## 3 Results
### Portfolio Analysis of Influencing Factors  
Unlike necessity analysis, configuration analysis aims to find the configurations composed of conditions leading to the results. Based on existing research [31; 40; 41; 42], \"0.8\" and \"1\" were set as the raw consistency and case thresholds, and PRI consistency was set to \"0.75\". The results were analyzed by outputting complex, parsimonious, and intermediate solutions. In this paper, intermediate solutions [26; 28], with both representativeness and parsimony, were selected for analysis, supplemented by parsimonious solutions. The conditions jointly contained in the parsimonious and intermediate solutions are called \"core conditions\", and those contained only in the parsimonious solution are called \"auxiliary conditions\". The antecedent condition configurations leading to safety accidents are shown in Table 5.  
From Table 5, we found that the total consistency was 0.96141, which meets the requirement; the total coverage was 0.571208, indicating that the cases explain 57.12% of the reasons for safety accidents. Eleven paths that may lead to safety accidents were obtained, with consistency greater than 0.8. That is, all of them were sufficient conditions leading to safety accidents. Each path contains more than three antecedent conditions, indicating that multiple factors caused the safety accident. From a lateral perspective in the table, BS as the core condition was contained in most configurations, indicating its importance for the safety accident. In addition, safety accidents were influenced by all antecedent conditions, which appear in all configurations, and from a longitudinal perspective, the eleven paths can be summarized into four types according to the distribution of core conditions.  
Type 1 contains paths S1-S3, which can be simplified to: AG*-LI*(DE*MM*-BS*RG*WE + -DE*MM*BS*-RG*-WE + DE*MM*BS*-RG*WE). AG and \(\sim\)LI were the core conditions common to this type, which shows that older drivers are more likely to cause accidents during the day. This is related to the higher transport turnover during the day and greater safety risk with higher turnover.  
Type 2 contains paths S4-S5, which can be simplified to: MM*WE*(DE*-BS*RG*LI + -AG*BS*RG*-LI). MM and WE were the core conditions common to this type, which shows that in bad weather conditions, the higher the degree of individualization of the management model, the more likely the safety accident.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Antecedent Condition** & \multicolumn{2}{c}{**Outcome Variable**} \\  & **Consistency** & **Coverage** \\ \hline DE & 0.689594 & 0.579259 \\ \(\sim\)DE & 0.543210 & 0.724706 \\ AG & 0.635802 & 0.658748 \\ \(\sim\)AG & 0.690917 & 0.708729 \\ MM & 0.778660 & 0.744519 \\ \(\sim\)MM & 0.585097 & 0.654339 \\ BS & 0.589065 & 0.607273 \\ \(\sim\)BS & 0.410935 & 0.423636 \\ RG & 0.896825 & 0.580645 \\ \(\sim\)RG & 0.292328 & 0.739130 \\ WE & 0.470899 & 0.508571 \\ \(\sim\)WE & 0.529101 & 0.521739 \\ LI & 0.411199 & 0.564836 \\ \(\sim\)LI & 0.719753 & 0.593837 \\ \hline \hline \end{tabular} Note: \(\sim\), absence.  
\end{table}
Table 4: Necessity detection of condition.  
Type 3 contains paths S6-S7, which can be simplified to: BS*-WE*LI*(-DE*AG*-MM*RG + DE*AG*MM*RG). BS, -WE, and LI were the core conditions common to this type, which shows that in sunny and cloudy weather, passenger transport at night is more likely to lead to safety accidents.  
Type 4 contains paths S8-S10, simplified to: -AG*BS*(-DE*-MM*RG*WE*LI + -DE* MM*RG*-WE + -DE*RG*-WE*-LI). -AG and BS were the core conditions common to this type, which shows that younger drivers are more likely to lead to safety accidents. It reflects that young drivers, although full of energy, were more likely to lead to safety accidents due to lacking calm responses when encountering an emergency.