Figures (1)  Tables (2)

    • Figure 1. 

      Questionnaire survey results of the impact distribution of different causal factors that trigger pedestrians' red-light running behaviors.

    • Facet Factors B Standard error Significance Exp (B)
      Demographic-discipline Gender −0.166 0.067 0.013 0.847
      Age −0.211 0.087 0.042 0.847
      Education level −0.185 0.089 0.039 0.831
      Occupation −0.005 0.024 0.827 0.995
      Familiarity with regulations 0.101 0.039 0.009 1.106
      Self-discipline Importance of a trip 0.758 0.088 < 0.001 2.135
      Familiar with an intersection 0.469 0.078 < 0.001 1.1598
      proximity to a scheduled time 0.504 0.073 < 0.001 1.656
      External-discipline Presence of a companion 1.173 0.076 < 0.001 3.231
      Leniency of law enforcement 0.562 0.084 < 0.001 1.755
      Environmental-discipline Long waiting time 0.797 0.072 < 0.001 2.220
      Attractive destinations on the opposite side of the intersection 0.152 0.072 0.033 1.164
      Low road traffic 0.748 0.074 < 0.001 2.113
      Constant −1.258 0.259 < 0.001 0.284

      Table 1. 

      Results of the binary logistic regression to assess the impact of individual factors, together with their combinations, on pedestrians' red-light running behaviors.

    • Methods Precision Recall AUC
      Naïve bayes 87.4% 87.3% 87.4%
      Logistic regression 87.7% 87.6% 93.2%
      Multi-layer perceptron 88.3% 88.2% 94.6%
      Decision tree 88.4% 88.8% 94.9%
      Random forest 88.9% 88.8% 95.2%

      Table 2. 

      The supervised learning performances of the chosen classifiers based on the extracted feature engineering.