The principal excitation to a vehicle's chassis system is the road profile. Simulating a vehicle traversing long roads is
impractical and a method to produce short roads with given characteristics must be developed. There are many methods
currently available to characterize roads when they are assumed to be homogeneous. This work develops a method of
characterizing non-stationary road profile data using ARIMA (Autoregressive Integrated Moving Average) modeling
techniques. The first step is to consider the road to be a realization of an underlying stochastic process. Previous work
has demonstrated that an ARIMA model can be fit to non-stationary road profile data and the remaining residual
process is uncorrelated. This work continues the examination of the residual process of such an ARIMA model.
Statistical techniques are developed and used to examine the distribution of the residual process and the preliminary
results are demonstrated. The use of the ARIMA model parameters and residual distributions in classifying road
profiles is also discussed. By classifying various road profiles according to given model parameters, any synthetic road
realized from a given class of model parameters will represent all roads in that set, resulting in a timely and efficient
simulation of a vehicle traversing any given type of road.
Load data representing severe customer usage is needed throughout a chassis development program; the majority of these chassis loads originate with the excitation from the road. These chassis loads are increasingly derived from vehicle simulations. Simulating a vehicle traversing long roads is simply impractical, however, and a greatly reduced set of characteristic roads must be found. In order to characterize a road, certain modeling assumptions must be made. Several models have been proposed making various assumptions about the properties that road profiles possess. The literature in this field is reviewed before focusing on two modeling assumptions of particular interest: the stationarity of the signal (homogeneity of the road) and the corresponding interval over which previous data points are correlated to the current data point. In this work, 2-D topographic road profiles are considered to be signals that are realizations of a stochastic process. The objective of this work is to investigate the stationarity assumption and the interval of influence for several carefully controlled sections of highway pavement in the United States. Two statistical techniques are used in analyzing these data: the autocorrelation and the partial autocorrelation. It is shown that the road profile signals in their original form are not stationary and have an extremely long interval of influence on the order of 25m. By differencing the data, however, it is often possible to generate stationary residuals and a very short interval of influence on the order of 250mm. By examining the autocorrelation and the partial autocorrelation, various versions of ARIMA models appear to be appropriate for further modeling. Implications to modeling the signals as Markov Chains are also discussed. In this way, roads can be characterized by the model architecture and the particular parameterization of the model. Any synthetic road realized from a particular model represents all profiles in this set. Realizations of any length can be generated, allowing efficient simulation and timely information about the chassis loads that can be used for design decisions. This work provides insights for future development in the modeling and characterization of 2-D topographic road profiles.