Andrea Papola

RANDOM UTILITY MODELS WITH IMPLICIT AVAILABILITY/PERCEPTION OF CHOICE ALTERNATIVES FOR THE SIMULATION OF TRAVEL DEMAND

Random utility models are undoubtedly the most used models for the simulation of transport demand. These models simulate the choice of a decision-maker among a set of feasible alternatives and their operational use requires that the analyst is able to correctly specify this choice-set for each individual. Some early applications basically ignored this problem by assuming that all decision-makers chose from the same pre-specified choice-set. This assumption may be unrealistic in many practical cases and cause significant misspecification problems (P. Stopher, Transportation Journal of ASCE 106 (1980) 427; H. Williams, J. Ortuzar, Transportation Research B 16 (1982) 167). The problem of choice-set simulation has been dealt within the literature following two basically different approaches: • simulating the perception/availability of an alternative implicitly in the choice model, • simulating the choice-set generation explicitly in a separate model. The implicit approach is more convenient from an operational point of view, while the explicit one is more appealing from a theoretical point of view. In this paper, a different approach to the modeling of availability/perception of alternatives in the context of random utility model is proposed. This approach is based on the concept of intermediate degrees of availability/perception of each alternative simulated through a model (or “inclusion function”) which in turn is introduced in the systematic utility of standard random utility models. This model, named implicit availability/perception (IAP), may be differently specified depending on assumptions made on the joint distribution of random residuals and the way in which the average degree of availability/perception is modeled. In this paper, a possible specification of the IAP model, based on the assumption of random residual distributed as i.i. Gumbel and with the average degree of availability/perception modeled as a binomial logit, is proposed. The paper also proposes ML estimation models in two cases: in the first, only information on alternatives choices is available, while in the second, this information is complemented with others on variables related to a latent (i.e., non-observable) alternatives availability/perception degree (e.g., information on car availability of decision-maker i used as an indirect measurement of the unknown and non-observable availability/perception degree of alternative car for decision-maker i in a modal split). The proposed specification is tested on mode choice data; the calibration results are compared with those of a similar logit specification with encouraging results in terms of goodness of fit.

A joint mode-transit service choice model incorporating the effect of regional transport service timetables

Transit systems offer substantially different services in urban and inter-urban areas; this usually leads to different assumptions about user behaviour in path choice modelling. These differences have not yet been recognised in mode choice modelling. Consistent with the above, in this paper we propose a joint mode-transit service choice model where the elementary alternatives are specific services of the available transit modes together with the private car. Among the attributes of transit services we propose inclusion of the difference between the desired arrival time of the traveller and the arrival time of the specific service (early/late arrival penalty), instead of service/line frequency. Different utility specifications were tested and their results compared for different correlation structures (both nested and cross-nested). In general a high significance was observed in the case of the early/late arrival penalty parameters, indicating the importance of such factors in mode choice.

Multi Regional Input-Output Models for Freight Demand Simulation at a National Level

This paper discusses the analysis of freight transport and economic effects of infrastructural investments in Southern Italy, through the application of a recently updated MRIO-based Italian freight DSS (Marzano and Papola 2004, Cascetta et al. 2005). First, the paper proposes an original state-of-the-art systematization of input-output models for freight demand simulation at a national level. Then, generalizations allowing for elasticity both in trade and in technical coefficients are discussed, together with further research perspectives. Finally, a modification of the RUBMRIO model (De La Barra 1989) is applied for infrastructural investments appraisal in Southern Italy, focusing also attention on the impacts on the overall national economy.

A Network Generalized Extreme Value Model for Route Choice Allowing Implicit Route Enumeration

Medium to large urban networks are normally characterized by a high number of origin-destination pairs that are connected by a large amount of strongly overlapping routes. This article introduces an adaptation of the Network Generalized Extreme Value (GEV) model for modelling joint choices, named Joint Network GEV (JNG), and its application to the route choice context, named Link-Based JNG (LB-JNG), and assumes the choice of a route as the joint choice of all links belonging to that route. The LB-JNG model aims at reproducing the effects of routes overlapping with a theoretical robust framework (since it belongs to the Network GEV, to date the most flexible closed-form model in reproducing covariances), allowing at the same time for easy application to real networks through a closed-form probability statement, a proper definition of its parameters and the availability of an implicit route enumeration algorithm for network loading. An overview of the theoretical properties of the JNG model is presented in this article. The LB-JNG adaptation to route choice is described, and the capability to reproduce the effects of routes overlapping is investigated using some test networks. The article compares the performances of the proposed model with those of other route choice models available in the literature. The article proposed and tested an implicit route enumeration algorithm for macroscopic static stochastic network loading on real size networks based on a double-step generalization of Dials STOCH algorithm.

A Trip Distribution Model Involving Spatial and Dominance Attributes

Modeling a destination choice is a difficult task and often represents the weakest step in travel demand modeling. This weakness is mainly due to the high number of potential alternatives and to the limited number of available attributes. Indeed, alternative destinations in a distribution model are generally all the traffic zones identified in the zoning phase. Moreover, the most widely adopted specification of random utility (RU) destination choice models introduces just 2 categories of attributes: attractiveness attributes of the destination zone and impedance attributes reproducing the origin-destination generalized cost. Through this kind of attribute alone it is quite difficult reproducing the real choice context faced by the decisionmaker, who generally knows only a limited part of the study area with sufficient detail to evaluate its attractiveness and the generalized transport costs of reaching it. In this regard, this paper proposes 2 sets of new dummy-like attributes to be used within the destination choice models to identify, within the whole choice set, a smaller subset of zones (those with nonzero value of dummy-like attributes), more or less likely to be perceived. The former are generated by extending and applying the concept of dominance among alternatives to the framework of RU theory and can be used to identify a set of alternatives less likely to be perceived (exclusion variables) whose systematic utility will be penalized as a function of these variables. The latter are spatial variables reproducing better knowledge of zones with a privileged spatial position and can be used to identify a set of alternatives more likely to be perceived (selection variables) whose systematic utility will be improved as a function of these variables. These new attributes are tested on empirical data related to nonsystematic trips in Rome. It is also important to underline that the proposed dominance variables can be conveniently used in any other choice context.

Impact of E-Economy on Traffic and Traffic-Related Indicators in Urban Areas

As part of the Prediction of E-Economy Impacts on Transport (POET) project for the European Commission, the impact of the e-economy (including an increasing uptake of teleworking and teleshopping and better use of intelligent transport systems) on transport in a number of selected urban areas in Europe was modeled. The modeling for the urban areas used existing transport models (passengers and freight) for Paris; Stockholm, Sweden; Naples, Italy; Hamburg, Germany; and the Randstad, the Netherlands. Second, it used several scenarios on the degree to which information and communication technologies would be adopted for the year 2010. The third element in the modeling at the urban level was the use of so-called front-end models, estimated on new stated preference data. The outcomes in vehicle kilometers were also used to calculate the impact of the e-economy on energy use, emissions, and traffic accidents. Also, for some areas, impact on congestion and accessibility was calculated. For some of the areas and scenarios, considerable reductions in passenger kilometerage were found as a result of e-economy developments, but freight transport increased.

Effectiveness of Origin–Destination Matrix Correction Procedure Using Traffic Counts

The estimation and correction of the origin-destination (O-D) matrix from traffic counts is a classical procedure usually adopted in transport engineering by practitioners to improve the overall reliability of transport models. Recently, Papola and Marzano showed through laboratory experiments that this procedure is generally not able to provide an effective correction of the O-D matrix. This result can be justified from a theoretical standpoint because of the lower number of stochastic equations (independent observed link flows) with respect to the unknowns (O-D flows). The current study confirms, first, that this circumstance represents the main reason for failure of the procedure by showing that good correction is generally obtained when the number of equations is greater than the number of unknowns. Then, since this circumstance does not occur in practice, in which the number of O-D pairs is usually much greater than the number of link counts, the study explores alternative assumptions and contexts that allow for a proper balance between unknowns and equations. In more detail, this balance can be achieved by moving to within-day dynamic contexts, in which a much larger number of equations is generally available. Obviously, to bound the corresponding increase in the number of unknowns, specific reasonable hypotheses in O-D flow variation across time slices must be introduced. In that respect, the effectiveness of the O-D matrix correction procedure in the usually adopted linear hypothesis on the dynamic process evolution of O-D flows and under the assumption of constant distribution shares is analyzed.

A schedule-based methodology proposal for Sea Motorways feasibility evaluation

In order to evaluate the economic and technical feasibility of new Sea Motorways services a schedule-based methodology is proposed as an extension of a recently developed and applied methodology.

The importance of choosing appropriate random utility models in complex choice contexts

This research proposes a comparative analysis of the performance of various random utility models (RUM) — namely Multinomial Logit, Nested Logit, Cross Nested Logit, FinMix and CoNL-estimated on a synthetic dataset with variable sample size and correlation patterns. This experimental framework allows comparing model estimates in a fair, controlled environment wherein all relevant characteristics (coefficients, attributes, covariances, likelihood, elasticities) of the “true” underlying model are known. Models are validated especially by comparing true and estimated market share elasticities where the market share is the sum over all observations of the individual probabilities of a given alternative. Indeed, this indicator represents the real forecasting capability of a model, that is the main target for the analyst. Moreover, its true value can be computed when dealing with a synthetic database by evaluating the difference in the number of choices of a given alternative between future and current scenarios, due to a difference in some attributes value. Comparisons are carried out on several choice contexts, characterized by different correlation matrices and variable sample size.

Quantitative overview of efficiency and effectiveness of public transport in Italy: The importance of using ITS

This paper reports on a summary overview of a study aimed at assessing the health of the Italian Local Public Transport system. In close cooperation with the Italian Ministry of Infrastructures and Transport, more than 500 Italian local public transport operator, covering most of Italian cities, were analyzed, to investigate their efficiency and effectiveness. All relevant economic and operational (i.e. transport-related) characteristics of the companies have been quantified through a set of performance indicators. The paper illustrates some of them, aggregated at a regional level, showing a considerable heterogeneity amongst regions and providing preliminary policy recommendations. Concluding remarks are drawn based upon these indicators, showing in particular how diffusion of Intelligent Transportation Systems — especially for ticketing, monitoring and enforcement — is crucial to improve system performances.