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Improving Pedestrian Safety at Unsignalized Crossings (2006)

Chapter: Chapter 7 - Findings From the Field Study

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Suggested Citation:"Chapter 7 - Findings From the Field Study." National Academies of Sciences, Engineering, and Medicine. 2006. Improving Pedestrian Safety at Unsignalized Crossings. Washington, DC: The National Academies Press. doi: 10.17226/13962.
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Suggested Citation:"Chapter 7 - Findings From the Field Study." National Academies of Sciences, Engineering, and Medicine. 2006. Improving Pedestrian Safety at Unsignalized Crossings. Washington, DC: The National Academies Press. doi: 10.17226/13962.
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Suggested Citation:"Chapter 7 - Findings From the Field Study." National Academies of Sciences, Engineering, and Medicine. 2006. Improving Pedestrian Safety at Unsignalized Crossings. Washington, DC: The National Academies Press. doi: 10.17226/13962.
×
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Suggested Citation:"Chapter 7 - Findings From the Field Study." National Academies of Sciences, Engineering, and Medicine. 2006. Improving Pedestrian Safety at Unsignalized Crossings. Washington, DC: The National Academies Press. doi: 10.17226/13962.
×
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Suggested Citation:"Chapter 7 - Findings From the Field Study." National Academies of Sciences, Engineering, and Medicine. 2006. Improving Pedestrian Safety at Unsignalized Crossings. Washington, DC: The National Academies Press. doi: 10.17226/13962.
×
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Suggested Citation:"Chapter 7 - Findings From the Field Study." National Academies of Sciences, Engineering, and Medicine. 2006. Improving Pedestrian Safety at Unsignalized Crossings. Washington, DC: The National Academies Press. doi: 10.17226/13962.
×
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Suggested Citation:"Chapter 7 - Findings From the Field Study." National Academies of Sciences, Engineering, and Medicine. 2006. Improving Pedestrian Safety at Unsignalized Crossings. Washington, DC: The National Academies Press. doi: 10.17226/13962.
×
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Suggested Citation:"Chapter 7 - Findings From the Field Study." National Academies of Sciences, Engineering, and Medicine. 2006. Improving Pedestrian Safety at Unsignalized Crossings. Washington, DC: The National Academies Press. doi: 10.17226/13962.
×
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Suggested Citation:"Chapter 7 - Findings From the Field Study." National Academies of Sciences, Engineering, and Medicine. 2006. Improving Pedestrian Safety at Unsignalized Crossings. Washington, DC: The National Academies Press. doi: 10.17226/13962.
×
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Suggested Citation:"Chapter 7 - Findings From the Field Study." National Academies of Sciences, Engineering, and Medicine. 2006. Improving Pedestrian Safety at Unsignalized Crossings. Washington, DC: The National Academies Press. doi: 10.17226/13962.
×
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Suggested Citation:"Chapter 7 - Findings From the Field Study." National Academies of Sciences, Engineering, and Medicine. 2006. Improving Pedestrian Safety at Unsignalized Crossings. Washington, DC: The National Academies Press. doi: 10.17226/13962.
×
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Suggested Citation:"Chapter 7 - Findings From the Field Study." National Academies of Sciences, Engineering, and Medicine. 2006. Improving Pedestrian Safety at Unsignalized Crossings. Washington, DC: The National Academies Press. doi: 10.17226/13962.
×
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Suggested Citation:"Chapter 7 - Findings From the Field Study." National Academies of Sciences, Engineering, and Medicine. 2006. Improving Pedestrian Safety at Unsignalized Crossings. Washington, DC: The National Academies Press. doi: 10.17226/13962.
×
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Suggested Citation:"Chapter 7 - Findings From the Field Study." National Academies of Sciences, Engineering, and Medicine. 2006. Improving Pedestrian Safety at Unsignalized Crossings. Washington, DC: The National Academies Press. doi: 10.17226/13962.
×
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Suggested Citation:"Chapter 7 - Findings From the Field Study." National Academies of Sciences, Engineering, and Medicine. 2006. Improving Pedestrian Safety at Unsignalized Crossings. Washington, DC: The National Academies Press. doi: 10.17226/13962.
×
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Suggested Citation:"Chapter 7 - Findings From the Field Study." National Academies of Sciences, Engineering, and Medicine. 2006. Improving Pedestrian Safety at Unsignalized Crossings. Washington, DC: The National Academies Press. doi: 10.17226/13962.
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43 The field data provided information on several pedestrian and motorist behaviors. This chapter summarizes those findings. For specific variables, the following data reduction proto- col was used. For pedestrians crossing in groups and clusters, observers only considered the leading pedestrian or the pedestrian closest to the oncoming traffic. All pedestrians in a group or cluster were counted as a single pedestrian cross- ing event. For the dataset of 3,155 crossings, 74 percent of the observations represented crossings of individuals, 18 percent were groups (over three-fourths being groups of two), and 8 percent were clusters (most of which occurred at sites show- ing a red indication, i.e., pedestrians are restricted by pedes- trian signal indications). Non-staged (or general population) pedestrians were rep- resented in the descriptive statistics presented in this chapter. For computing motorist compliance rates, the staged pedes- trians were also used. Walking Speed One of the pedestrian characteristics collected during field studies conducted as part of this TCRP/NCHRP study was the time for the pedestrian to cross to the middle of the street or median and then to the other side of the street. Using the distances being traversed, the walking speeds of the pedes- trians were determined. The walking speeds associated with different roadway conditions and pedestrian characteristics are available from the dataset. Various statistical analyses were used to better understand walking speed and to explore its relationship with the roadway environment and pedes- trian characteristics. Appendix N provides more details on walking speed findings. This section provides a summary. Pedestrian Walking Speed by Age Groups To permit comparisons with other studies, the data were grouped to reflect the following: • Young—consists of pedestrians between the ages of 13 and 60, and • Old—includes pedestrians older than 60. The gender of the pedestrian was also recorded if the tech- nician was able to determine the information from the field observation or later in the office during the video data reduc- tion effort. A total of 3,155 pedestrian crossings were recorded during this study. Of that, 81 percent (2,552 pedestrians) were observed as “walking.” The remaining 19 percent of the pedes- trians (603) were observed to be running, both walking and running during the crossing, or using some form of assistance (e.g., skates or bicycles). These 603 data points were not included in the following analyses. Also not included in the analyses were the 107 walking pedestrians whose ages could not be estimated and the 6 pedestrians whose genders could not be determined. Table 18 lists walking speeds by age group and gender. The walking speed values for older pedestrians are lower than those for younger people. For young pedestrians, the 15th per- centile walking speed was 3.77 ft/s (1.15 m/s). Older pedestri- ans had a slower walking speed with the 15th percentile being 3.03 ft/s (0.9 m/s). The average walking speed was 4.25 and 4.74 ft/s (1.3 and 1.45 m/s) for old and young pedestrians, respec- tively. Figure 22 illustrates the distribution of the walking speeds along with the current MUTCD walking speed and the walking speed recommended by the U.S. Access Board (57). Age Group Comparison An F test was used to find out if the walking speeds by gen- der and age were statistically different. Table 19 shows the results of the tests. The male, female, and combined male and female older pedestrian groups had 15th percentile walking speeds that were statistically different from the 15th percentile walking speeds of the younger pedestrians. For example, the 15th percentile walking speed of 3.03 ft/s (0.9 m/s) for older C H A P T E R 7 Findings From the Field Study

pedestrians was statistically different from the 15th percentile walking speed of 3.77 ft/s (1.15 m/s) for younger pedestrians. For the comparison done with the 50th percentile walking speeds, the female groups did not show a statistical difference. It is believed that this lack of difference was influenced by the small number of older women within the study set (only 31 older women pedestrians). In most cases, the walking speeds of the male and female pedestrian groups were similar. The only statistical difference in gender among the age groups was for the 50th percentile walking speed of the young group as shown in Table 19. The young female group walked slightly slower (4.67 ft/s [1.4 m/s]) than the young male group (4.78 ft/s [1.5 m/s]). Comparison of TCRP/NCHRP Walking Speed Findings with Previous Work As documented in Appendix M, several studies have exam- ined walking speed, including • Manual on Uniform Traffic Control Devices for Streets and Highways (1), 44 Walking Speed, ft/s (m/s) Age Groups Sample Size 15th Percentile 50th Percentile Male Young 1434 3.75 (1.14) 4.78 (1.46) Old 75 3.11 (0.95) 4.19 (1.28) ALL 1509 3.67 (1.12) 4.75 (1.45) Female Young 890 3.79 (1.16) 4.67 (1.42) Old 31 2.82 (0.86) 4.41 (1.34) ALL 921 3.75 (1.14) 4.67 (1.42) Both Genders Young 2324 3.77 (1.15) 4.74 (1.45) Old 106 3.03 (0.92) 4.25 (1.30) ALL 2430 3.70 (1.13) 4.72 (1.44) Table 18. Walking speed by gender and age group. 0 10 20 30 40 50 60 70 80 90 100 0 1 2 3 4 5 6 7 8 9 10 Walking Speed (ft/s) Cu m ul at iv e Pe rc en t ( %) Young Old Access Board MUTCD A cc es s B oa rd R ec om m en da tio n MUTCD "Normal" Walking Speed Figure 22. Older than 60 (Old) and 60 and younger than 60 (Young) walking speed distribution.

• Public Rights-of-Way Access Advisory Committee draft 2002 guidelines (57), • LaPlante and Kaeser (58), • 1982 Transportation and Traffic Engineering Handbook (59), • Knoblauch et al. (14), • Guerrier and Jolibois (60), • Milazzo et al. (61), • 2000 Highway Capacity Manual (23), • 2001 Traffic Control Devices Handbook (62), • Guidelines and Recommendations to Accommodate Older Drivers and Pedestrians (63), • Dahlstedt (11) in a study in Sweden, • Coffin and Morrall (12), • Dewar (8) in Human Factors in Traffic Safety, • Bennett et al. (9) in a 2001 Australian Institute of Trans- portation study, and • Akçelik & Associates (64) in a 2001 Australian study. Most of the studies have provided values at the 15th per- centile level. The 15th percentile level is frequently used to set policy for roadway design or traffic operations, but not in every situation. The portion of the population to include in calculating the 15th percentile value also varies. For example, in setting driver eye height values for use in stopping sight distance, the question of whether to include the higher eye heights represented by trucks and by drivers in sport utility vehicles (SUVs) was debated. (For the final determination, values for trucks and SUVs were not included in setting the design driver eye height; see NCHRP Report 400[65].) A similar debate exists for walking speed. Should “walking speed” include all crossing maneuvers, even if the pedestrian is running? Should those using some form of wheels, whether it be in-line skates or a wheelchair, be considered? Should design be based only on older pedestrians or a mix of older and younger pedestrians? Figure 23 summarizes the 15th percentile findings from sev- eral studies. The figure also includes key characteristics of the study, such as whether the data reflect old or young pedestri- ans. As shown in Figure 23, previous work has identified or recommended walking speeds as low as 2.2 ft/s (0.7 m/s) and as high as 4.27 ft/s (1.3 m/s) for a 15th percentile value. Two studies with databases known to include over 2,000 pedes- trian crossings are the 1996 Knoblauch et al. study (14) with data collected in 1993 and this TCRP/NCHRP study with data collected in 2003. Table 20 summarizes the findings for young, old, and all pedestrians from these two studies. Based on their findings, Knoblauch et al. suggested a value of 4.0 ft/s (1.22 m/s) for younger pedestrians and 3.0 ft/s (0.9 m/s) for older pedestrians for traffic signal design. The U.S. Access Board has recommended a walking speed of 3.0 ft/s (0.9 m/s). LaPlante and Kaeser (58) in a September 2004 ITE Journal article recommended 3.5 ft/s (1.1 m/s) minimum walking speed for curb-to-curb for determining the pedes- trian clearance interval and 3.0 ft/s (0.9 m/s) walking speed from top of ramp to far curb for the entire walk plus pedes- trian clearance signal phasing. This TCRP/NCHRP study had a similar number of young pedestrians within the dataset as the 1993 study (over 2,000 pedestrians). The TCRP/NCHRP study, however, found a slower walking speed (3.77 ft/s [1.15 m/s], as compared with 4.02 ft/s [1.23 m/s]). Therefore, the findings do not support the suggestion of a 4.0 ft/s (1.22 m/s) walking speed for traffic signal design. If both older and younger pedestrians are con- sidered, the TCRP/NCHRP study found 3.7 ft/s (1.13 m/s), while the larger 1993 study found 3.53 ft/s (1.08 m/s). Based on the larger number of older pedestrians included in the 1993 study, a recommendation of 3.5 ft/s (1.1 m/s) for the timing of a traffic signal design seems more reasonable. If older pedes- trians are a concern at the intersection, then a signal timing design using a 3.0 ft/s (0.9 m/s) walking speed is suggested. 45 Comparison 15th Walking Speed (ft/s) F 15th P 50th Walking Speed (ft/s) F 50th P F1,n-1,0.05 Male, Old & Young 3.11 0.0001 4.19 4.78 19.2 0.0001 3.85 Female, Old & Young 2.82 3.79 24.8 0.0001 4.41 4.67 1.78 0.1825 3.85 Both Age Groups Male & Female 3.67 3.75 2.91 0.0882 4.75 4.67 2.91 0.0882 3.84 Old Male & Female 3.11 2.82 2.67 0.1053 4.19 4.41 1.54 0.2174 2.91 Young Male & Female 3.75 3.79 0.70 0.4029 4.78 4.67 5.31 0.0213 3.84 Both Genders Old & Young 3.03 3.77 35.25 0.0001 4.25 4.74 14.96 0.0001 3.84 Bold cells indicate the walking speeds are different between the comparison groups. 22.593.75 Table 19. F-test results for gender and age group walking speed comparisons.

Conclusions Comparing the findings from this TCRP/NCHRP study with previous work resulted in the following recommendations: • 3.5 ft/s (1.1 m/s) walking speed for the general population; and • If older pedestrians are a concern, use a 3.0 ft/s (0.9 m/s) walking speed. Motorist Compliance This section presents the study findings on the effectiveness of pedestrian crossing treatments at unsignalized intersections as measured by motorist compliance (yielding or stopping as required by law). This section also describes an analysis of street and traffic characteristics (e.g., speed limit, number of lanes, and traffic volumes) that influence motorist compliance at marked crosswalks at unsignalized intersections. More details are included in Appendix M. Summary of Motorist Yielding Rates Tables 21 and 22 summarize the measured motorist yield- ing data from both types of pedestrian crossings (general population and staged), including comparable evaluation data from the literature where available. The results are grouped into the three basic categories of pedestrian crossing treatments used in the study. The range column in the table 46 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 19 50 a ll a ge s (58 ) 19 50 o ld er (5 8) 19 82 E ng in ee rin g Ha nd bo ok (5 9) 19 82 E ng in ee rin g Ha nd bo ok (5 9) D ah ls te dt , o ld er (1 1) 19 95 C of fin , o ld er , i nt er se ct io n (12 ) 19 95 C of fin , o ld er , m id bl oc k (12 ) 19 93 d at a, K no bl au ch , o ld er , 2 ,3 78 d at a po in ts (1 4) 19 93 d at a, K no bl au ch , o ld er , d es ig n (14 ) 19 93 d at a, K no bl au ch , y ou ng er , 2 ,0 81 da ta p oi nt s (14 ) 19 93 d at a, K no bl au ch , y ou ng er , d es ig n (14 ) 19 98 G ue rri er , o ld er (6 0) 19 98 G ue rri er , y ou ng er (6 0) 20 01 A kc el ik, w /w al kin g di ffi cu ltie s, m id bl oc k sig na l (6 4) 20 01 B en ne tt, fo r d es ig n (9) 20 01 B en ne tt, w /w al kin g di ffi cu ltie s, in te rs ec tio n (9) 20 01 B en ne tt, w /w al kin g di ffi cu ltie s, m id bl oc k sig na l (9 ) 20 01 B en ne tt, w /o w al kin g di ffi cu ltie s, in te rs ec tio n (9) 20 01 B en ne tt, w /o w al kin g di ffi cu ltie s, m id bl oc k sig na l (9 ) 20 01 O ld er P ed es tri an H an db oo k, le ss ca pa bl e (63 ) 20 01 T ra ffi c Co nt ro l D ev ice s Ha nd bo ok (62 ) 20 04 L A, p ro bl em in te rs ec tio ns (5 8) 20 03 d at a, F itz pa tri ck , o ld er , 1 06 d at a po in ts 20 03 d at a, F itz pa tri ck , y ou ng er , 2 ,3 35 da ta p oi nt s W al ki ng S pe ed (ft /s) Figure 23. Comparison of findings from previous studies for 15th percentile walking speed (labels contain year of study or year data were collected if known, authors or abbreviation of title, characteristics of study if relevant, and reference number in parentheses). Walking Speed (ft/s) Knoblauch et al. TCRP/NCHRP Age Group Sample Size 15th Percentile 50th Percentile Sample Size 15th Percentile 50th Percentile Young 2081 4.02 4.79 2335 3.77 4.74 Old 2378 3.10 3.94 106 3.03 4.25 All 3.53* 4.34* 2441 3.70 4.72 *Calculated using values provided in Knoblauch et al. paper (14). 4459* Table 20. Walking speed by age groups for Knoblauch et al. and TCRP/NCHRP studies.

47 TCRP D-08/NCHRP 3-71 Study Other Studies Compliance – Staged Pedestrian Crossing Compliance – General Population Pedestrian Crossing Compliance – Literature Review (from Table L-1) Crossing Treatment # of Sites Range (%) Average (%) # of Sites Range (%) Average (%) # of Sites Range (%) Average (%) Red Signal or Beacon Midblock Signal 2 97 to 100 99% 4 91 to 98 95% NA NA NA Half Signal 6 94 to 100 97% 6 96 to 100 98% 1 99 99% HAWK Signal Beacon 5 94 to 100 97% 5 98 to 100 99% 1 93 93% Active When Present In-Roadway Warning Lights NA NA NA NA NA NA 11 8 to 100 66% Overhead Flashing Beacon (Pushbutton Activation) 3 29 to 73 47% 4 38 to 62 49% 10 13 to 91 52% Overhead Flashing Beacon (Passive Activation) 3 25 to 43 31% 3 61 to 73 67% NA NA 74% Pedestrian Crossing Flags 6 46 to 79 65% 4 72 to 80 74% NA NA NA Notes: “NA” indicates that data were not collected or available in the literature. The “Range” column represents the range of motorist yielding for all sites with the treatment. The “Average” column represents the average value of motorist yielding for all sites with the treatment. Table 21. Summary of motorist yielding compliance from three sources for red signal or beacon and active when present. TCRP D-08/NCHRP 3-71 Study Other Studies Compliance – Staged Pedestrian Crossing Compliance – General Population Pedestrian Crossing Compliance – Literature Review (from Table L-1) Crossing Treatment # of Sites Range (%) Average (%) # of Sites Range (%) Average (%) # of Sites Range (%) Average (%) Enhanced and/or High-Visibility In-Street Crossing Signs (25 to 30 mph [40 to 48 km/h] Speed Limit) 3 82 to 91 87% 3 84 to 97 90% 7 44 to 97 77% High-Visibility Signs and Markings (35 mph [55 km/h] Speed Limit) 2 10 to 24 17% 2 4 to 35 20% NA NA NA High-Visibility Signs and Markings (25 mph [40 km/h] Speed Limit) 1 61 61% 1 91 91% 1 52 52% Median Refuge Islands 6 7 to 75 34% 7 7 to 54 29% NA NA NA Notes: “NA” indicates that data were not collected or available in the literature. The “Range” column represents the range of motorist yielding for all sites with the treatment. The “Average” column represents the average value of motorist yielding for all sites with the treatment. Table 22. Summary of motorist yielding compliance from three sources for enhanced and/or high-visibility treatments.

represents the range of average compliance values for the sites with that treatment. If a site had less than 10 general popula- tion pedestrians crossing the street during data collection, the compliance values were not included in summary statistics. The average column represents the average compliance rate for all sites with that treatment. The research team prepared these findings from Tables 21 and 22: • The motorist compliance rates for staged pedestrians and general population pedestrians were in relatively close agreement for most crossing treatments. Only two crossing treatments (total of four study sites) had motorist yielding rates with a greater than 10 percent difference between general population and staged pedestrians. At three Los Angeles sites, the research team attributed the differences to general population pedestrians who routinely stepped off the curb while waiting, whereas staged pedestrians did not step off the curb until motorists yielded. At a single Tucson site, the general population pedestrian flow was fairly heavy, which could lead to two possible explanations: (1) motorists were more likely to yield to larger groups of pedestrians than the single staged pedestrian and (2) the larger groups of pedestrians could have been more assertive in claiming the crosswalk right-of-way. Because the behavior of the staged pedestrians was consistent among all sites, these compliance rates are used in further analyses. • Red signal or beacon treatments consistently perform well, with compliance rates above 94 percent. The research team concluded that these treatments are effective because they send a clear regulatory message (a red signal means “Stop”) to motorists that they must stop for pedestrians. Nearly all the red signal or beacon treatments evaluated were used on busy, high-speed arterial streets. • Pedestrian crossing flags and in-street crossing signs also were effective in prompting motorist yielding, achieving 65 and 87 percent compliance, respectively. However, many of these crossing treatments were installed on lower-volume, two-lane roadways. It has been suggested that motorists are more likely to yield to pedestrians crossing narrow, low- volume and low-speed roadways. This is supported by the difference in compliance for high-visibility signs and markings. On streets with a 35-mph (55-km/h) speed limit, the average compliance rate was 17 percent; however, on streets with a 25-mph (40-km/h) limit, the average compliance rate was 61 percent (although only a single site had this speed limit). • The measured compliance rates for many crossing treat- ments varied considerably among sites. For example, treatments in the “active when present” and “enhanced and/or high-visibility” categories have a wide range of compliance rates as shown in Tables 21 and 22. The research team concluded that other factors (e.g., traffic volume, roadway width, and street environment) were affecting compliance rates. These factors are discussed in more detail in Appendix L. Significant Differences in Treatment Effectiveness As indicated in the previous section, many crossing treat- ments had wide ranges in the measured compliance rate (see Figure 24). Thus, even though the average compliance may be greater for some treatments, the wide range in compliance does not mean that one treatment is statistically more effec- tive than others. The research team tested statistical differ- ences of compliance rates between the crossing treatments using two different methods: • Analysis of variance—determines whether the mean com- pliance rates of the crossing treatments are statistically dif- ferent and • Multiple comparisons test—uses Tukey’s “honestly signif- icant differences” (HSD) test to find out which crossing treatments have statistically similar mean compliance rates. The findings of the statistical analyses are summarized as follows: • The three devices designated as red signal or beacon had statistically similar mean compliance rates. These devices include the midblock signal, half signal, and HAWK signal beacon. All three devices had average compliance rates greater than 97 percent. These statistical results validate the research team’s approach of grouping these devices into the same “red signal or beacon” category. • Many crossing treatments in the “active when present” and “enhanced and/or high-visibility” categories had compli- ance rates that were not statistically different than other treatments. Only three treatments were statistically differ- ent from others in these categories. The compliance rate for in-street crossing signs was statistically different than com- pliance rates for high-visibility signs and markings and overhead flashing beacons (pushbutton activation). The research team concluded that it may still be appropriate to differentiate between the “active when present” and “enhanced and/or high-visibility” treatments when dis- cussing function. However, the statistical results indicated that nearly all treatments in these two categories did not have statistically significant differences between the mean compliance rates. 48

Street Characteristics That Influence Treatment Effectiveness Because of the wide range in measured compliance rates among sites, the research team hypothesized that other vari- ables were influencing the treatment effectiveness. For exam- ple, an in-street crossing sign installed on a wide, high-speed arterial would likely produce a lower compliance rate than if installed on a narrow, lower-speed collector street. The research team performed a qualitative analysis and a statisti- cal analysis of covariance to find those factors that most affected the range in compliance rates. Effect of Number of Lanes The top chart in Figure 25 shows the motorist yielding by treatment type (major grouping) and number of lanes. For the “red signal or beacon” devices, the number of lanes did not affect performance. Within the study set, red devices were on two-, four-, and six-lane roadways.A compliance rate above 94 percent exists, regardless of the number of lanes on the facil- ity. The half signal treatment had statistically the same com- pliance rate for both two and four lanes. The same result was true for the HAWK treatment on four- and six-lane roads. Pedestrian crossing flags did not show a statistically differ- ent mean compliance for locations with a different number of lanes. The flags on two-, four-, and six-lane highways had sta- tistically similar compliance rates. Median refuge islands were the only treatment with statistically different compliance val- ues based on the number of lanes. The bottom chart in Figure 25 regroups the data in the top chart of Figure 25 by number of lanes. As seen in the bottom chart of Figure 25 for four-lane highways, the red devices have a much higher compliance rate than the other non-red devices. All but one of the devices on a two-lane roadway performed at better than a 60-percent compliance rate. The statistical analysis of covariance also indicated that the number of lanes crossed was a statistically significant variable (at the 0.05 level) in predicting motorist yielding at treatments. Effect of Speed Limit Figure 26 shows motorist yielding by treatment type and speed limit.As seen in the top chart of Figure 26, in-street pedes- trian crossing signs and overhead flashing beacons (pushbutton activation) appear to have an increase in compliance with an 49 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Msig Half Hawk InSt Flag OfPb Refu HiVi OfPa Treatment Type M ot or is t Y ie ld in g (% ) Minimum site value Maximum site value Average of all sites Abbreviations: Msig=midblock signal; Half=half signal; Hawk=HAWK signal beacon; InSt=in- street crossing signs; Flag=pedestrian crossing flags; OfPb=overhead flashing beacons (pushbutton activation); Refu=median refuge island; HiVi=high-visibility signs and markings; OfPa=overhead flashing beacons (passive activation) Figure 24. Site average and range for motorist yielding by crossing treatment.

50 Grouped by Treatment Type 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% M si g (4) H al f (2 ) H al f (4 ) H aw k (4) H aw k (6) In St (2 ) Fl ag (2 ) Fl ag (4 ) Fl ag (6 ) O fP b (4) R ef u (2) R ef u (4) H iV i (4 ) O fP a (2) O fP a (4) Treatment Type (Number of Lanes) M ot or is t Y ie ld in g (% ) Minimum site value Maximum site value Average of all sites Grouped by Number of Lanes 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% H al f (2 ) In St (2 ) Fl ag (2 ) R ef u (2) O fP a (2) M si g (4) H al f (4 ) H a w k (4) Fl ag (4 ) O fP b (4) R ef u (4) H iV i (4 ) O fP a (4) H a w k (6) Fl ag (6 ) Treatment Type (Number of Lanes) M ot or is t Y ie ld in g (% ) Minimum site value Maximum site value Average of all sites Abbreviations: Msig=midblock signal; Half=half signal; Hawk=HAWK signal beacon; InSt=in-street crossing signs; Flag=pedestrian crossing flags; OfPb=overhead flashing beacons (pushbutton activation); Refu=median refuge island; HiVi=high-visibility signs and markings; OfPa=overhead flashing beacons (passive activation) Figure 25. Motorist yielding by crossing treatment and number of lanes.

51 Grouped by Treatment Type 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% M si g (35 ) H al f (3 5) H aw k (35 ) H aw k (40 ) In St (2 5) In St (3 0) Fl ag (25 ) Fl ag (30 ) Fl ag (35 ) O fP b (30 ) O fP b (35 ) R ef u (25 ) R ef u (30 ) R ef u (35 ) H iV i (2 5) H iV i (3 5) O fP a (30 ) O fP a (35 ) Treatment Type (Speed Limit) M ot or is t Y ie ld in g (% ) Minimum site value Maximum site value Average of all sites Grouped by Speed Limit 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% In St (2 5) Fl ag (2 5) R ef u (25 ) H iV i (2 5) In St (3 0) Fl ag (3 0) O fP b (30 ) R ef u (30 ) O fP a (30 ) M sig (3 5) H al f (3 5) H aw k (35 ) Fl ag (3 5) O fP b (35 ) R ef u (35 ) H iV i (3 5) O fP a (35 ) H aw k (40 ) Treatment Type (Speed Limit) M ot or is t Y ie ld in g (% ) Minimum site value Maximum site value Average of all sites Abbreviations: Msig=midblock signal; Half=half signal; Hawk=HAWK signal beacon; InSt=in-street crossing signs; Flag=pedestrian crossing flags; OfPb=overhead flashing beacons (pushbutton activation); Refu=median refuge island; HiVi=high-visibility signs and markings; OfPa=overhead flashing beacons (passive activation) Figure 26. Motorist yielding by crossing treatment and posted speed limit.

increase in speed; however, the average compliance rates are not statistically different. In other words, the performance at these two devices is independent of the posted speed limit. The per- formance of the overhead flashing beacons (passive activation) shows a statistically different compliance rate between the device on the 30-mph (48-km/h) roadway and the device on the 35-mph (55-km/h) roadway, with the device on the higher- speed roadway having a higher compliance rate. Reviewing the specific sites showed that the 30-mph (48-km/h) site was in a commercial area while the 35-mph (55-km/h) site was in a res- idential area. Given that other devices show a decrease in com- pliance with an increase in speed limit, the findings for overhead flashing beacons (pushbutton activation) may be an anomaly. The median refuge island and high-visibility marking sites all had decreases in compliance rates with increases in speed limit. The F-statistical tests revealed that the compliance rates were statistically different, which indicates that the speed limit affects the performance of the device. Flags, refuge islands, and high-visibility markings all perform better on the lower-speed roadways. Figure 26 shows a clear break between two groups of treat- ments at the 35-mph (55-km/h) speed limit. The most effec- tive treatments are all red signal or beacon devices. On a 35-mph (55-km/h) roadway, the best compliance rate observed for a treatment not showing a red indication to the motorist is about 63 percent. Compliance rates go as low as 8 percent for the 35-mph (55-km/h) speed limit group. For the 25-mph (40-km/h) speed limit roadways, all the devices have a high compliance (greater than 60 percent). The statistical analysis of covariance also indicated that the posted speed limit was a statistically significant variable (at the 0.10 level) in predicting treatment compliance when accounting for interaction between other model variables. Gap Acceptance This section summarizes the findings on characteristics of gap acceptance behavior as observed at the field study sites. Appendix N contains more discussion and findings. The analysis of gap acceptance data had two components: behavioral analysis and statistical analysis. The former was concerned with identifying actions and patterns that pedes- trians commonly use in crossing events. The latter was intended to provide a mathematical model to determine gap size for a proportion of the crossing population. Behavioral Analysis Specific behavioral patterns affect how data are presented. One particular pattern is the concept of the “rolling gap.”Dur- ing data reduction, gap lengths were measured based on the times when vehicles entered the crosswalk. At certain sites, particularly sites with high volumes of traffic, pedestrians did not wait to cross the street when all lanes were completely clear. Rather, they anticipated that the lanes would clear as they crossed and used a “rolling gap” to cross the street; essentially, there was a separate gap for each lane of traffic that occurred to coincide with the pedestrian’s path across the street. For example, consider the conditions presented in Figure 27. There is not a sufficient gap for the pedestrian to cross the entire two-lane segment from the curb to the median between approaching vehicles because the traffic volumes are too high 52 Acceptable Opening A B C Figure 27. Pedestrian waiting to cross at crosswalk with high traffic volumes.

and are distributed between both lanes. In the “rolling gap”sce- nario, the pedestrian would begin the crossing maneuver when the acceptable opening between vehicles A and C occurred in the near (curb) lane, even though a second vehicle (vehicle B) might be approaching in the adjacent lane. However, by the time the pedestrian reaches the adjacent lane, vehicle B has already passed through the crosswalk, leaving an open lane to complete the crossing. After this, another approaching vehicle in the curb lane (vehicle C) might enter the crosswalk, giving the appearance that the actual gap was very small; but if the pedestrian properly timed the crossing, the gap is acceptable to the pedestrian at a comfortable walking speed. CA-LA-2 is a four-lane divided roadway with a configura- tion similar to that shown in Figure 27. Under these condi- tions, there is essentially a separate available gap for each lane that the pedestrian decides to accept or reject. Those gaps may or may not begin or end at the same time, but they occur in such a way that, when taken together, they create a combined gap sufficient for the pedestrian to cross the entire segment. Of the 66 accepted gaps at the CA-LA-2 study site, 60 percent (39 accepted gaps) were “rolling gaps.” Statistical Analysis The Statistical Analysis Software (SAS) computer pro- gram was used to conduct a logit transformation analysis. Each roadway approach was considered individually in the analysis; that is, each site was analyzed separately, and if the roadway was divided at that site, each side of the roadway had a unique analysis. As a result, 47 distinct analyses were performed, in addition to an overall analysis of all gaps for reference. From these analyses, graphs were generated showing the cumulative distribution of pedestrians accepting gaps of various lengths. Figure 28 shows an example of this type of graph. The data from some sites did not meet the conver- gence criterion. For the logistic model to run successfully, the values of accepted and rejected gaps must overlap, that is, there should be a gap length (or small range of gap lengths) that was both accepted and rejected. At sites with no overlap in values, the maximum likelihood estimate did not converge, but SAS continued with the analysis and matched a function. Under these conditions, the function does not have the smooth S-curve as shown in Figure 28 but rather resembles a step function, with a straight (and very steep) line between the values of the longest gap rejected and the shortest gap accepted. The results obtained from these functions have a lower level of confidence than the functions where the maximum likelihood estimate existed. This con- dition is explained in further detail in Appendix N. The complete set of results from the SAS logistical analysis is shown in Table 23. 53 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 2 4 6 8 10 12 14 16 Gap (s) Pe rc en ta ge A cc ep tin g G ap Figure 28. Sample cumulative distribution of gap acceptance.

Findings Several elements can affect the size of the 85th percentile accepted gap. First, the amount of data can have a significant effect, especially when only a few pedestrians were faced with making a gap acceptance decision. To minimize the potential effect that only a few pedestrians could have on the results, only those approaches with more than 20 pedestrians on the approach were considered in this evaluation. Second, the distribution of the data can affect the analysis of a large number of data points. At the NB2/SB1 approach of CA-LA-2, there were 241 observed gaps but only 32 pedestri- ans. Out of these 241 gaps, 196 required the pedestrian to make a gap acceptance decision on a gap of 3 seconds or less while only 10 were gaps of longer than 10 seconds. With such dense traffic, the gap acceptance was skewed lower. The gap acceptance results would be stronger if based only on free- flow vehicles; however, using only free-flow vehicles does not capture the conditions faced by the pedestrian. When the location is within a coordinated corridor, the pedestrian may ignore the gaps within the platoons of vehicles and wait for the larger gap present between the platoons. Third, the lack of some overlap in the accepted and rejected gaps is an important factor, as mentioned in the analysis section above. If there is separation of data, the maximum likelihood estimate does not converge; however, SAS will still provide an output, which will often have a very large standard error. An example is the NB2/SB1 approach of CA-SM-2, which had 125 observed gaps. An examination of the data reveals that almost all gaps between 1 and 5 seconds were rejected (one 5-second gap was accepted), and all the gaps above 5 seconds were accepted. The logit model tries to match these data with an equation, but because of the complete sep- aration for the accepted and rejected gaps, the equation almost forms a straight vertical line between 5 and 6 seconds where no data exist. Table 24 lists those approaches whose distribution has sep- aration of data. This table shows the values of the longest gaps rejected by at least 85 percent of pedestrians and of the short- est gaps accepted by at least 85 percent of pedestrians. Results from the logit model indicate a trend in the 85th percentile accepted gaps, in that the accepted gap increased as crossing distance increased. The trend for the 85th percentile accepted gap is compared with the critical gap for a walking speed of 3.5 ft/s (1.1 m/s) in Figure 23. Inspection of Figure 29 reveals that the observed gaps were less than the calculated critical gap for a walking speed of 3.5 ft/s (1.1 m/s). Thus, the 54 Site Approach β’(x) 50th Percentile Gap (s) 85th Percentile Gap (s) Number of Pedestrians Maximum Likelihood Estimate Converges? CA-LA-2 NB 1/SB 2 5.0462-0.8193x 6.2 8.3 34 Y CA-LA-2 NB 2/SB 1 7.9928-1.5001x 5.3 6.5 32 Y CA-SM-2 NB 1/SB 2 12.6355-2.4996x 5.1 5.8 40 Y CA-SM-2 NB 2/SB 1 37.0931-7.2800x 5.1 5.3 30 N CA-SM-3 NB 1/SB 2 6.9634-1.1879x 5.9 7.3 31 Y CA-SM-3 NB 2/SB 1 11.8970-2.0942x 5.7 6.5 29 Y MD-P1 NB 2/SB 1 65.1435-10.6485x 6.2 6.3 21 N MD-TO-1 NB 6.7212-0.9039x 7.4 9.4 22 Y MD-TO-1 SB 14.4907-1.7604x 8.2 9.2 34 Y UT-SL-2 NB 6.2673-1.2341x 5.1 6.5 22 Y WA-KI-3 WB 42.176-8.7008x 4.8 5.0 22 N ALL Sites and Approaches 6.2064-0.9420x 6.6 8.4 512 Y Site Approach Value of Longest Rejected Gap (s) Value of Shortest Accepted Gap (s) CA-SM-2 NB 1/SB 2 4.0 6.0 CA-SM-2 NB 2/SB 1 5.0 6.0 CA-SM-3 NB 2/SB 1 4.0 4.0 7.0 MD-P1 NB 2/SB 1 6.0 7.0 MD-TO-1 SB 7.0 10.0 WA-KI-3 WB 6.0 Table 23. Result of SAS logistic analysis for approaches with more than 20 pedestrians. Table 24. Summary of gap distribution for approaches with separation of data.

pedestrians in this study were not consistently accepting gaps exceeding the calculated critical gap, and the 3.5 ft/s (1.1 m/s) design criterion appears sufficient for the pedestrians observed. Transit Rider Walking Behavior Before Departing Whether or not a pedestrian was a transit rider was noted as part of the data reduction effects for those sites where a transit stop was within view of the cameras. A total of 878 pedestrians were observed at sites when a transit stop was in camera view with 6 percent (53 pedestrians) being transit rid- ers who boarded a bus and 5 percent (43 pedestrians) being transit riders who alighted from a bus. Of the 53 pedestrians who boarded a bus, the distribution of crossing behavior is listed in Table 25. About 17 percent of the boarding pedestrians ran or walk/ran through the major roadway crossing before board- ing. When the pedestrians who used assistance (e.g., skates or bicycles) are excluded, the percentage of pedestrians who ran or walk/ran becomes 18 percent. For the entire database available from this study on pedestrian crossing behavior, about 14 percent of the pedestrians who did not use assis- tance either ran or walk/ran through the crossing. In other words, a small but notably larger percentage of transit pedestrians ran or walk/ran as compared with the general population. The time that each boarding pedestrian waited was deter- mined as the difference between arrival of the pedestrian at the transit stop and the arrival of the bus. The relationship between crossing speed and the wait time is shown in Figure 30. Pedestrians with wait times less than 2 minutes showed the largest range of crossing speeds with the three fastest crossing speeds associated with wait times of less than 0.5 minutes. These pedestrians could be examples of the situation when pedestrians will run because they see an approaching bus. As a contrast to that situation, some of the pedestrians with wait times on the order of 10 minutes also ran or walk/ran in their crossing. Figure 30 shows a nonlinear relationship between crossing speed and rider wait time with an increasing trend in cross- ing speed as wait time increases. Researchers attempted to find a statistical relationship. Several transformations were tried on both crossing speed and rider wait time. The no transformation on crossing speed and the log transformation on rider wait time led to the smallest root mean square error (RMSE) when fitting was done by the least squares method. Table 26 contains the estimated coefficients and the corre- 55 0 2 4 6 8 10 12 14 16 20 25 30 35 40 45 50 55 Crossing Distance (ft) (NOTE: 1 ft = 0.305 m) G ap (s ) Observed 85th Percentile Accepted Gap Adequate Gap at 3.0 ft/s (0.9 m/s) Adequate Gap at 3.5 ft/s (1.1 m/s) Adequate Gap at 4.0 ft/s (1.2 m/s) Figure 29. Comparison of trends for observed 85th percentile accepted gaps and calculated critical gaps for walking speeds of 3.0, 3.5, and 4.0 ft/s (0.9, 1.05, and 1.2 m/s). Number of Pedestrians Percent (%) Pedestrian Crossing Behavior (Based on Technician’s Judgment) 2 4 6 79 100 Assisted (had skates, bicycles, etc.) 6 11 Ran and walked 3 Ran 42 Walked 53 TOTAL Table 25. Crossing behavior prior to boarding transit.

sponding P-values. The P-value for the coefficient estimate of Log (wait time in seconds) is 0.0731, which is at the border- line. At α = 0.05, the linear relationship between crossing speed and Log (rider wait time) is not significant, but it is at α = 0.1. The prediction equation is given as Crossing speed = 6.9075 × 0.3107 Log (wait time in seconds) Table 27 shows the R-square value of 0.06 and the adjusted R-square value of 0.04 for the fit in Table 26. As shown in Fig- ure 30, there is considerable variability in crossing speed, which leads to the very low R-square value in Table 27. Pedestrian Visual Search Each crossing pedestrian was coded into one of the follow- ing categories: • Looked for oncoming vehicles in each direction (B), • Looked for oncoming vehicle in one direction only (O), • Did not look for oncoming traffic in either direction (N), or • Data could not be determined from the video (X). Table 28 contains the distribution of pedestrian visual search by treatment. The only treatment where pedestrians only looked in one direction more than 3 percent of the time was the midblock signal; this was because a substantial num- ber of pedestrians tended to look before approaching the pushbutton to activate the signal. After activating the signal, they only watched for the signal indication to cross. The remaining treatments all had about two-thirds or greater of crossing pedestrians looking both ways, except for high- visibility markings and passive overhead beacons, which had a high percentage of unknowns. Pedestrian Crosswalk Use Each crossing pedestrian was coded into one of the follow- ing categories, showing • 0—crossed within the crosswalk markings or within 10 ft (3.1 m) of the crosswalk markings for most of the crossing event, • 1—crossed between 10 and 50 ft (3.1 and 15 m) of the crosswalk markings, • 2—crossed greater than 50 ft (15 m) from the crosswalk markings, or • X—data could not be determined from the video. Table 29 shows the crosswalk use by treatment. Each treatment that showed a red indication to the motorist (e.g., Half, HAWK, or Msig) had between 90 and 95 percent of the pedestrian crossings within 10 ft (3.1 m) of the crosswalk. 56 0 10 20 30 40 50 60 70 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 Crossing Speed (ft/s) Ti m e Be tw e e n P e de st ria n Ar riv a l a n d Bu s Ar riv a l (m in u te s) Figure 30. Crossing speed of 53 pedestrians who boarded transit by wait time. RSquare RSquare Adjusted 0.04323 Root Mean Square Error 1.672732 Mean of Response 5.196981 Observations (or Sum Weights) 53 0.06163 Table 27. Summary of fit in Table 26. Term Parameter Estimate Std Error t Ratio Prob>|t| Intercept 0.962458 7.18 <0.0001 Log(Wait time in seconds) – 0.310708 0.169769 – 1.83 0.0731 6.9075201 Table 26. Least squares fit for crossing speed.

All other treatments had rates of 80 to 89 percent. If the dis- tance is extended to 50 ft (15 m), all treatments had rates of 84 to 98 percent. Pedestrian Activation If the crossing treatment could be activated, each crossing pedestrian was coded into one of the following categories: • 1—the pedestrian did not attempt to activate the system but had to wait for an acceptable gap; • 2—the pedestrian did not attempt or was not properly positioned to activate the pedestrian crossing, or an acceptable gap was present when the pedestrian arrived at the curb; • 3—the crossing treatment was activated by the pedestrian, who waited until the proper time to cross (i.e., Walk signal or flashing light activation); • 4—the crossing treatment was activated by the pedestrian, who did not wait until the proper time to cross (i.e., Walk signal or flashing light activation); or • X—data could not be determined from the video. The distribution of pedestrian activation in Table 30 shows that red devices were activated about two-thirds of the time. Passive yellow devices (OfPa) were activated for 57 Visual Search Treatment Flag Half Hawk HiVi InSt Msig OfPa OfPb Refu Grand Total 62% 74% 94% 89% 47% 21% 71% 34% 85% 79% 83% B Total after removing unknowns N 1% 1% 0% 0% 3% 10% 2% 1% 0% 2% 3% O 1% 2% 1% 1% 3% 4% 2% 1% 1% 2% 3% X 15% 17% 13% 65% 22% 66% 49% 9% 25% 35% NA Count 350 342 224 606 310 393 164 254 512 3155 2082 Crosswalk Compliance Treatment Flag Half Hawk HiVi InSt Msig OfPa OfPb Refu Grand Total 1 5% 7% 5% 4% 5% 3% 3% 2% 13% 6% 2 7% 13% 5% 6% 2% 2% 10% 16% 5% 7% X 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Count 350 342 224 606 310 393 164 254 512 315587% 82% 82% 87% 95% 93% 89% 90% 80% 88% 0 Activate Treatment Flag Half Hawk HiVi InSt Msig OfPa OfPb Refu Grand Total 2 46% 25% 15% 88% 85% 20% 21% 43% 53% 50% 3 8% 63% 69% 0% 0% 64% 33% 22% 0% 24% 4 9% 4% 1% 0% 0% 3% 25% 6% 0% 4% X 2% 0% 2% 0% 0% 1% 1% 1% 3% 1% Count 350 342 224 606 310 393 164 254 512 3155 20% 21% 44% 27% 11% 15% 12% 13% 8% 35% 1 Table 28. Pedestrian visual search by treatment. Table 29. Pedestrian crosswalk use by treatment. Table 30. Pedestrian activation by treatment.

about 60 percent of crossing pedestrians, while active yellow devices were activated 28 percent of the time. Also, about one-half of the pedestrians at a refuge island had no wait, while 85 to 90 percent of pedestrians at other enhanced treatments had no wait. Of the 67 OfPa pedestrians who had no activation, 27 were at a site where the detector was malfunctioning, 24 were not detected by the system, and 16 were not compliant in using the crosswalk. Pedestrian-Vehicle Conflicts A pedestrian-vehicle conflict was counted if either a pedes- trian or a vehicle acted to avoid a pedestrian-vehicle collision. Evasive actions by the pedestrian included rushing to com- plete a crossing or aborting a started crossing. Evasive actions by the vehicle included sudden swerving, lane changing, or braking. Each pedestrian-vehicle conflict was coded into one of the categories shown in Figure 20. In addition the follow- ing location for the conflict was recorded: 1. Conflict with the first direction of main street vehicle traffic, 2. Conflict with the second direction of main street vehicle traffic, 3. Conflict with left-turning side street vehicle traffic, or 4. Conflict with right-turning side street vehicle traffic. Only one conflict was observed in the 3,155 crossings eval- uated in this study. That conflict had a car maneuver onto the curb to avoid another car that was stopping for a crossing pedestrian. Pedestrian Delay Two types of pedestrian delay were extracted from the videotapes by recording the difference in time between two events, as follows: • For initial delay, the difference in time between points A and B in Figure 21, recorded as the variable initial delay; and • For median delay, the difference in time between points C and D in Figure 21, recorded as the variable median delay. Table 31 summarizes the initial, median, and total pedes- trian delay by treatment. Initial pedestrian delay is highest at sites with red treatments, followed by beacons (passive and active) and refuge islands. Sites with flags, high-visibility markings, and in-street signs all had an average initial pedes- trian delay lower than 3 seconds. Median pedestrian delay for all sites was very low, except for those with refuge islands. Sites with HAWK signals were the only other sites to have an aver- age median pedestrian delay higher than 1 second. 58 Initial Delay (s) Median Delay (s) Total Delay (s) Treatment Avg StdDev Avg StdDev Avg StdDev Count Flag 2.67 3.37 0.10 0.37 2.72 3.39 350 Half 16.88 19.78 0.69 3.04 17.06 19.70 342 Hawk 7.80 7.86 1.83 6.21 9.63 9.60 224 HiVi 1.86 4.08 0.53 2.35 2.39 4.88 606 InSt 2.09 3.67 0.09 0.86 2.15 3.78 310 Msig 26.35 27.67 0.00 0.00 26.35 27.67 393 OfPa 5.54 9.47 0.10 1.12 5.62 9.59 164 OfPb 5.44 6.61 -- -- 5.44 6.61 254 Refu 5.36 10.20 3.86 11.47 9.22 16.21 512 Grand Total 8.12 15.46 1.36 6.41 9.01 16.29 3155 Table 31. Pedestrian delay by treatment.

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TRB's Transit Cooperative Research Program (TCRP) and National Cooperative Highway Research Program have jointly produced and published Improving Pedestrian Safety at Unsignalized Crossings. The product, which can be referred to as TCRP Report 112 or NCHRP Report 562, examines selected engineering treatments to improve safety for pedestrians crossing high-volume and high-speed roadways at unsignalized locations. The report presents the edited final report and Appendix A. TCRP Web-Only Document 30/NCHRP Web-Only Document 91 (Pedestrian Safety at Unsignalized Crossings: Appendices B to O) contains the remaining appendixes of the contractor's final report.

A summary of TCRP Report 112/NCHRP Report 562 as published in the July-August 2007 issue of the TR News is available online.

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