Identifying High-Risk Intersections for Walking and Bicycling Using Multiple Data Sources in the City of San Diego

<div class="floats-full-page floats-full-page-other"><h2>Table 1</h2><div class="floats-full-page-caption"><div>Negative binomial regression model for pedestrians.</div></div><div class="floats-full-page-content"><table class="fixed-width table-group" id="tab1"><tr><td><table class="table"><colgroup><col style="width:42.08em" /><col style="width:5.13em" /><col style="width:3.55em" /><col style="width:3.50em" /></colgroup><tr><td class="thead-hr" colspan="4"><hr /></td></tr><tr class="thead"><td class="align_left">Variable</td><td class="align_center">Estimates</td><td class="align_center">t-stat</td><td class="align_center">Sig.</td></tr><tr><td class="thead-hr" colspan="4"><hr /></td></tr><tr><td class="align_left">(Intercept)</td><td class="align_center">4.865</td><td class="align_center">23.24</td><td class="align_center">0.000</td></tr><tr><td class="align_left">Transit stop density (0.5 miles)</td><td class="align_center">8.782</td><td class="align_center">3.81</td><td class="align_center">0.000</td></tr><tr><td class="align_left">Percentage of regular transit rider, pedestrian, or bicyclist population (0.25 miles)</td><td class="align_center">3.717</td><td class="align_center">1.95</td><td class="align_center">0.051</td></tr><tr><td class="align_left">Employment density (0.25 miles)</td><td class="align_center">0.051</td><td class="align_center">2.41</td><td class="align_center">0.016</td></tr><tr><td class="align_left">Maximum speed limit within the intersection less than 40 mph</td><td class="align_center">1.135</td><td class="align_center">5.37</td><td class="align_center">0.000</td></tr><tr><td class="align_left">Percentage of vacant housing units (0.5 miles)</td><td class="align_center">−3.517</td><td class="align_center">−2.99</td><td class="align_center">0.003</td></tr><tr><td class="align_left">Total commercial or mixed-use land area (0.1 mile)</td><td class="align_center">0.190</td><td class="align_center">5.01</td><td class="align_center">0.000</td></tr><tr><td class="align_left">If the area contains a higher crime count than the average crime counts among the buffers (0.25 miles)</td><td class="align_center">−0.292</td><td class="align_center">−1.65</td><td class="align_center">0.098</td></tr><tr><td class="align_left" colspan="4"><hr /></td></tr><tr><td class="align_left">N</td><td class="align_center" colspan="3">45</td></tr><tr><td class="align_left">R-squared</td><td class="align_center" colspan="3">0.70</td></tr><tr><td class="align_left">RMSE</td><td class="align_center" colspan="3">1633.24</td></tr><tr><td class="align_left">MAE</td><td class="align_center" colspan="3">1147.41</td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr" /></td></tr></table></td></tr></table></div></div>

Table 1 | Identifying High-Risk Intersections for Walking and Bicycling Using Multiple Data Sources in the City of San Diego

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Table 1: Identifying High-Risk Intersections for Walking and Bicycling Using Multiple Data Sources in the City of San Diego

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Table 1

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