Domenico Sassanelli

Modelling Parking Choice Behaviour using Possibility Theory

This article presents a discrete choice model for evaluating parking users’ behaviour. In order to explicitly take into account imprecision and uncertainty underlying a users choice process, the proposed model has been developed within the framework of Possibility Theory. This approach is an alternative way to represent imperfect knowledge (uncertainty) of users about both parking and transportation system status, as well as the approximate reasoning of the human decision maker (imprecision). The resulting model is a quantitative soft computing tool that could support traffic analysts in planning parking policies and Advanced Traveller Information Systems. In fact, effects of information on user choice can be incorporated into the model itself. Thus, we consider the parking user be a decision maker who assumes a certain choice set (set of perceived parking alternatives); the user has some information about the parking supply system and he/she associates each parking alternative with an approximate perceived cost/utility that is represented by a possibility distribution; and, finally, the user chooses the alternative which minimises/maximises his/her perceived parking cost/utility. The results show how the model is able to represent the effect of various parking policies on users’ behaviour and how the single component of parking policy affects the decision process.

A hybrid approach to combine fuzziness and randomness in travel choice prediction

Abstract Several approaches have been developed in order to deal with uncertainty, in the prediction of travellers’ choices. Uncertainty almost always affects travel alternatives in several different choice contexts. However, the way in which this uncertainty affects choice options may consistently vary. Two main types of uncertainty can be identified: randomness and fuzziness. This paper discusses the use of a hybrid approach for choice modelling of travellers’ behaviour in choice contexts that involve conditions of high variability and uncertainty. The aim of the work is to implement a hybrid methodology, based on the use of a two-term hybrid utility, which allows taking into account, at the same time, the effects of both random and fuzzy uncertainty on travellers’ choice behaviour. An application of the proposed methodology to a transit choice context is then presented.

An Adaptive Neuro-Fuzzy Inference System for Simulation of Pedestrians Behaviour at Unsignalized Roadway Crossings

“Gap acceptance” behaviour oversees pedestrians crossing manoeuvre at unsignalized road crossings. From a scientific point of view, the study of pedestrians behaviour has a particular interest, since the underlying factors of behavioural interaction between pedestrians and motor vehicles drivers have a strong non-deterministic component, which makes their simulation very complex. In this paper a Fuzzy logic model for representation and simulation of pedestrian behaviour in such a manoeuvre is proposed. The calibration of Fuzzy model membership functions is executed through an Adaptive Neural Network which considers a sample of “gap acceptance” decisions collected on field. The analysis method is at first theoretically defined and then applied to a real pedestrian crossing.

New Decision Support System for Optimization of Rail Track Maintenance Planning Based on Adaptive Neurofuzzy Inference System

It is well known that maintenance planning affects, in general, the life of the structures, material wear, and quality of service. In particular, the maintenance of rail tracks affects the traffic volume as well, and therefore it is an important issue for the management of a railway system. Accurate maintenance planning is necessary to optimize resources. The condition of railways is checked by special diagnostic trains. Because of the vast amount of data that these trains record, it is necessary to analyze these data through an appropriate decision support system (DSS). However, the most up-to-date DSSs, such as EcoTrack, are based on a binary logic with rigid thresholds and complicated algorithms with a large number of rules that restrict their flexibility in use. In addition, they adopt considerable simplifications in the rail track deterioration model. In this paper, a neurofuzzy inference engine has been implemented for a DSS to overcome these drawbacks. Based on fuzzy logic, it was able to handle thresholds expressed as a range, an approximate number, or even a verbal value. Moreover, through artificial neural networks, it was possible to obtain more precise rail track deterioration models. The results obtained with the proposed model have been clustered through a fuzzy procedure to optimize the maintenance schedule, thus grouping the interventions in space and in time.

User-Oriented Model to Support Funding Decisions in Pavement Management

A new choice model for corrective road maintenance work based on an economic evaluation of users’ expectations and perceptions about road quality is proposed. Because uncertainty affects human subjective perception processes, the model uses fuzzy sets to deal with this kind of uncertainty. Accordingly, the international roughness index was first fuzzified by combining users’ perceptions about pavement conditions with ranges of speeds for four different categories of rural roads. These fuzzy values were then used to calculate vehicle operating costs and freeflow speed. The latter can be considered a function of the international roughness index and the law enforcement factor. To calculate this factor, a fuzzy inference system was set up. Vehicle operating costs and free-flow speed, as well as the value of time, were then used to calculate the travel cost perceived by users. The model finds, through fuzzy maximization of the difference between perceived travel costs before and after interventions, the best allocation of the available budget for a rural road network. In other words, the results suggest the extent of interventions on specific road sections. An application to a real network shows how the proposed model may be used.

Multicriteria Fuzzy Methodology for Feasibility Study of Transport Projects: Case Study of Southeastern Trans-European Transport Axes

This paper presents a multicriteria fuzzy methodology for evaluating the transport network that could better connect the Balkans to the European Union (EU). The Balkan countries have not yet solved the problems related to poverty, unemployment, and development. To address their problems, a comparison study conducted the implementation of Egnatia Way Corridor VIII, and of the Durrës, Albania–Skopje, Macedonia–Thessaloniki, Greece–Kipi, Greece, route as an intermediate solution between the two. A functional comparison is made of the routes not only as physical transport systems, but also as instruments of economic and political development. The proposed approach is a multicriteria fuzzy analysis as developed by G. Munda, the so-called NAIADE (novel approach to imprecise assessment and decision environments) method, to study the possible integration of the Balkan countries (Albania, FYR Macedonia, and Bulgaria) into the EU. This method can be charted on an impact matrix, which contains different numerical values and therefore allows the use of information and data with various degrees of accuracy to determine the most efficient solution among the project alternatives. An equity analysis can also be performed to evaluate the alternative infrastructures the groups involved in the decision-making process will consider. The fuzzy multicriteria methodology will be used to determine whether the more efficient solution among the alternatives is also the fairest.

A Fuzzy Logic-Based Methodology for Ranking Transport Infrastructures

Transport companies in many cases have to evaluate their competitiveness, comparing it with that of their competitors. Usually this assessment is performed through one or more indices representing facility performances, derived from a set of indicators relevant to problem representation. If the aim is to estimate the user evaluation for the service offered by a facility, the development of a synthetic index can be difficult since user’s choice is often characterized by significant uncertainties and it is not always governed by certain rules and rational behaviour, so that it could not be easily and explicitly represented by traditional mathematical techniques and models. Such uncertainties in the relationship between indicator values and facility attractiveness can be properly defined by explicitly specifying them in an approximate way using fuzzy sets theory. In this paper an innovative approach for the classification of Transport Facilities is proposed. The method is based on a Fuzzy Inference System and may be employed both as a benchmarking/ranking procedure and as a decision support tool to evaluate future scenarios as a result of facilities remodelling.

Sketch Models for Air Transport Demand Estimation: Case Study for Regional Airports System in Italy

Air travel demand at regional airports must be estimated to support transportation planning. Simplified models are needed for acceptable estimates of travel demand even if a small amount of data from immediate information sources is used. To this purpose, regressive nonlinear and monomodal statistical models were considered, and different model specifications were investigated and analyzed, assuming various functional forms as well as different sets of potential explicative variables. The airport system of the Apulia region (southern Italy) and the demand flows from Apulia toward important Italian regions such as Lombardy, Latium, and Veneto were studied. Statistical analyses were carried out with cheap data, normally available on the Internet websites of the air companies or agencies. Through a trial-and-error procedure for each model, the statistical significance and the performances of considered variables and of the single model were determined. The calibration phase showed the effectiveness of some variables that were indirectly representative of the average income in the chosen area. The proposed models include variables describing both land use characteristics and transport supply systems relevant to the considered regions as well as the level of service of the air transport. Considering the small amount of data and the cheap information sources, the results obtained are interesting, and the best-performing model could be used for preliminary study as well as starting point for more detailed, accurate, and expensive estimation tools.