Traffic flow-accidents relationship for urban intersections on the basis of the translog function

• We analyze the potentiality of the translog function to represent the traffic flow-accidents relationship at intersections.
• We compare the translog with commonly used function forms using NB models.
• The translog function is distinct from the log-linear functions with better performance, especially at the boundary values.
• We apply random effects model to analyze the influence of omitted variables in variable bias.
• The random effects results are consistent with previous results.

In this paper, we use a flexible function to represent the traffic flow-accidents relationship for urban intersections, called translog function. Considering the common use of predictive models and given the complexity of the traffic movements that usually occur at intersections, this flexible function provides a richer interpretation of traffic flow-accidents relationship. Therefore, five functional forms commonly used are compared to the translog function by modeling accidents using data of four-legged signalized intersections. Better results in terms of goodness of fit are obtained for the translog model. In addition, sensitivity analysis shows that the translog function is distinct from the log-linear functions, especially at the boundary values, revealing the potential to capture the accident risk complexity usually existent at intersections as a result of several traffic movements. Moreover, to analyze if the omitted variables cause variable bias, and thus, affecting the previous model assessment and comparison, panel count data models are applied, namely, random effects models. The results obtained are consistent with those previously obtained, which proves that the translog model may be an alternative providing a richer interpretation of the accident occurrence at intersections.