Does Temporal Aggregation of Data Matter?

Most accident prediction models belong to the count data regression models, in particular the negative binomial model, which assumes all data or cases are statistically independent. This assumption, however, may be violated when repeated observations over multiple periods (e.g., yearly accident counts) at the same locations (e.g., intersections and road segments) are used as independent cases in model calibration. A common solution for avoiding this problem is to aggregate data from multiple periods for each location into a single observation (e.g., combining monthly data into yearly data or combining multiyear observations into a single-year average). This aggregation treatment addresses the issue of data correlation but will likely result in loss of information and reduction in sample size (3). This paper introduces a multilevel regression approach to capturing the clustered nature of some accident data. The investigation focuses on two specific questions: (a) What is the impact of using disaggregate modeling approach? and (b) Do multilevel models give substantively different results than a single-level model? A data set compiled for winter road safety is used to examine these questions (4, 5). This accident database includes hourly accident counts for individual winter snowstorms on four highway sections in Ontario. This unique data structure allows development and comparison of models of two levels of aggregation: aggregated event-based and disaggregated hourly based models. The hourly observations are used to calibrate and compare single-level and multilevel models.