AGW for efficient freight transport in container yard: models and costs

Abstract Different modes of transport are frequently used to transfer goods from origin to destination, especially on medium-long distances, in relation to the network supply, the available services, the costs. The transfer from one carrier to another, in an interchange node such as a port, a rail station, a logistics terminal, often implicates an increase of monetary and temporal costs, connected to material and immaterial operations. The principal aim is to minimize the overall cost of transport, but the freight interchange node can represent critical steps in logistics chain and for this reason much attention is now committed to actions to make efficient the functional organization of the terminal. In the last years an increasing interest is directed to the use of vehicles technologically advanced with automation of functions. The paper focuses on a particular technology, conceived recently, otherwise an intelligent rail wagon called AGW (Automated Guided Wagon) for handling of containers in a port. The use of intelligent system AGW as handling unit of containers in the yard, would allow the overcoming of diseconomies of scale and the reduction of the handling times and costs through a flexible management in relation to the characteristics of the transport supply and demand, the latter subject to a high variability. In the paper, after a brief description of the AGW technology and the advantages connected to the use of this handling system in a freight interchange node, the attention is focused on a comparative analysis between the handling system now operating in the container port (RTG, Straddle Carrier, AVG, etc.) and the system that involves the use of AGW. This analysis is made on the operational characteristics of the different handling systems, through the use of: functional schemes, with the aim to carry out evaluations related to the spatial, organizational and relational structure of container yard equipped with different handling unit; network models (graphical representation of links and paths; basic cost parameters) for the schematization and simulation of container handling in the yard; cost models for quantitative evaluation of monetary and temporal impacts, that derive from the use of different handling unit in the yard

Smart Materials and Technologies for Early Warning, Monitoring, and Increased Expected Life of Transportation Infrastructure

Different approaches can be used to make cities and transportation infrastructures smarter, more sustainable, and durable. These changes will positively affect the work of many stakeholders, such as authorities, road agencies, citizens, users, and driverless vehicles. Unfortunately, despite the fact that smart materials are becoming more and more common, the integration level between smart materials and early warning technologies is still in need of a holistic approach.In light of this, the main objective of the work presented in this paper is to provide an overview of the materials and technological solutions that can be used in the field of transportation infrastructures to satisfy some of the Sustainable Development Goals of the United Nation (resolution A/RES/70/1/2015).The solutions above include an innovative monitoring method, set up by the authors of this paper, which is based on the concept of vibro-acoustic signature. The method mentioned above is a Non-Destructive Test and sensor-based solutions in order to detect damage to road pavements. The proposed method was validated using Finite Element Modelling simulations, and experimental investigations followed by data analysis carried out using Machine Learning- and Wavelet-based algorithms.Results show that smart materials and technologies can be used to target A/RES/70/1/2015 goals and to improve the sustainability of the current and future transportation infrastructures. Materiali e tecnologie intelligenti per allerta, monitoraggio, e per aumentare la vita utile delle infrastrutture di trasportoDifferenti approcci possono essere utilizzati per rendere le città e le infrastrutture di trasporto più intelligenti, sostenibili e durature. Queste tendenze influenzeranno positivamente il lavoro di molti portatori di interesse come ad esempio le autorità competenti, le società che si occupano di strade, i cittadini, gli utenti, ed i veicoli senza guidatore. Sfortunatamente, malgrado il fatto che i materiali intelligenti sono sempre più utilizzati, il livello di integrazione tra materiali intelligenti e tecnologie per l’allerta precoce ha ancora bisogno di un approccio olistico.Alla luce di questo, l’obiettivo principale del lavoro presentato in questo documento è quello di fornire una panoramica su soluzioni basate su materiali e tecnologie che potrebbero essere utilizzate nel campo delle infrastrutture di trasporto per soddisfare alcuni degli obiettivi della risoluzione per lo sviluppo sostenibile (Sustainable Development Goals) delle Nazioni Unite (A/RES/70/1/2015).Le soluzioni su citate includono un metodo innovativo, messo a punto dagli autori della memoria, il quale è basato sul concetto di firma vibro-acustica. Il metodo su citato è una soluzione basata su test non distruttivi (NDT) e sensori per l’identificazione di danni nelle pavimentazioni stradali. Il metodo proposto è stato validato attraverso simulazioni fatte con un modello agli elementi finiti (FEM), e indagini sperimentali seguite da un’analisi dati svolta usando un modello basato sull’apprendimento automatico (machine learning).I risultati mostrano che materiali e tecnologie intelligenti possono essere utilizzate per raggiungere gli obiettivi della risoluzione A/RES/70/1/2015 e migliorare la sostenibilità delle attuali e future pavimentazioni stradali.Different approaches can be used to make cities and transportation infrastructures smarter, more sustainable, and durable. These changes will positively affect the work of many stakeholders, such as authorities, road agencies, citizens, users, and driverless vehicles. Unfortunately, despite the fact that smart materials are becoming more and more common, the integration level between smart materials and early warning technologies is still in need of a holistic approach.In light of this, the main objective of the work presented in this paper is to provide an overview of the materials and technological solutions that can be used in the field of transportation infrastructures to satisfy some of the Sustainable Development Goals of the United Nation (resolution A/RES/70/1/2015).The solutions above include an innovative monitoring method, set up by the authors of this paper, which is based on the concept of vibro-acoustic signature. The method mentioned above is a Non-Destructive Test and sensor-based solutions in order to detect damage to road pavements. The proposed method was validated using Finite Element Modelling simulations, and experimental investigations followed by data analysis carried out using Machine Learning- and Wavelet-based algorithms.Results show that smart materials and technologies can be used to target A/RES/70/1/2015 goals and to improve the sustainability of the current and future transportation infrastructures. Materiali e tecnologie intelligenti per allerta, monitoraggio, e per aumentare la vita utile delle infrastrutture di trasportoDifferenti approcci possono essere utilizzati per rendere le città e le infrastrutture di trasporto più intelligenti, sostenibili e durature. Queste tendenze influenzeranno positivamente il lavoro di molti portatori di interesse come ad esempio le autorità competenti, le società che si occupano di strade, i cittadini, gli utenti, ed i veicoli senza guidatore. Sfortunatamente, malgrado il fatto che i materiali intelligenti sono sempre più utilizzati, il livello di integrazione tra materiali intelligenti e tecnologie per l’allerta precoce ha ancora bisogno di un approccio olistico.Alla luce di questo, l’obiettivo principale del lavoro presentato in questo documento è quello di fornire una panoramica su soluzioni basate su materiali e tecnologie che potrebbero essere utilizzate nel campo delle infrastrutture di trasporto per soddisfare alcuni degli obiettivi della risoluzione per lo sviluppo sostenibile (Sustainable Development Goals) delle Nazioni Unite (A/RES/70/1/2015).Le soluzioni su citate includono un metodo innovativo, messo a punto dagli autori della memoria, il quale è basato sul concetto di firma vibro-acustica. Il metodo su citato è una soluzione basata su test non distruttivi (NDT) e sensori per l’identificazione di danni nelle pavimentazioni stradali. Il metodo proposto è stato validato attraverso simulazioni fatte con un modello agli elementi finiti (FEM), e indagini sperimentali seguite da un’analisi dati svolta usando un modello basato sull’apprendimento automatico (machine learning).I risultati mostrano che materiali e tecnologie intelligenti possono essere utilizzate per raggiungere gli obiettivi della risoluzione A/RES/70/1/2015 e migliorare la sostenibilità delle attuali e future pavimentazioni stradali

multimodal choice model for e mobility scenarios

Abstract The paper focuses on the definition, calibration and testing of a simulation model that is able to represent multimodal choice behaviours for electric vehicles. Taking into account the interchange between public transport and electric private mobility, the model estimates the parking demand at the Park & Ride sites equipped with charging stations. The model is based on a data-driven approach, in which mainly Floating Car Data and open data of public transport have derived the explanatory variables. Specifically, a machine learning method (Random Forest) has been used to calibrate and test the model in the real case of the metropolitan area of Rome (Italy). We first perform a stability analysis, letting the parameters of the model vary. We then carry out a sensitivity analysis on the variables that can affect the user propensity to adopt the Park & Ride. Finally, we profile and test an incentive policy to boost the choice of Park & Ride. Results suggest that the model succeeds in simulating Park & Ride by electric vehicles and, therefore, it can be extremely valuable for planning financial support to the multimodal travel choice and forecasting vehicle-to-grid scenarios

Sensor Technologies for Intelligent Transportation Systems

Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS. Finally, we discuss some of the challenges that need to be addressed to enable a fully operational and cooperative ITS environment

Modelling of interactions between rail service and travel demand: a passenger-oriented analysis

The proposed research is situated in the field of design, management and optimisation in railway network operations. Rail transport has in its favour several specific features which make it a key factor in public transport management, above all in high-density contexts. Indeed, such a system is environmentally friendly (reduced pollutant emissions), high-performing (high travel speeds and low values of headways), competitive (low unitary costs per seat-km or carried passenger-km) and presents a high degree of adaptability to intermodality. However, it manifests high vulnerability in the case of breakdowns. This occurs because a faulty convoy cannot be easily overtaken and, sometimes, cannot be easily removed from the line, especially in the case of isolated systems (i.e. systems which are not integrated into an effective network) or when a breakdown occurs on open tracks. Thus, re-establishing ordinary operational conditions may require excessive amounts of time and, as a consequence, an inevitable increase in inconvenience (user generalised cost) for passengers, who might decide to abandon the system or, if already on board, to exclude the railway system from their choice set for the future. It follows that developing appropriate techniques and decision support tools for optimising rail system management, both in ordinary and disruption conditions, would consent a clear influence of the modal split in favour of public transport and, therefore, encourage an important reduction in the externalities caused by the use of private transport, such as air and noise pollution, traffic congestion and accidents, bringing clear benefits to the quality of life for both transport users and non-users (i.e. individuals who are not system users). Managing to model such a complex context, based on numerous interactions among the various components (i.e. infrastructure, signalling system, rolling stock and timetables) is no mean feat. Moreover, in many cases, a fundamental element, which is the inclusion of the modelling of travel demand features in the simulation of railway operations, is neglected. Railway transport, just as any other transport system, is not finalised to itself, but its task is to move people or goods around, and, therefore, a realistic and accurate cost-benefit analysis cannot ignore involved flows features. In particular, considering travel demand into the analysis framework presents a two-sided effect. Primarily, it leads to introduce elements such as convoy capacity constraints and the assessment of dwell times as flow-dependent factors which make the simulation as close as possible to the reality. Specifically, the former allows to take into account the eventuality that not all passengers can board the first arriving train, but only a part of them, due to overcrowded conditions, with a consequent increase in waiting times. Due consideration of this factor is fundamental because, if it were to be repeated, it would make a further contribution to passengers’ discontent. While, as regards the estimate of dwell times on the basis of flows, it becomes fundamental in the planning phase. In fact, estimating dwell times as fixed values, ideally equal for all runs and all stations, can induce differences between actual and planned operations, with a subsequent deterioration in system performance. Thus, neglecting these aspects, above all in crowded contexts, would render the simulation distorted, both in terms of costs and benefits. The second aspect, on the other hand, concerns the correct assessment of effects of the strategies put in place, both in planning phases (strategic decisions such as the realisation of a new infrastructure, the improvement of the current signalling system or the purchasing of new rolling stock) and in operational phases (operational decisions such as the definition of intervention strategies for addressing disruption conditions). In fact, in the management of failures, to date, there are operational procedures which are based on hypothetical times for re-establishing ordinary conditions, estimated by the train driver or by the staff of the operation centre, who, generally, tend to minimise the impact exclusively from the company’s point of view (minimisation of operational costs), rather than from the standpoint of passengers. Additionally, in the definition of intervention strategies, passenger flow and its variation in time (different temporal intervals) and space (different points in the railway network) are rarely considered. It appears obvious, therefore, how the proposed re-examination of the dispatching and rescheduling tasks in a passenger-orientated perspective, should be accompanied by the development of estimation and forecasting techniques for travel demand, aimed at correctly taking into account the peculiarities of the railway system; as well as by the generation of ad-hoc tools designed to simulate the behaviour of passengers in the various phases of the trip (turnstile access, transfer from the turnstiles to the platform, waiting on platform, boarding and alighting process, etc.). The latest workstream in this present study concerns the analysis of the energy problems associated to rail transport. This is closely linked to what has so far been described. Indeed, in order to implement proper energy saving policies, it is, above all, necessary to obtain a reliable estimate of the involved operational times (recovery times, inversion times, buffer times, etc.). Moreover, as the adoption of eco-driving strategies generates an increase in passenger travel times, with everything that this involves, it is important to investigate the trade-off between energy efficiency and increase in user generalised costs. Within this framework, the present study aims at providing a DSS (Decision Support System) for all phases of planning and management of rail transport systems, from that of timetabling to dispatching and rescheduling, also considering space-time travel demand variability as well as the definition of suitable energy-saving policies, by adopting a passenger-orientated perspective

A systematic literature review

Albuquerque, V., Dias, M. S., & Bacao, F. (2021). Machine learning approaches to bike-sharing systems: A systematic literature review. ISPRS International Journal of Geo-Information, 10(2), 1-25. [62]. https://doi.org/10.3390/ijgi10020062Cities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques’ contributions applied to bike-sharing systems to improve cities’ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction.publishersversionpublishe

Survey of smart parking systems

The large number of vehicles constantly seeking access to congested areas in cities means that finding a public parking place is often difficult and causes problems for drivers and citizens alike. In this context, strategies that guide vehicles from one point to another, looking for the most optimal path, are needed. Most contributions in the literature are routing strategies that take into account different criteria to select the optimal route required to find a parking space. This paper aims to identify the types of smart parking systems (SPS) that are available today, as well as investigate the kinds of vehicle detection techniques (VDT) they have and the algorithms or other methods they employ, in order to analyze where the development of these systems is at today. To do this, a survey of 274 publications from January 2012 to December 2019 was conducted. The survey considered four principal features: SPS types reported in the literature, the kinds of VDT used in these SPS, the algorithms or methods they implement, and the stage of development at which they are. Based on a search and extraction of results methodology, this work was able to effectively obtain the current state of the research area. In addition, the exhaustive study of the studies analyzed allowed for a discussion to be established concerning the main difficulties, as well as the gaps and open problems detected for the SPS. The results shown in this study may provide a base for future research on the subject.Fil: Diaz Ogás, Mathias Gabriel. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Fabregat Gesa, Ramon. Universidad de Girona; EspañaFil: Aciar, Silvana Vanesa. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentin

A role-based software architecture to support mobile service computing in IoT scenarios

The interaction among components of an IoT-based system usually requires using low latency or real time for message delivery, depending on the application needs and the quality of the communication links among the components. Moreover, in some cases, this interaction should consider the use of communication links with poor or uncertain Quality of Service (QoS). Research efforts in communication support for IoT scenarios have overlooked the challenge of providing real-time interaction support in unstable links, making these systems use dedicated networks that are expensive and usually limited in terms of physical coverage and robustness. This paper presents an alternative to address such a communication challenge, through the use of a model that allows soft real-time interaction among components of an IoT-based system. The behavior of the proposed model was validated using state machine theory, opening an opportunity to explore a whole new branch of smart distributed solutions and to extend the state-of-the-art and the-state-of-the-practice in this particular IoT study scenario.Peer ReviewedPostprint (published version

Delivering in Urban Areas: A Probabilistic-Behavioral Approach for Forecasting the Use of Electric Micromobility

Urban delivering is facing some significant changes that are heading towards unsustainable scenarios. At the same time, local administrations as well as city planners are involved in promoting new solutions that can help to improve city sustainability and livability. In this context, electric micromobility could offer a valuable contribution. In fact, electric micromobility systems such as e-bikes and e-scooters, both at an individual level or as a shared service, could represent sustainable mobility options for city logistics, especially for specific classes of parcel delivery, users’ characteristics and travelled distances. Considering both the growth of e-commerce and the spreading of new options for delivering parcels (e.g., crowdshipping), electric micromobility (e-bikes and e-scooters) could support the penetration and acceptability of such new options, limiting the impacts of delivery operations. After analysis of the current e-commerce background and a review of the current delivery options to satisfy delivery demand, crowdshipping stands out. Thus, the potential shift from private transport to e-micromobility for crowdshipping is investigated, assuming that potential crowdshippers may, mainly, be commuters. The methodology is based on using probabilistic-behavioral models developed within random utility theory, which allow the potential shift towards e-micromobility for commuting to be forecasted. The models were calibrated in Rome, where more than 200 interviews with commuters were available

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.This work was supported in part by the Basque Government for its funding support through the EMAITEK program (3KIA, ref. KK-2020/00049). It has also received funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government