Traffic accident risk perception among drivers: a latent variable approach

ABSTRACT

Governments require decision tools to deal with road traffic accidents, a pandemic resulting in millions of deaths around the world. Evidence shows that human factors are one of the major causes of road accidents, and there is much interest in identifying variables that may have an impact on drivers’ perception of risk. To this aim, we design a stated choice experiment with eight hypothetical driving scenarios considering attributes that have been strongly associated with increased accident risks: (i) driving speed, (ii) driving the wrong way in a one-way street, (iii) overtaking on a bend, and (iv) driving under the influence of alcohol or drugs. Data from a sample of survey respondents are used to estimate a hybrid discrete choice model incorporating two latent variables, Driver Concentration and Safe Driving. Our results may contribute to the design of public policies geared to prevent accidents by encouraging safer driving behaviour.     

Mixed‐mode travel demand forecasting techniques

From an extensive review of literature covering 34 cases where mixed‐mode policies had been used, ranging from strategic area wide to local studies, it was concluded that none of the techniques adopted had adequately dealt with the particular problem of mixed modes.

Two philosophies were identified, a market research approach and formal simulation modelling. The latter ranged from coarse zonal diversion curve models, to individual based multi modal split models. The limited information given in the reports prevented making practical comparisons between the models.

Monte‐Carlo simulation was used in order to look in more depth at the multi‐modal choice model structures encountered in the review. It was found that the multi‐nomial logit, which is the most commonly used model, was quite robust but performed badly when modal costs were highly correlated, a case which may occur far too often.     

Fundamentals of discrete multimodal choice modelling

Abstract

In this paper an overview is given of the most relevant issues relating to the application of multimodal choice models, with particular emphasis on disaggregate modal split models. The paper considers questions of data, such as type of data, alternative sampling strategies and problems of measurement; and modelling issues, such as model specification and estimation, including a good presentation of the statistical techniques'available. The paper also addresses the aggregation problem, which lies at the heart of one of today's most hotly contested debates: whether to use aggregate or disaggregate models for policy analysis, and in which circumstances.