Maria Nadia Postorino

A neuro-fuzzy approach to simulate the user mode choice behaviour in a travel decision framework

Abstract Increasing congestion on the main roads in urban areas pushes analysts to improve simulation of modal choice to obtain good estimation of the demand shares for different travel modes. In the literature, several kinds of behavioural models have been proposed in order both to evaluate the modal choice percentage in urban areas and to capture the travel behaviour by means of the estimation of some suitable parameters. However, behaviour is complex in itself and often standard models, even if sophisticated, cannot capture all the complex mechanisms underlying the user behaviour. In this paper, a neuro-fuzzy approach is proposed to extract the mode choice decision rules of travel users, by evaluating different sets of rules and different membership functions. Particularly, to determine which inputs are the most relevant in such decision process, fuzzy curves and surfaces have been taken into account. In this way, nonlinear effects can be considered.

Cluster Analysis For Road Accidents Investigation

The paper describes a procedure for the evaluation of road accidents in order to obtain a clustering of similar accidents on the basis of their characteristics. The procedure is formed by different interrelated steps that allow identifying homogeneous groups of accidents. The proposed clustering allows separating the elements that are not similar, but, at the same time, the individual characteristics of each event is maintained in the group. Discussed also is further development that can be expected by defining a different score function and by constructing an analytical function that links the main identified factors for each representative event to the generic accident, in order to verify the impact produced by modifying one ormore of the values of the factors.

UPGRADING URBAN TRAFFIC FLOW BY A DEMAND-RESPONSIVE FUZZY-BASED TRAFFIC LIGHTS MODEL

Abstract A fuzzy model is proposed here to speed up traffic flows and reduce delay at road-signalized intersections by using real-time measures and traffic lights adaptation. The proposed demand-responsive control approach combines fixed-duration cycle and fuzzy calculus to reduce the overall delay without giving too much penalty to traffic streams. The traffic lights signal modification is based on the concept of “intersection saturation degree” that identifies the intersection performances. The numerical results of the proposed fuzzy approach confirm its better performances with respect to the other traditional control approaches.

Modelling User Mode Choices by an Ellipsoidal Fuzzy Approach

Abstract Among transport-related choices one of the most important and studied is the travel mode choice. In fact, the estimation of the travel mode percentages is crucial to forecast possible configurations of the transportation system according to suitable policies. In this work, the travel mode choice is studied by using a fuzzy approach. The underlying idea is that user travel behaviour is intrinsically uncertain and ambiguous and the user choices can be predicted only to some extent. The fuzzy approach aims at identifying the most important variables affecting user’s mode choices and predicts mode choices on the basis of fuzzy similarities approach based on ellipsoidal rules and neuro-fuzzy tuning procedure. The procedure has been tested by using a large database concerning actual mode choices outperforming other conventional fuzzy systems exploiting generalized network-based fuzzy inference.

An Integrated GPS-GIS Surface Movement Ground Control System

The high congestion at the airports, due both to the increase of air traffic demand and the lack of significant intervention on the infrastructures, and the need to guarantee a high safety level in the airport areas ask for more intensive studies concerning the problem of the ground circulation in an airport area. Particularly, high traffic airport areas must be continuously monitored in orderto know at each time the position of all the moving objects, land vehicles and aircrafts, on the ground (aprons, taxiways, runways). Recent solutions used to resolve the problem of object positioning move towards the use of GPS apparatus. In this work, a system that integrates GPS and Geographical Information System is proposed for monitoring the airport areas, following the new requirements of Intelligent Transport System (ITS) applications and with an innovative communication network among GISs.

An Acoustic Passive Detector for Traffic Counts with Neural Networks

Traffic count is fundamental for monitoring traffic flows on a transportation network and for the solution of different problems (as, for example, the estimation of the Origin/Destination demand matrix). Different techniques and instruments, each one with specific advantages and disadvantages, can be used for counting the vehicles on a transportation network. In this paper the use of a passive acoustic detector together with two neural networks working sequentially is proposed for counting the cars on the urban roads. The results obtained with this system are satisfactory and comparable with the existing ones. The proposed system also shows a large improvement of the cost/benefit ratio.

A Neural Network to Identify Driving Habits and Compute Car-Sharing Users’ Reputation

A main question in urban environments is the continuous growth of private mobility with its negative effects such as traffic congestion and pollution. To mitigate them, it is important to promote different forms of mobility among the citizens. Car-sharing systems give users the same flexibility and comfort of private cars but at smaller costs. For this reason, car-sharing has continuously increased its market share although rather slowly. To boost such growth, car-sharing systems needs to increase vehicle fleet, improve company profits and, at the same time, make it more affordable for consumers. In this paper the promotion of car-sharing by reputation is proposed. Neural networks have been used to identify drivers’ habits in using car-sharing vehicles. To verify the effectiveness of the proposed approach, some experiments based on real and simulated data were carried out with promising results.

A reputation framework to share resources into IoT-based environments

Internet of Things (IoT) is an emerging paradigm for cooperation among physical objects provided with computational and communication capabilities. The great amount of interactions occurring within the IoT environments expose IoT users and interconnected objects to security and privacy risks. Consequently, adding security and privacy into the IoT domain is fundamental for a wider IoT diffusion. In open and dynamic IoT environments such problems are more relevant for the greater possibility of anomalous behaviors and the adoption of approaches uniquely based on authentication methods could result infeasible and/or inadequate for creating an effective trustworthiness atmosphere. A possible solution, widely applied and investigated in different fields, is represented by trust- and reputation-based systems. To tackle the issues above we conceived a distributed reputation model along with a framework to manage information about IoT devices reputation. In particular, the model provides some countermeasures to detect malicious or cheating devices. To test the performance of our proposal, a set of experiments simulating the vehicular mobility on a basic urban network has been executed by providing promising results.

Integration Between Aircraft And Handling Vehicles During Taxiing Procedures To Improve Airport Sustainability

Integration of procedures is an important aspect of the air transport system, which focuses mainly on interoperability, safety and security. While both transport analysts and air operators have studied the integration aspects concerning these main items, less attention has been devoted to some other integration features that could reduce airport environmental impacts. In this article, the integration between handling vehicles and aircraft during taxi-out procedures has been analysed by referring to the prototypal, semi-robotic vehicle called Taxibot. aircraft emissions due to taxiing have been modelled for before and after scenarios, this latter referring to the use of the Taxibot. a simulation of the benefits – both environmental and monetary effects – obtained by using the Taxibot system has been made on a medium-size airport in Northern Italy.

Gender-Based Analysis of Zones of Tolerance for Transit Service Quality Considering Intelligent Transportation Systems

Quality service in public transport is important in transit systems. User needs and expectations have great relevance to transit modeling and implementation. This explanatory study examined young men’s and young women’s expectations and perceptions of transit quality of service with the use of the zone of tolerance (ZOT) and adequate and desired levels of service. Given the critical role of intelligent transportation systems (ITS) in public transport service enhancement, a specific dimension that reflected user perceptions and expectations of ITS also was considered. ZOTs for men and women were obtained separately, and the results showed differences. In particular, women’s minimum acceptable and desired levels of service were higher than they were for men, and women had relatively bigger ZOTs.