Abstract: Identification of accident black spots has gained tremendous popularity among road agencies and safety specialists for evaluating and subsequently enhancing road traffic safety. However, there is still limited understanding of the internal relationship between black spots and microscopic vehicle kinetic parameters. To address this gap, this paper describes a project that was undertaken using the real-time tire force data (kinetic response) obtained from road experiments on Wenli Expressway. First, factor analysis was applied to extracted three independent indicators (power-braking, handling stability, and ride comfort) from seven original kinetic indicators with multiple collinearities. Afterward, the main indicators were given vehicle kinetic meaning by analyzing the characteristics of original indicators associated with them. A compelling correlation was established among kinetic parameters, vehicle running qualities, and accident risk. Additionally, an integrated evaluation framework was established to identify accident black spots based on applying ordered logit models and PLS-entropy-TOPSIS approaches. The recognition results exhibited that the overall recognition accuracy obtained by the latter was found to be comparable to that achieved using the previous one. The compound evaluation model proposed in this paper has been proven to present many advantages for black spot identification. It is evidently clear from the findings that the vehicle kinetic parameters have significant correlations with road accident risk. This paper could provide some insightful knowledge for identifying and preventing the black spots from ameliorating traffic safety.