Dominance among alternatives in random utility models

In many discrete choice contexts the actual choice set, including the alternatives effectively perceived and considered by the decision maker, may substantially differ from the universal choice set, including all available alternatives: one of the most relevant examples within transport demand simulation is probably the choice of destination, wherein the universal choice set normally includes hundreds of traffic zones. In these cases, proper simulation of the choice set is crucial for correct simulation of the choice context.In this regard, our paper has two main objectives. The first is to give a general contribution to choice set modelling by extending and applying the concept of dominance among alternatives to the framework of random utility theory. The main result is the definition of a methodology for the generation of new dominance attributes, which can be used in choice set modelling. The second aim is to make a specific contribution to destination choice modelling: dominance attributes are defined from the above methodology and introduced into this choice context, and new spatial variables reproducing better knowledge of zones with a privileged spatial position are also proposed. Methodology and attributes are tested both on synthetic and on real data.     

Calibrating aggregate travel demand models with traffic counts: Estimators and statistical performance

Traffic counts on network links constitute an information source on travel demand which is easy to collect, cheap and repeatable. Many models proposed in recent years deal with the use of traffic counts to estimate Origin/Destination (O/D) trip matrices under different assumptions on the type of "a-priori" information available on the demand (surveys, outdated estimates, models, etc.) and the type of network and assignment mapping (see Cascetta & Nguyen 1988). Less attention has been paid to the possibility of using traffic counts to estimate the parameters of demand models. In this case most of the proposed methods are relative to particular demand model structures (e.g. gravity-type) and the statistical analysis of estimator performance is not thoroughly carried out. In this paper a general statistical framework defining Maximum Likelihood, Non Linear Generalized Least Squares (NGLS) and Bayes estimators of aggregated demand model parameters combining counts-based information with other sources (sample or a priori estimates) is proposed first, thus extending and generalizing previous work by the authors (Cascetta & Russo 1992). Subsequently a solution algorithm of the projected-gradient type is proposed for the NGLS estimator given its convenient theoretical and computational properties. The algorithm is based on a combination of analytical/numerical derivates in order to make the estimator applicable to general demand models. Statistical performances of the proposed estimators are evaluated on a small test network through a Monte Carlo method by repeatedly sampling "starting estimates" of the (known) parameters of a generation/distribution/modal split/assignment system of models. Tests were carried out assuming different levels of "quality" of starting estimates and numbers of available counts. Finally NGLS estimator was applied to the calibration of the described model system on the network of a real medium-size Italian town using real counts with very satisfactory results in terms of both parameter values and counted flows reproduction.     

A unified framework for estimating or updating origin/destination matrices from traffic counts

This paper presents an in-depth study of the methodology for estimating or updating origin-to-destination trip matrices from traffic counts. Following an analysis of the statistical foundation of the estimation and updating problems, various basic approaches are reviewed using a generic traffic assignment map. Computational issues related to specific assignment maps and estimation models for both road and transit networks are then discussed. Finally, additional insight into the relative performance of several estimators is provided by a set of test problems with varying input data.     

A new method for measuring the quality of circulation on motorways

The paper presents a method of measuring the quality of circulation worked out by processing the data derived from 155, 000 vehicle passages over two sections of two-lane, divided-carriageway motorway of identical geometric characteristics. The mass of observed data was broken down into a sequence of 293 periods of constant flow rate, each characterized by a specific flow rate and percentage of heavy vehicles. The percentage of vehicles passing on lane No. 1 and the suitable transformations of the characteristics of the speed and time headway processes on the two lanes during each of the periods gave rise to a vector of 15 components representing the circulation conditions during that period. By performing a linear transformation of the vectors for the 293 constant flow rate periods, it was possible to single out three non-correlated variables, called the “principal components”, which accounted for 75% of the total variance observed for the 15 components, and in turn permitted the representation, without excessive distortion, of the observed circulation conditions within a three-dimensional space by means of a swarm of 293 points. Analyzing the manner in which the three principal components varied jointly within the swarm, a total of four zones were identified, representing respectively free flow, stable flow, unstable flow and forced flow. Moreover, it was possible to identify the numerical value of the quality of circulation with the first principal component, which by itself accounted for 55% of the total variance. This component showed very strong correlation with the flow rate and the percentage of heavy vehicles, and thus permits among other things the mutual comparison of the conditions of circulation caused by different flow rates and compositions.