Enhancing Pedestrian Volume Estimation and Developing HCM Pedestrian Methodologies for Safe and Sustainable Communities (2022) / Chapter Skim
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100 Chapter 4. Findings and Applications Introduction This chapter presents the results and findings from the project's investigations into three aspects of pedestrian QOS: Pedestrian satisfaction crossing roadways, with and without the presence of selected pedestrian safety countermeasures; Updating the HCM 6th Edition pedestrian delay estimation methods for signalized and unsignalized crossings; and Investigating methods for evaluating pedestrian network QOS.
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101 Table 4-1 also shows that there is little clear difference in pedestrian-stated satisfaction between RRFB and median island crossings, while there does seem to be higher satisfaction for marked crosswalks compared to unmarked. Table 4-2 also shows that in general most respondents (75%)
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102 Table 4-3. Pedestrian Satisfaction at Unsignalized Intersections by Speed Limit.
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103 Table 4-5 shows the age and gender of the 90% of respondents who indicated their age category and male or female gender. Most of the Chapel Hill respondents were in the youngest age group (under 25)
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104 Trip Purpose Among those who agreed to take the survey, 86% indicated their trip purpose. As shown in Table 4-6, most (55%)
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105 Table 4-8. Trip Length.
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106 Table 4-10. Level of Agreement with Statements.
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107 Table 4-12 summarizes how pedestrian-stated crossing satisfaction seems to be related to survey respondents' agreement with the statements. Some statements had strong inverse relationships, while others were positively related, and others did not have a clear relationship.
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108 pedestrians (65%) who participated in the survey were not distracted.
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109 Table 4-15. Pedestrian Crossing Satisfaction when Delayed due to Motorist.
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110 Table 4-18. Pedestrian Crossing Satisfaction Relationship with Interactions.
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111 Table 4-19. Rotated Factor Loadings for Eight Survey Response Questions at Signalized Sites.
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112 199 observations; the limited distribution of "very dissatisfied" responses required collapsing both "very dissatisfied" and "dissatisfied" responses into a single "dissatisfied" category. This "dissatisfied" category of the response variable (experience_cat)
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113 The negative signs on the estimates for the left-turning volume from the minor road indicate that increases in left-turning traffic are associated with a decrease in the log odds of being either satisfied or very satisfied with the crossing experience compared to being dissatisfied. These results are also intuitive and indicate potentially that a high number of vehicles turning into the path of crossing pedestrians is associated with pedestrian dissatisfaction.
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114 crossing conditions for respondents in Chapel Hill. However, Portland's tradition of providing abundant crossing time to pedestrians and minimizing delays by keeping cycle lengths short at signalized intersections may partially account for differences observed in crossing satisfaction between pedestrians in Chapel Hill vs.
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115 The results of this factor analysis differ significantly from that of the signalized site factor analysis. Only one of the four intended constructs emerged as a factor, and instead two more latent factors presented themselves within the data.
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116 Table 4-23. Logistic Regression Model for Unsignalized Sites with Survey Results.
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117 delayed in their trips, satisfaction with the crossing experience increases. This finding may indicate that providing enough access and crossing opportunities that allow pedestrians to experience little delay are more important than treatment type, especially considering that treatment type was not a significant variable in the model.
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118 Table 4-24. Logistic Regression Model for Unsignalized Sites without Survey Results.
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119 Findings This analysis shows that pedestrian-stated crossing satisfaction is higher at RRFB and median island crossings compared to unmarked (or even marked) crosswalks, but these are far from the only influence on pedestrian-stated crossing satisfaction.
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120 Table 4-25. Descriptive Statistics for Unsignalized Sites with Median.
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121 yielding rates on the far side were higher than yielding rates on the near side. This was probably due to the presence of the median, which gave drivers additional time to react and yield to the crossing pedestrians.
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122 Table 4-26. Descriptive Statistics for Unsignalized Sites without Median.
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123 Table 4-27. RRFB and Control Sites Descriptive Statistics.
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124 Table 4-28. Median Island and Control Sites Descriptive Statistics.
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125 Table 4-29. Signalized Sites Descriptive Statistics.
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126 Naturalistic Walking Study Model Development After a 7-day period, participants produced linked Empatica E4 streaming and SpyTec GPS data for a total of 21 walking trips. Among these 21 trips, 9 recorded participants' GPS location data at 5-second intervals; the remaining 12 trips provided GPS location information at 1-minute intervals.
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127 Note: * denotes variables that maintained a statistically significant association with participants' EDA averaged over 5-second and 1-minute intervals.
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128 Table 4-31. Multilevel Mixed-Effects Generalized Linear Model Results for Participants' EDA and HR, with a Random Interval Fit at the Level of Participants' Trips (n = 21)
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129 Note: * denotes variables that maintained a statistically significant association with participants' HR averaged over 5-second and 1minute intervals.
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130 As background noise has been shown to increase cognitive load (Herweg and Bunzeck 2015; Meister et al.
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131 Crossing width: 52 ft Pedestrian walking speed: 3.5 ft/s Crosswalk width: 10 ft Pedestrian start-up time: 3 s Pedestrian flow rate: 20 ped/h Traffic volume: 100 to 1100 veh/h Traffic volume was varied over the range of values indicated in the list above. Pedestrian delay was computed for each volume level.
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132 a. Proportion motorists yielding equal to 1.0.
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133 Findings This section summarizes the findings from an evaluation of the revised pedestrian delay prediction methodology. The evaluation examines the sensitivity of the predicted delay to traffic volume.
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134 a. Proportion motorists yielding equal to 1.0.
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135 a. Proportion motorists yielding equal to 1.0.
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136 Proposed Motorist Yield Rates This section presents the proposed motorist yield rates for selected engineering treatments at uncontrolled pedestrian crossings. The literature review findings in Table 3-12, combined with additional data developed from the Task 6D video observations (described above)
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137 Validation of Pedestrian Delay Method for Uncontrolled Crossings Database Summary This section provides a summary of the data collected at each site during each study period. The first subsection describes the study site location and geometry.
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138 streets (one at a time)
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139 Motorist yield rates are shown in the last column of Table 4-35. In general, one rate was computed for each combination of site and study period.
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140 Table 4-35. Traffic Characteristics at the Subject Crossing -- Motorized Vehicles.
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141 Table 4-36. Traffic Characteristics at the Subject Crossing -- Pedestrians.
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142 Findings This section documents the findings from the validation analysis. It consists of two subsections.
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143 Pedestrian Delay The revised model (described in Chapter 3) was used to calculate the average pedestrian delay for each of the site and study period combinations.
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144 Figure 4-7. Comparison of Predicted and Measured Delay -- Crossings without a Left-turn Lane.
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145 The data in Figure 4-7 show that the revised model can provide an unbiased prediction of the delay for crossings that do not include a left-turn lane. The R2 of 0.36 suggests that the predictive model explains about 36 percent of the variability in the measured delay data.
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146 Pedestrian delay incurred in walking parallel to the segment, dpp: equal to 0.1 × L/Sp Roadway difficulty crossing factor, Fcd: 1.0 The intersection LOS score (which equals the link score) was varied over the range of 1 to 6.
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147 The sensitivity analysis findings described in this section are based on the evaluation of pedestrian segment LOS score for a street segment. The following list identifies the input variables and values: Pedestrian LOS score for intersection, Ip,int: 1, 2, 3, 4, 5, 6 Pedestrian LOS score for link, Ip,link: equal to Ip,int Walking speed, Sp: 4 ft/s Segment length, L: 330 ft Pedestrian delay incurred in walking parallel to the segment, dpp: equal to 0.1 × L/Sp Roadway difficulty crossing factor, Fcd: 0.8, 1.2 The intersection LOS score (which equaled the link score)
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148 compute the roadway crossing difficulty factor. This factor is then used in Equation 52 and Equation 54 to compute the segment LOS score for each of the two methodologies.
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149 Sensitivity Analysis This section summarizes the findings from an evaluation of the revised pedestrian segment LOS prediction methodology. The evaluation examines the sensitivity of the predicted segment LOS score to changes in link LOS score, roadway crossing difficulty, and segment length.
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150 Figure 4-13. Influence of Roadway Crossing Difficulty on Segment LOS -- Proposed Changes.
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151 a. Link LOS Score and Intersection LOS Score equal to 1.0.
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152 Pedestrian Network LOS Comparison of PLOS and PLTS The research team applied the HCM PLOS and ODOT PLTS methodologies to collector and arterial roadway segments for the entire state of Florida, using GIS shapefiles obtained from the FDOT Transportation Data and Analytics Office (https://www.fdot.gov/statistics/gis/)
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153 Table 4-37. Data Obtained from FDOT for PLOS and PLTS Calculation.
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154 Figure 4-15. PLTS for Roadway Segments in Tampa.
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155 Figure 4-16. PLOS for Roadway Segments in Tampa.
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156 Quantifying Network Connectivity As a proof-of-concept of using PLTS (for roadway segments) and the NCHRP 17-87 pedestrian satisfaction model (for uncontrolled crossings)
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