In this study, the internal area of an unsignalized intersection is divided into multiple road right points, and the road right points occupied by the traffic accident caused by the collision between the vehicle and the pedestrian or the non-motor vehicle are set as “failure points”. This work studies the traffic efficiency of the unsignalized intersection when vehicle failure occurs. The sparrow search algorithm (SSA) is selected to improve traffic efficiency, while SSA is easy to fall into local extreme points in the early stage and has low optimization accuracy in the later stage. To this end, the study introduces the improved strategy of adaptive learning parameters and level-based opposition-based learning to enhance the global search ability in the early stage and the deep exploration ability in the later stage. SSA based on adaptive parameters and level-based opposition-based learning (ALSSA) is proposed. A total of 13 benchmark test functions and the Wilcoxon rank-sum test P value are selected for verification separately. Experimental results show that ALSSA has a great improvement in global search capability and convergence compared with other algorithms. Finally, the optimal traffic time under different traffic flows of two-way two lanes, two-way four lanes, and two-way eight lanes is calculated.