Transport analysis spans many scales. At one extreme may be the design oftraffic signals at an urban intersection; at a wider geographical level, transport planning over a city network may be required to examine the impact on route choice and travel demand of measures such as road user charging. At a still wider level, analysis may need to address problems of regional or national impact, such as the introduction of a national road user charging system. An element in all such analyses is that as the geographicalscope of the problem changes, so does the scale of the models used, in terms of the fidelity of network representation, the size of the zones over which trip demands are modelled, and the level of disaggregation of traveller responses by, say, socio-economic group. The question then naturally arises: are these models all consistent in some sense, across the differentaggregation scales? Such questions of scale and model aggregation are highly relevant in practice. Looking to past research on network aggregation mostly reveals empirical reports simply demonstrating that the level of aggregation alters model prediction. A systematic methodology was established in order to make the transition from a disaggregate representation to anequivalent aggregate representation of the same network. Specifically, toachieve some generality, the focus is the aggregation of the Stochastic User Equilibrium (SUE) model, which is well known to approximate the more widely used User Equilibrium (UE) model to an arbitrary accuracy by suitable choice of perception error variances. Via sensitivity analysis of the SUE fixed point condition, the method analyses the impact of changes to the origin destination (OD) travel demand matrix on mean perceived OD travel costs (satisfactions). The process of re-equilibration at each stage is implicitly embedded in the sensitivity analysis, avoiding the need to re-solve equilibrium at many points. This yields an explicit functional relationship between OD demands and OD satisfactions. For those familiar with the discrete choice modelling field, it is noted that the procedure for aggregating the network supply-side is conceptually similar to the process by which aggregation or 'nesting' of alternatives is addressed in travel demand models; in this analogy, the log sum takes the role of our 'OD satisfactions'. There are several ways in which this aggregation method may then be subsequently applied in network analysis. The illustration used in the paper considers the interaction between a demand model operating on a simplified aggregate network, and a detailed highway network model. The application considers a problem of mode choice for urban commuters between train andcar. The approach is to focus on a particular OD movement of interest, and agglomerate all other OD movements as an overall 'traffic intensity', which reflects that as demands on other movements grow they may delay the travel of the OD movement of interest. The resulting problem is an aggregation/simplification of the original problem, consisting of only two dimensions of 'demand flow' and two dimensions of 'travel cost', compared with themany OD movements, links and paths in the original network. Numerical results are reported, comparing the aggregate and disaggregate approaches to the combined mode choice/network assignment problem. For the covering abstract see ITRD E145999
Samenvatting