Indexes and models to analyse road safety within the aggregate approach.

The use of aggregate/disaggregate models to analyse accidents is discussed. Models for the analysis of accidents can be specified at different aggregation/disaggregation levels, also due to the availability of the relative data base. Generally, the use of disaggregate models presupposes the realization of specific data base collections, in order to register all accident data, at a microscopic level, that occur in a prefixed area and in a time interval as large as to obtain a representative sample of the event. On the contrary, the construction of aggregate models does not require this kind of data collection, because of the macroscopic level of the analysis; in this case the data collected by police (or other institutional figures) allow a preliminary analysis to be carried out, and they can be used to obtain the probability that an accident will occur in a prefixed area as a function of the mobility characteristics, the transport system and the socio-economic characteristics of the area itself. On the basis of the previous considerations, in this paper after an analysis of the main aggregate approaches used to study road accidents, a model has been specified and calibrated for a prefixed area, by using official sources of data. Furthermore, a comparison among safety indexes and calibrated models, by using the same data base, has been carried out. Two kinds of models have been specified and calibrated, referring to roads and intersections. In fact, it has been considered that this separation can implicitly consider the specific behaviour of users in the two different conditions. The results in terms of goodness of fit of the specified models are very satisfactory and some interesting considerations have been carried out in terms of link between accidents and explanatory variables. Similarly interesting results have been obtained in terms of indexes, particularly in terms of time-space variations. Further developments concern the estimation of a behavioural probabilistic model to analyse and forecast the number of accidents given the transport network and the socio-economic characteristics. For the covering abstract see ITRD E126595.