prior crash frequencies. 1

13 TABLE Strategies for Ranking Locations for Potential Pedestrian Safety Treatments Ranking Type Definition Benefits Limitations Frequency based on reported crashes Covers known crash locations. Simple to perform. Number of predicted crashes at an intersection (based on SPF) Empirical Bayes (EB) weighted combination of reported and SPF- predicted crashes Potential for safety improvement (PSI) i Conventional/hotspot approach. Uses the crash count to rank locations. Applies the model coefficients for significant parameters to predict crashes at every intersection and ranks locations according to the SPF predictions. Uses weighted estimate of predicted (P) and reported (acc) crash frequencies. PSI is the difference between the EB expected crashes and the SPF- predicted crashes i The PSI method was introduced by Persaud et al (1). Provides an estimate of crashes based on the model predictions; locations may rank highly even if they have not observed prior crashes. Considers SPF- prediction and prior crash history (observed), which better accounts for imperfect data and latent or unobserved variables. The EB method has been found to be more accurate for predicting (mv) crashes than the other methods (1). Like EB, allows consideration of crash history as well as findings from SPF. Weights prior crash history more strongly which some safety practitioners believe is an effective way of identifying locations needing improvement. May suffer from regression to the mean; does not account for exposure. Is not built on analysis of the factors that may contribute to more crashes than predicted for similar sites. May provide a biased list if important predictors are unobserved/missing. Requires accounting for pedestrian and auto traffic. Requires accounting for pedestrian and auto traffic. Weighting for prior crashes can help compensate for unobserved/missing factors. Requires accounting for pedestrian and auto traffic. May provide a biased list if important predictors are unobserved/missing. 1

14 1 1 RESULTS AND DISCUSSION The variable importance rankings from the CRF analysis for both crash types are presented in Figure. All variables to the right of the dashed line are considered to be important predictors. However, the further a variable is from the top of the plot, the less predictive it is with respect to the response variable (crashes). Thus, the change in significance at the cutoff point between selected and non-selected variables was visually apparent at the zero line on the plot. (Both solid and vertical dashed lines are essentially at zero.) The goal of this criterion was to ensure that the breakpoint was not being chosen within a subset of variables with near identical importance values. Traffic signal presence was the most important predictor identified for Ptot_int crashes, while the numbers of buses stopping daily within feet was the most important predictor for PXing_int. All variables to the right of the dashed line were tested in the relevant regression model to determine which combination of variables provides best prediction of that pedestrian crash type. 1

15 a) b) FIGURE. Results of Conditional Random Forest assessments of variables importance in predicting a) Ptot_int, all pedestrian crashes and b) PXing_int, pedestrian crossing with the driver going straight crashes both at intersections. [Note: the horizontal line and labels show the least important predictor variable that was tested in the respective SPF model development process.] 1 1 The final SPF models resulting from testing of the variables identified by the CRF analyses are shown below.while it is desirable to find a plausible connection to crashes among the predictors, and we discuss some of these issues in light of Seattle s network and the gaps in data, we cannot infer that the predictive variables are causal of crashes from these analyses. Unmeasured risk factors (such as AADT, signal operations and traffic speed) and endogenous or latent factors associated with individual crashes such as light conditions, weather, micro-conditions and behviors of the parties involved are not accounted for by this prediction method. Analysis #1: Total Pedestrian Crashes at Intersections (Ptot_Int) The SPF is: y (Ptot_int) = Exp (bo + b1xtenth_comm + bxtransit_stops ft + bxbldg_vol_all_tenth + bxln_aadp_int + bxaadp_int + bxbldg_vol_comm_tenth+ bxlegs_unklanep + bx mean_inc + 1

16 bxavg_slope_pct_half_mi + bxtenth_pop+ bxsignal_bin + b1xlegs_cat + b1xvehlanes_cat + b1xbiglanes_cat + b1xartcls_cat + b1xparking_bin) The model statistics for Ptot_Int are shown in Table. Analysis #: Pedestrian Crossing at Intersection and Struck by Motorist Going Straight (PXing_Int) The SPF for PXing_int takes the form: Y(PXing_int) = Exp (bo + b1xtransit_stops ft + bxtenth_comm + bxlnaadp_int + bxaadp_int + bxlegs_unklanep + bxbldg_vol_all_tenth + bxmean_inc + bxbldg_vol_comm_tenth + bxsignal_bin + bxartcls_cat + bxlegs_cat+ b1xbiglanes_cat) The statistics for this model are also shown in Table. Many of the traits associated with all pedestrian crashes at intersections (Ptot_int) were also predictive of this sub-group of intersection crashes (PXing_int). TABLE Statistics for Total Pedestrian Crashes and Crashes Involving Pedestrian Crossing and Motorists Going Straight at Intersections SPF Effect Intercept tenth_comm Commercial properties transit_stops ft Daily buses within ft bldg_vol_all_tenth Total building volume (0.1 mi) ln_aadp_int Natural log of AADP AADP_int Avg. daily ped volume (00) bldg_vol_comm_tenth Commercial building volume (0.1 mi) Category n/a Ptot_int PXing_int Estimate StdErr Estimate StdErr -. i i 1. n/a 0.0 i i 0.00 n/a i i n/a.e- 0 i E- 0 i n/a 0.1 i i 0. n/a i i 0.0 n/a -.E- 0 i E- 0 i

17 Effect legs_unklanep Local Streets Proportion mean_inc Mean Income area residents avg_slope_pct_half_mi Avg Slope Terrain 0. Mi tenth_pop Total population 0.1mi signal_bin Presence of traffic signal legs_cat Number of Legs vehlanes_cat Total Lanes Intersection Biglanes_cat Total Lanes Largest Leg Artcls_cat Highest Arterial Class parking_bin Parking presence Scale i Significant at p < 0.0 Category Ptot_int PXing_int Estimate StdErr Estimate StdErr n/a i i 0.00 n/a -.E- 0 i E- 0 i n/a i 0.0 n/a n/a i n/a Yes 1.01 i i 0. No base cat. base cat. 0.0 i i 0. >/= 0.1 i i 0. base cat. base cat i 0. n/a i 0. n/a - base cat. n/a i i base cat. base cat. Major 1.1 i i 0. Minor 1.1 i i 0. Collector 0. i i 0. Local base cat. base cat. Yes 0.0 i 0.0 n/a No base cat. n/a Model paramet er 1 Both the natural log of pedestrian volume estimate (ln_aadp int) and the raw estimate of average annual daily pedestrian volume (AADP_int) were associated with collisions with motor vehicles in both models. Our estimates for the effects of the log of annualized pedestrian volume (0. for PXing_int and 0. for Ptot_int) are in the range of effects reported in the literature: 0. for signalized/unsignalized combined (); 0. 0., signalized (, ); and 0.0, unsignalized (). However, we tested and also found that the raw estimated number of pedestrians was negatively associated with pedestrian collisions in both models. The upward convex pattern resulting from these interactions, illustrated in Figure, suggests there is a critical AADP at which pedestrian safety is diminished, then begins to improve with increasing numbers of pedestrians. The ratio of the predicted crashes at an AADP over what would be expected at the 1

18 minimum AADP in the data, which is 1,1, might be regarded as a crash modification factor (CMF), if the effect is due solely to numbers. It is intuitive that fewer pedestrian crashes happen at locations with lower pedestrian volume as shown by the upward trajectory of the curve, but the causes for the downward curve above around,000 AADP are not entirely clear. Drivers may drive more cautiously when pedestrians are expected (1), and/or busier roadways may contribute to slower traffic and fewer crashes in environments where many pedestrians. CMF AADP CMF for Pxing_int CMF for Ptot_int FIGURE Crash Modification Factor (suggestive only) vs AADP_int for Seattle data. are found. However, safer roadways may in fact come first, leading to more pedestrians, rather than the other way round, as a directional relationship has not been firmly established (0). These analyses are also cross-sectional, not before and after, and therefore we cannot say that higher numbers of pedestrians led to greater safety. The number of buses stopping within feet of the intersection was also positively correlated with Ptot_int and PXing_int crashes with the effect appearing larger for PXing. Two of four prior SPF models have also found that measures of transit activity were significantly predictive of crashes while controlling for pedestrian volumes (,). Total population within 0.1 mi. was positively correlated with Ptot but not with PXing crashes. Mean income of area residents was negatively correlated with both types of pedestrian intersection crashes, consistent with at least one prior study (); again, this measure as well as total population may be helping to account for activity-based exposure rather than any inherent risk associated with the population numbers or income. Several built and other environment variables were significantly and positively associated with both Ptot_int and PXing_int collisions including the number of commercial properties within 0.1 mile of the intersection, and the total building volume within that distance, whereas the commercial building volume within 0.1 mile of the intersection somewhat offset these positive relationships with a negative correlation to crashes. The average slope of the 1

19 surrounding terrain was negatively correlated with Ptot, but not significant for PXing. We assume that these relationships may be providing a nuanced measure of exposure and crash prediction in conjunction with AADP and the other activity measures, as well as potentially capturing other elements that may contribute to crashes. The density of commercial properties close to an intersection has, however, previously been identified as a crash predictor, even when pedestrian volume is included in the models (,,,). Finally, larger intersections were predictive of more pedestrian crashes. Both more legs at an intersection and larger numbers of total lanes were associated with more pedestrian collisions of both crash types. More lanes on the largest leg also added predictive power to Ptot, although not to PXing crash type. Higher entering arterial classes (principal and minor were also associated with more crashes of both types. The proportion of (lower volume) local street legs at an intersection also had a decreasing effect on both types of crashes, also consistent with an effect due to AADT. While traffic signals were also predictive of both Ptot and PXing crashes, the effect is weaker in PXing. Several of these variables may be accounting in part for exposure to amounts of auto traffic, traffic speed or other unmeasured or endogenous characteristics. For example, unrestricted left turn phasing is common at signalized intersections in Seattle. Quistberg et al., also found signals to be associated with more pedestrian crashes in Seattle compared to unsignalized locations (both intersections and midblocks) (1). Variables accounting for the presence of turning lanes did not help predict these pedestrian crash types, but may prove more useful when turning crashes are examined. In addition, parking presence, and more total lanes for all legs of the intersection (-, or - lanes compared with - total lanes) were predictive of more Ptot crashes although not PXing. Parking could also increase the complexity of interactions and sight distance issues at the intersection as well as reflect traffic and pedestrian travel. Using the Results for Safety Screening Our SPF model results are similar to findings from past research indicating that higher levels of activity (measured through pedestrian volume estimates, as well as land use and other variables such as transit activity) are predictive of pedestrian crashes. Although as yet, we have not been able to validate model predictions with a subset of the data, the findings are generally consistent with earlier studies and also have plausible connections to crashes; however, no direct causal inferences are possible. To apply the results to screening the network, the SPFs were used to predict crashes for all intersections of legs and greater (the data included in these models). The SPF predictions were also used to generate EB estimates and PSI estimates. Intersections were ranked according to each of these esimates, and by prior crash frequencies. Significant variables and the associated rankings of both the pedestrian and bicycle crash types analyzed (reported on in a separate paper) were all combined into a spreadsheet that was used as the basis for building a GIS-based tool. 1

20 1 Example Application Figure shows the top 0 crash locations using the SPF-predicted and EB-estimated rankings in comparison with the top 0 by crash frequency for Pedestrians who were struck while crossing an intersection by a motor vehicle traveling straight through (PXing_Int type) Note that several of the top 0 crash locations are not predicted by either method to be in the top 0, whereas additional along the same corridors or in areas with high crash locations are predicted to be in the top 0 by these other methods. EB estimated locations tend to overlap more with high frequency crash locations than SPF-predicted ones, since prior crashes contribute to the EB estimates but not the SPF predictions, which are based only on the model risk factors. The EB estimate also considers variance in the prediction, so is weighted more strongly by crashes when prediction is less precise Imperfect data and latent crash variables such as light and traffic conditions at the time of crashes, driver populations or behaviors and others, suggest that it makes sense to consider where crashes have already occurred, as there may be undetected risks at work. Conversely, if there is FIGURE Top Twenty Intersections for SPF- predicted, only a focus on where crashes have EB- estimated, and crash frequency for Pedestrian already occurred, as in frequencycrossing- Motor Vehicle Straight at Intersection Crashes, based ranking, then locations that can Seattle, WA. be expected to have crashes in the future will be missed. There is a need to consider both. The EB estimates consider both SPFprediction and prior crash history, and has yielded more accurate predictions than other methods for motor vehicle crashes (1). However, top-ranked SPF locations might also be investigated as they demonstrate significant risk based on network-wide factors, even if they have not yet experienced crashes. With the spreadsheet/gis tool, the City can conduct screenings using various crash types and ranking methods, as well as filter by various location characteristics or areas of the City to 0

21 help prioritize locations for further assessment. City staff will be able to visualize highly ranked locations for the two pedestrian crash types, as well as three bicycle crash types, and identify areas of overlapping concern. High-ranking locations along a corridor or area, may also be identified. For any priority intersections, our tool allows filtering by other risk characteristics, such as signalization, numbers of lanes, and others. These features enhance the ability to identify potentially appropriate countermeasures. We emphasize that field and other investigations are still required to complete diagnosis of the particular safety issues. Study Limitations and Follow-up to Safety Analyses Potential limitations to this study that could have influenced model results include those already acknowledged: the use of a relatively long study period and greater potential for infrastructure changes over that time period, the lack of network-wide AADT data, and the need to develop and use ballpark estimates to represent pedestrian activity exposure. The pedestrian volume estimates still need validation. The need to aggregate certain variables such as numbers of lanes may also have potentially obscured specific relationships or break points for risk increase. Although the data available for analysis in this study were extensive and very complete for many types of measures, these gaps in exposure and other measures that are typically associated with crash risk no doubt influenced the other predictors selected in our models. These gaps may have affected the variables and coefficients, and potentially the relatively large number of variables that proved predictive of crashes in our models. The City has already undertaken to make data improvements, including to develop estimates of AADT for the entire street network, so future analyses should be able to incorporate AADT and continuing improvements in pedestrian activity measures. We need also to validate the current model predictions with new crash data, and develop SPF models for additional crash types (e.g., pedestrians hit by left-turning drivers, mid-block crashes, nighttime crashes) CONCLUSIONS The aim of this study was to use robust analysis methods to identify and prioritize pedestrian safety issues in order to proactively treat locations where risks are higher, not just those that have already experienced significant crashes. We successfully developed SPFs for two pedestrian crash types at Seattle intersections and provided an example of how the resultant SPFs and related ranking procedures provide a way to prioritize locations based on overall risk of crashes using the data currently available. The screening tools developed from the SPF predictions, EB estimated crashes, and PSI (potential for safety improvement) can be used to compare highly ranked locations by the different ranking methods as well as across the varied pedestrian (and bicycle) crash types modeled. Overall, this has the potential to lead to a more comprehensive approach to multi-modal treatment. The models need validation and we expect that pedestrian SPFs for Seattle will also evolve as new and improved data becomes available. Pedestrian activity and conflict/intersection complexity-related factors such as parking and numbers of lanes and legs seem to be important in predicting where crashes have occurred and might occur in the future in Seattle. A number of similar measures have also been identified in earlier SPF models from other cities. Roadway measures including arterial class and signal 1

22 presence were also important predictors. We lacked motor vehicle traffic volume data for the entire network and this gap may have affected some of these other significant predictors in our models. While it is desirable for the predictive factors to have plausible relationships to crashes and the other variables in the models, and we discussed the results in this light, the variables in predictive models do not provide clarity on the underlying causal relationships. The SPFs do provide a more systemic approach to prioritizing risky locations, based on the combinations of predictors selected. Based on the collective research to date, jurisdictions should collect or estimate pedestrian and traffic volumes for their entire network on a routine basis since these measures have been identified as important for pedestrian crash prediction. To compensate for the lack of widespread pedestrian volumes, we generated ballpark estimates of annualized pedestrian volume for all intersections. The expected increases in pedestrian intersection crashes based on our estimated volumes are within the range of others reported in the literature (using log transformed data). In addition, higher (raw) numbers of pedestrians above a threshold value were correlated with increased safety in our data, but we cannot infer causes or even the direction of this relationship. Improved pedestrian and motor vehicle exposure data over time may finally allow researchers to conduct before and after analyses to determine whether crash risk is lowered by increasing pedestrian volumes or whether relative safety leads to greater numbers of pedestrians. Our research and that of others also suggest there may be multiple ways to represent risks due to volumes of road users and their interactions, and time will tell whether our methods which employed a mix of less than ideal measures to account for exposure are good enough for analysis of risk at specific locations. Many of the variables we used that helped predict crashes are widely available to agencies from census and land use data, and once compiled to the right scales, may not prove too difficult to be maintain in the future. Improvements in user volume data may, however, mean that fewer alternate measures may be needed to predict crashes effectively. Risks attributable to other roadway and built environment factors that might be treatable may also be easier to determine if the right mix of other activity variables can be welldefined. Ultimately, the decision of which measures to use may relate to other goals of the analysis and the target safety scale. Emerging and future research from varied environments and regions of the country, and including variables frequently omitted, such as traffic speed, will hopefully lead to improved understanding of essential data needs, and may ultimately lead to more generalizable models and knowledge of roadway and built environment crash risks beyond exposure. ACKNOWLEDGEMENTS This work was funded by the Seattle Department of Transportation. In addition, we thank Belinda Judelman for her help with the literature review, and Monica Dewald, Craig Moore, Chris Svolopoulos and Mike Morris-Lent and others at Seattle DOT for providing data and supporting this project.