The purpose is to study the performance compensation of the bid purchased during the mergers and acquisitions (M&A) process. An intelligent model of enterprise performance appraisal is built to analyze the performances of the acquired enterprises. First, the evaluation indicators of enterprise performance are selected from both financial and non-financial aspects. An enterprise performance appraisal model is established based on the neural networks and optimized by the factor analysis method and Genetic Algorithm (GA). The principal factors affecting enterprise performance are analyzed. Then the M&A parties’ performances during the M&A commitment period under the earnings compensation mechanism are analyzed quantitatively. Corresponding hypotheses and evaluation indicators are established. Mean test results and regression analyses demonstrate that the hypotheses proposed are valid under particular circumstances. Introducing the earnings compensation mechanism during the M&A process can improve the enterprise performance effectively so that the earnings forecasted in the commitment period are significantly higher than the historical profitability. Hence, the earnings compensation mechanism plays a positive role in guiding enterprise performance. Comparison with models proposed in previous research reveals that the output error ratio of the designed corporate performance evaluation model is 1.16%, which can effectively evaluate corporate performance. The above results provide a reference for studying the impact of the earnings compensation mechanism on enterprise performance during the M&A process.

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The COVID-19 pandemic has led to a burgeoning demand for active travel (walking or cycling), which is a healthy, pollution-free, and affordable daily transportation mode. Park green space (PGS), as an open natural landscape, have become a popular destination for active travel trips in metropolitan areas. Pedestrians and cyclists are often at high crash risk when exposed to complicated traffic environments in urban areas. Therefore, this study aims to propose a safety assessment framework for evaluating active travel traffic safety (ATTS) near PGS from the perspective of urban planning and exploring the effect of the point-of-interest (POI) aggregation phenomenon on ATTS. First, links between ATTS and the environment variables were investigated and integrated into the framework using the catastrophe model. Second, the relationship between the POI density and ATTS was investigated using three spatial regression models. Results in the Wuhan Metropolitan Area as a case study have shown that (1) the population density, road density, nighttime brightness, and vegetation situation near PGS have pronounced effects on ATTS; (2) pedestrians near PGS enjoy safer road facilities than cyclists. Active travel traffic near PGS requires more attention than non-park neighborhoods; (3) among four park categories, using active travel to access theme parks is the safest; and (4) SEM has the best fit for POI cluster research. Increases in leisure facility density and residence density may lead to deterioration and improvement in ATTS safety levels near PGSs, respectively. The safety framework can be applied in other regions because the selected environment indicators are common and accessible. The findings offer appropriate traffic planning strategies to improve the safety of active travel users when accessing PGS.

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