Structure Indicators for Transportation Graph Analysis I: Planar Connected Simple Graphs

The paper deals with the representation of a transportation infrastructure by a planar connected simple graph and aims at studying its features through the analysis of graph properties. All planar and connected graphs with 4 up to 7 edges are analysed and compared to extract the most suitable parameters to investigate some network features. Then, a set of 41 graphs representing some actual underground networks are also analysed. Besides, as a third scenario, the underground network of Milan, along its development in years, is proposed in order to apply the proposed methodology. Many parameters are taken into consideration. Some of them are already discussed in literature, such as the eigenvalues and gaps of adjacency matrix or such as the Bclassical^ parameters α, β, γ. Others, such as the first two Betti numbers, are new for these applications.In order to overcome the problem of comparing features of graphs with different size, the normalisation of these parameters is considered. Some relationships between Betti numbers, eigenvalues, and classical parameters are also investigated. Results show that the eigenvalues and gaps of the adjacency matrix well represent some features of the graphs while combining them with the Betti numbers, a more significant interpretation can be achieved. Particularly, their normalised values are able to describe the increasing complexity of a graph

Back-propagation neural networks and generalized linear mixed models to investigate vehicular flow and weather data relationships with crash severity in urban road segments

The paper deals with the identification of variables and models that can explain why a certain Severity Level (SL) may be expected in the event of a certain type of crash at a specific point of an urban road network. Two official crash records, a weather database, a traffic data source, and information on the characteristics of the investigated urban road segments of Turin (Italy) for the seven years from 2006 to 2012 were used. Examination of the full database of 47,592 crash events, including property damage only crashes, reveals 9,785 injury crashes occurring along road segments only. Of these, 1,621 were found to be associated with a dataset of traffic flows aggregated in 5 minutes for the 35 minutes across each crash event, and to weather data recorded by the official weather station of Turin. Two different approaches, a back-propagation neural network model and a generalized linear mixed model were used. Results show the impact of flow and other variables on the SL that may characterize a crash; differences in the significant variables and performance of the two modelling approaches are also commented on in the manuscript

IL CONTROLLO DI UN DEFLUSSO AUTOSTRADALE CON RETI NEURALI

1993/1994VI Ciclo1957Versione digitalizzata della tesi di dottorato cartacea

Empirical investigation of a tradable credits scheme on travel demand: a household utility based approach incorporating travel money and travel time budgets

We investigate the influence of a new mobility management measure, the tradable credits scheme (TCS), on the daily travel mode choices of individuals. Generally, we assume the individuals’ travel consists of different modes, e.g. private car mode and mass transit mode. In order to control the rapid increase in use of the private car mode in an area, policy makers may wish to implement a TCS basing on the VKT (vehicle kilometre travelled). The effects of the TCS are investigated in this paper based on a utility-theory travel demand model proposed by Golob et al. (1981), a household utility based model incorporating proposed travel money and travel time budgets. The empirical investigation is based on comparison studies of the short-term response and long-term effects with and without TCS. It finds that the implementation of TCS has not a clear impact to the value of time of household in the short-term, and the presence of TCS will not affect the linear relationship between travel time budget and travel money budget over long term. Numerical results demonstrate that the TCS will affect the travel distance of the available transport modes differentially, according to different levels of annual household income

Vehicle movements in roundabouts

This article describes a research study that used a new method based on image processing for analyzing vehicle movements in traffic circles (roundabouts). The study had three stages: a field survey to collect vehicular flow images captured by video cameras; the processing of these images using a proprietary software (VeTRA—Vehicle Tracking for Roundabout Analysis); and the analysis of the collected data. The authors note that the software allows the automatic computation of the main variables necessary to rank and evaluate a generic roundabout: the entry/exit (E/E) matrix with classification of vehicles (e.g., heavy, light, and motorcycles), vehicle trajectories, and vehicular speed diagrams along the paths of the traffic circle. The processing system can overcome classic problems affecting image processing such as variable wind conditions, cloud cover, shadows, and obstructions. The authors manually compared data on entry and exits generated by VeTRA to those manually counted on the corresponding video images. They also present a case study of an existing roundabout in an urban Italian environment. The authors conclude that the software has a high capability of generating the E/E matrix and that the analysis of vehicular trajectories enable the accurate evaluation of driver behavior in the roundabout

A comparison of two Monte Carlo algorithms for 3D vehicle trajectory reconstruction in roundabouts

Visual vehicular trajectory analysis and reconstruction represent two relevant tasks both for safety and capacity concerns in road transportation. Especially in the presence of roundabouts, the perspective effects on vehicles projection on the image plane can be overcome by reconstructing their 3D positions with a 3D tracking algorithm. In this paper we compare two different Monte Carlo approaches to 3D model-based tracking: the Viterbi algorithm and the Particle Smoother. We tested the algorithms on a simulated dataset and on real data collected in one working roundabout with two different setups (single and multiple cameras). The Viterbi algorithm estimates the Maximum A-Posteriori solution from a sample-based state discretization, but, thanks to its continuous state representation, the Particle Smoother overcomes the Viterbi algorithm showing better performance and accuracy

RETI NEURALI PER LA VALUTAZIONE DELLE CURVE DI DEFLUSSO AUTOSTRADALI IN DIFFERENTI CONDIZIONI METEOROLOGICHE E DI FLUSSO

none1L. MUSSONEMussone, Lorenz