Traffic management system for smart road networks reserved for self-driving cars

A model of a smart road network consisting of unsignalised intersections and smart roads connecting them is considered in this work with the aim of presenting a traffic management system for self-driving cars (or, more generally, autonomous vehicles) which travel the network. The proposed system repeatedly solves a set of mathematical programming problems (each of them relative to a single intersection or to a single road stretch of the network) within a decentralised control scheme in which each local intersection controller and each local road controller communicates with the fully autonomous vehicles in order to receive travel data from vehicles and to provide speed profiles to them once determined the optimal solution of the problem. In order to reduce the computational effort required to provide the optimal solution, a discrete-time approach is adopted so that, in each time interval, a limited number of vehicles are taken into consideration; in this way, solutions can be determined in a very short time thus making the proposed model compatible with a practical application to real traffic systems. The proposed model is general enough, and can be adapted to different scenarios of smart road networks reserved for self-driving cars

solving stochastic assignment to transportation networks with tvs and avs

Abstract This paper focuses on solving stochastic assignment with several types of vehicles, for instance advanced and traditional vehicles, competing for the same arcs and jointly participating to congestion. In urban transportation networks paths likely overlap, thus two path choice models, derived from Random Utility Theory, are analyzed: Probit and Gammit, properly modeling path overlap through covariance between path perceived utilities. Since for these two models no closed form is available for choice probabilities, two specifications of Montecarlo algorithms for assignment to uncongested networks are presented: the efficiency of the commonly used Mersenne Twister Pseudo-Random Number Generator is compared with a PRNG based on Sobol (quasi-random) numbers. Then, several MSA-based algorithms for equilibrium assignment ot congested networks are analyzed: some step size strategies are proposed and compared with existing ones aiming at improving practical rate of convergence. Sufficient convergence conditions are presented for equilibrium assignment with arc cost flow functions with symmetric or asymmetric Jacobian matrix. Results of applications are also discussed to support theoretical results

On analyzing the vulnerabilities of a railway network with Petri nets

Petri nets are used in this paper to estimate the indirect consequences of accidents in a railway network, which belongs to the class of the so-called transportation Critical Infrastructures (CIs), that is, those assets consisting of systems, resources and/or processes whose total or partial destruction, or even temporarily unavailability, has the effect of significantly weakening the functioning of the system. In the proposed methodology, a timed Petri ne<t represents the railway network and the trains travelling over the rail lines; such a net also includes some places and some stochastically-timed transitions that are used to model the occurrence of unexpected events (accidents, disruptions, and so on) that make some resources of the network (tracks, blocks, crossovers, overhead line, electric power supply, etc.) temporarily unavailable. The overall Petri net is a live and bounded Generalized Stochastic Petri Net (GSPN) that can be analyzed by exploiting the steady-state probabilities of a continuous-time Markov chain (CTMC) that can be derived from the reachability graph of the GSPN. The final target of such an analysis is to determine and rank the levels of criticality of transportation facilities and assess the vulnerability of the whole railway network

Testing a global standard for quantifying species recovery and assessing conservation impact.

Recognizing the imperative to evaluate species recovery and conservation impact, in 2012 the International Union for Conservation of Nature (IUCN) called for development of a "Green List of Species" (now the IUCN Green Status of Species). A draft Green Status framework for assessing species' progress toward recovery, published in 2018, proposed 2 separate but interlinked components: a standardized method (i.e., measurement against benchmarks of species' viability, functionality, and preimpact distribution) to determine current species recovery status (herein species recovery score) and application of that method to estimate past and potential future impacts of conservation based on 4 metrics (conservation legacy, conservation dependence, conservation gain, and recovery potential). We tested the framework with 181 species representing diverse taxa, life histories, biomes, and IUCN Red List categories (extinction risk). Based on the observed distribution of species' recovery scores, we propose the following species recovery categories: fully recovered, slightly depleted, moderately depleted, largely depleted, critically depleted, extinct in the wild, and indeterminate. Fifty-nine percent of tested species were considered largely or critically depleted. Although there was a negative relationship between extinction risk and species recovery score, variation was considerable. Some species in lower risk categories were assessed as farther from recovery than those at higher risk. This emphasizes that species recovery is conceptually different from extinction risk and reinforces the utility of the IUCN Green Status of Species to more fully understand species conservation status. Although extinction risk did not predict conservation legacy, conservation dependence, or conservation gain, it was positively correlated with recovery potential. Only 1.7% of tested species were categorized as zero across all 4 of these conservation impact metrics, indicating that conservation has, or will, play a role in improving or maintaining species status for the vast majority of these species. Based on our results, we devised an updated assessment framework that introduces the option of using a dynamic baseline to assess future impacts of conservation over the short term to avoid misleading results which were generated in a small number of cases, and redefines short term as 10 years to better align with conservation planning. These changes are reflected in the IUCN Green Status of Species Standard

Sensitivity analysis of the performances of seaport container terminals

In the last decades, the continuous growth of the containerized freight transportation has led to a fast development of container terminals, that, as a consequence, need keeping the handling equipment efficient and tech-nologically advanced. In this framework, the investments for enhancing the handling throughput, and for reducing the management costs, have to be carefully planned. The present paper provides a model for decision support in finding the best kinds, and numbers, of resources via of a global sensitivity analysis approach. In particular, the proposed methodology points out those elements of the handling system whose performance variabilities influence the global performance of the seaport terminal the most

Open Problems in Transportation Engineering with Connected and Autonomous Vehicles

In recent decades, technologies that can lead to fully automated driving have had a rapid development. In this framework, 'road transport automation' can potentially result in significant changes to the operation of road systems throughout the world. It is impossible to foresee how long it will take to realize such potential changes, because there are many uncertainties about both the technologies to deploy, and the policy environment where they should be deployed. 'Full automation' is the future of road transport, but the transition from manual to fully autonomous vehicles is especially dependent on the interactions between humans and automation, but also between automated vehicles and manual vehicles, and between automated vehicles and infrastructure. In the above context, this paper, after introducing some open problems related to automated vehicles, focuses on a particular one, consisting of the simplified evaluation of the equilibrium points achievable by a mixed flow with different percentages of automated vehicles. The aim of the considered problem is to provide a first general estimation of the performance of an existing network in various scenarios, characterized by different percentages of autonomous vehicles and mobility demand. More in detail, a simplified kinematic supply model is introduced to assess the link flow/cost performances, aiming at estimating the potential congestion reduction. An application to a real word network is described, and the relevant results are reported and discussed

On Evaluating Traffic Lights Performance Sensitivity via Hybrid Systems Models

AbstractThe problem of optimizing traffic light phases dates back to the fifties. Since there, many solutions for different network configurations (isolated intersections, coordinated intersections, and so on) and different modeling and solution approaches (empirical models, queue theory approaches, mathematical programming models, etc.) have been proposed.In parallel, it has been developed the general theory of hybrid systems, i.e., of those systems characterized by two kinds of states: normal states whose variation is governed by a fixed set of equations, and macro states whose change is governed by the occurrence of particular conditions or external events.In this paper, a hybrid model of traffic light dynamics is introduced aiming at providing a modelling framework for evaluating the sensitivity of the performances of different approaches for signal setting optimal design