Personalized fully multimodal journey planner

We present an advanced journey planner designed to help travellers to take full advantage of the increasingly rich, and consequently more complex offering of mobility services available in modern cities. In contrast to existing systems, our journey planner is capable of planning with the full spectrum of mobility services; combining individual and collective, fixed-schedule as well as on-demand modes of transport, while taking into account individual user preferences and the availability of transport services. Furthermore, the planner is able to personalize journey planning for each individual user by employing a recommendation engine that builds a contextual model of the user from the observation of user’s past travel choices. The planner has been deployed in four large European cities and positively evaluated by hundreds of users in field trialsPeer ReviewedPostprint (published version

MOVEUS Project: "ICT Cloud-based platform and mobility services available, universal and safe for all users"

The main goal of MoveUs project http://www.moveus-project.eu is to design, implement, pilot, evaluate, disseminate and exploit a number of ICT tools for smart mobility in smart cities, with the aim of radically change the European users’ mobility habits by offering intelligent and personalized travel information services, helping people to decide the best transport choice and providing meaningful feedback on the energy efficiency savings obtained as a result. The paper focus on the services developed for the Madrid use case: • A smart crossing service: allows pedestrians to activate a request for green light, but also takes into account the real-time status of the intersection, and directly interacts with the traffic control center and extends the green phase if needed. • A bus priority service: prioritizes delayed buses at selected crossings in order to optimize travel time and frequency, improving also the efficiency of this mode of transport. • A trip planning and info mobility service: calculates the best multimodal journey option, taking into account different parameters such as time, energy efficiency, incentives, etc

A Critical Review of New Mobility Services for Urban Transport

The growing pressure on urban passenger transport systems has increased the demand for new and innovative solutions to increase its efficiency. One approach to tackle this challenge has been the slow but steady shift towards shared mobility services (car-, bike-sharing etc.). Building on these new modes and the developments in information and communication technologies, the concept of “Mobility as a Service” (MaaS) has recently come to light and offers convenient door-to-door transport without the need to own a private vehicle. The term Mobility as a Service (MaaS) stands for buying mobility services based on consumer needs instead of buying the means of mobility. In recent years, various MaaS schemes have been arisen around the world. The objective of this paper is to review these newly existing mobility services and develop an index to evaluate the level of mobility integration for each based on the assumption that higher level of integration is more appealing to travellers. The review presented in this paper allows a comparison among the schemes and provides the background and the key points of MaaS systems that the research community could use for designing surveys. It also provides significant insights to transport operators and authorities on the elements they should take into account to apply an attractive MaaS scheme that could effectively shift demand away from private vehicles

MultiModal route planning in mobility as a service

This is an accepted manuscript of an article published by ACM in Proceedings 2019 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WI 2019 Companion) in October 2019, available online: https://doi.org/10.1145/3358695.3361843 The accepted version of the publication may differ from the final published version.Mobility as a Service (MaaS) is a new approach for multimodal transportation in smart cities which refers to the seamless integration of various forms of transport services accessible through one single digital platform. In a MaaS environment there can be a multitude of multi modal options to reach a destination which are derived from combinations of available transport services. Terefore, route planning functionalities in the MaaS era need to be able to generate multi-modal routes using constraints related to a user's modal allowances, service provision and limited user preferences (e.g. mode exclusions) and suggest to the traveller the routes that are relevant for specific trips as well as aligned to her/his preferences. In this paper, we describe an architecture for a MaaS multi-modal route planner which integrates i) a dynamic journey planner that aggregates unimodal routes from existing route planners (e.g. Google directions or Here routing), enriches them with innovative mobility services typically found in MaaS schemes, and converts them to multimodal options, while considering aspects of transport network supply and ii) a route recommender that filters and ranks the available routes in an optimal manner, while trying to satisfy travellers' preferences as well as requirements set by the MaaS operator (e.g. environmental friendliness of the routes or promotion of specific modes of transport).Published versio

T2* - personalized trip planner

In this research work we describe the framework for a Personalized Trip Planner identify as T2* (Travel to Anywhere) towards a digital concierge in everyone`s pocket by a tailor made aggregation of features and services. The mission is to empower key stakeholders in the travel and tourism industry (Travelers, Concierges, and Service Providers) to develop seamless travel experiences. This work is integrated with the MASAI H2020 project with the goal to serve in all stages of the travel process, including changes in travel-ers’ mobility patterns, associated local travel, long-distance travel, and busi-ness as well as to be used for leisure purposes. A personalized travel advice is produced based on the user’ profile created based on the user’ Facebook information extracted through a semantic approach. The quality of the ser-vice provided is measured and high-level service is promoted by an imple-mented reputation service.info:eu-repo/semantics/acceptedVersio

Context-aware user modeling strategies for journey plan recommendation

Popular journey planning systems, like Google Maps or Yahoo! Maps, usually ignore user’s preferences and context. This paper shows how we applied context-aware recommendation technologies in an existing journey planning mobile application to provide personalized and context-dependent recommendations to users. We describe two different strategies for context-aware user modeling in the journey planning domain. We present an extensive performance comparison of the proposed strategies by conducting a user-centric study in addition to a traditional offline evaluation methodPeer ReviewedPostprint (published version

Artificial Intelligence based multi-agent control system

Le metodologie di Intelligenza Artificiale (AI) si occupano della possibilità di rendere le macchine in grado di compiere azioni intelligenti con lo scopo di aiutare l’essere umano; quindi è possibile affermare che l’Intelligenza Artificiale consente di portare all’interno delle macchine, caratteristiche tipiche considerate come caratteristiche umane. Nello spazio dell’Intelligenza Artificiale ci sono molti compiti che potrebbero essere richiesti alla macchina come la percezione dell’ambiente, la percezione visiva, decisioni complesse. La recente evoluzione in questo campo ha prodotto notevoli scoperte, princi- palmente in sistemi ingegneristici come sistemi multi-agente, sistemi in rete, impianti, sistemi veicolari, sistemi sanitari; infatti una parte dei suddetti sistemi di ingegneria è presente in questa tesi di dottorato. Lo scopo principale di questo lavoro è presentare le mie recenti attività di ricerca nel campo di sistemi complessi che portano le metodologie di intelligenza artifi- ciale ad essere applicati in diversi ambienti, come nelle reti di telecomunicazione, nei sistemi di trasporto e nei sistemi sanitari per la Medicina Personalizzata. Gli approcci progettati e sviluppati nel campo delle reti di telecomunicazione sono presentati nel Capitolo 2, dove un algoritmo di Multi Agent Reinforcement Learning è stato progettato per implementare un approccio model-free al fine di controllare e aumentare il livello di soddisfazione degli utenti; le attività di ricerca nel campo dei sistemi di trasporto sono presentate alla fine del capitolo 2 e nel capitolo 3, in cui i due approcci riguardanti un algoritmo di Reinforcement Learning e un algoritmo di Deep Learning sono stati progettati e sviluppati per far fronte a soluzioni di viaggio personalizzate e all’identificazione automatica dei mezzi trasporto; le ricerche svolte nel campo della Medicina Personalizzata sono state presentate nel Capitolo 4 dove è stato presentato un approccio basato sul controllo Deep Learning e Model Predictive Control per affrontare il problema del controllo dei fattori biologici nei pazienti diabetici.Artificial Intelligence (AI) is a science that deals with the problem of having machines perform intelligent, complex, actions with the aim of helping the human being. It is then possible to assert that Artificial Intelligence permits to bring into machines, typical characteristics and abilities that were once limited to human intervention. In the field of AI there are several tasks that ideally could be delegated to machines, such as environment aware perception, visual perception and complex decisions in the various field. The recent research trends in this field have produced remarkable upgrades mainly on complex engineering systems such as multi-agent systems, networked systems, manufacturing, vehicular and transportation systems, health care; in fact, a portion of the mentioned engineering system is discussed in this PhD thesis, as most of them are typical field of application for traditional control systems. The main purpose if this work is to present my recent research activities in the field of complex systems, bringing artificial intelligent methodologies in different environments such as in telecommunication networks, transportation systems and health care for Personalized Medicine. The designed and developed approaches in the field of telecommunication net- works is presented in Chapter 2, where a multi-agent reinforcement learning algorithm was designed to implement a model-free control approach in order to regulate and improve the level of satisfaction of the users, while the research activities in the field of transportation systems are presented at the end of Chapter 2 and in Chapter 3, where two approaches regarding a Reinforcement Learning algorithm and a Deep Learning algorithm were designed and developed to cope with tailored travels and automatic identification of transportation moralities. Finally, the research activities performed in the field of Personalized Medicine have been presented in Chapter 4 where a Deep Learning and Model Predictive control based approach are presented to address the problem of controlling biological factors in diabetic patients

Data enabling digital ecosystem for sustainable shared electric mobility-as-a-service in smart cities-an innovative business model perspective

Increase in urbanization drives the need for municipalities to make mobility more efficient, both to address climate goals as well as creating a smart living environment for citizens, with less noise congestion, and pollution. As vehicles are being electrified, further advances will be needed to meet social, environmental, and economic sustainability targets, and a more efficient use of vehicles and public transport is central in this endeavor. Accordingly, Electric Mobility as a Service (eMaaS) has developed as a concept with the potential to increase sustainability mobility in cities and been designated as a phenomenon with potential to radically change how people move in the future. But presently there is the lack of a common business model that supports complex integration of all actors, digital technologies, and infrastructures involved in the eMaaS business ecosystem. This study aims to support the further development of eMaaS by providing a state of the art of eMaaS and further proposes a digital ecosystem as a business model for eMaaS sharing in smart cities. Accordingly, a systematic literature review was adopted grounded on secondary data from the literature to offers a new approach to urban mobility and demonstrate the suitability of the eMaaS concept in smart communities. The digital ecosystem is designed based on system design approach. Findings from this study provides a sustainable policy perspective, discusses the challenges and opportunities towards the development of eMaaS and its impact on electrification of vehicles. Overall, findings from this study considers the role of electric vehicles as part of the mobility sharing economy. Recommendations from this study provides designs and strategies for eMaaS, the interrelations between eMobility and other everyday practices, strategically highlighting the positive benefits of eMaaS and broader policies to limit private car usage in cities.publishedVersio

Door-to-Door Mobility Integrators as Keystone Organizations of Smart Ecosystems: Resources and Value Co-Creation – A Literature Review

Cities around the world face major mobility-related challenges, such as traffic congestion and air pollution. One primary cause of these challenges is the decision of citizens to use their private car instead of alternative mobility services such as public transport, car-sharing and bike-sharing. Technological progress offers new possibilities to address these challenges by making alternative mobility services easier and more convenient to use. This paper focuses on door-to-door (D2D) mobility integrators, which aim to offer citizens seamless D2D transport by packaging alternative mobility services. To better understand the practical barriers D2D mobility integrators face, this interdisciplinary literature review provides a holistic picture of their operand and operant resources, revealing significant gaps in our understanding of their capability to attract actors to their ecosystem and to manage value co-creation. Based on these gaps, we identify a potential avenue of future research

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications