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

Trip Prediction by Leveraging Trip Histories from Neighboring Users

We propose a novel approach for trip prediction by analyzing user's trip histories. We augment users' (self-) trip histories by adding 'similar' trips from other users, which could be informative and useful for predicting future trips for a given user. This also helps to cope with noisy or sparse trip histories, where the self-history by itself does not provide a reliable prediction of future trips. We show empirical evidence that by enriching the users' trip histories with additional trips, one can improve the prediction error by 15%-40%, evaluated on multiple subsets of the Nancy2012 dataset. This real-world dataset is collected from public transportation ticket validations in the city of Nancy, France. Our prediction tool is a central component of a trip simulator system designed to analyze the functionality of public transportation in the city of Nancy

How does interchange affect passengers' route choices in urban rail transit? - a case study of the Shanghai Metro

Interchange provides more flexibility in route choice, a key travel behaviour in urban rail transit, but its influence is usually simplified. This paper investigates how interchange affects route choice with passenger perception considered. At single-interchange level, perceived interchange time was proposed and modelled under three resolutions to capture passenger perception and its sensitivity. At route level, the influence of interchange was modeled by first comparing eight quantifications of interchange. Mixed logit models with the best interchange proxy were further developed to address the correlation among alternative routes and reveal the potential taste variations among passengers. Results based on Shanghai Metro data showed perceived interchange time, including passenger perception and interchange environment, better represents the influence of interchange in route choice, meanwhile the weights of interchanges on one route rise sequentially and non-linearly. The results can improve route choice prediction in demand modelling and route recommendation in advanced traveller information systems

Quantitative evaluation of advanced traveler information system benefits

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1998.Includes bibliographical references (p. 181-184).by Murtaza Zoher Sitabkhan.M.S

Traveler Responses to Real-Time Transit Passenger Information Systems

In recent years, a considerable amount of money has been spent on Real-time Transit Passenger Information Systems (RTPISs), which provide timely and accurate transit information to current and potential riders to enable them to make better pre-trip and en-route decisions. Understanding traveler responses to real-time transit information is critical for designing such services and evaluating their effectiveness. To answer this question, an effort is made in this dissertation to systematically conceptualize a variety of behavioral and psychological responses travelers may undertake to real-time transit information and empirically examine the causal effects of real-time information on traveler behavior and psychology. This research takes ShuttleTrac, a newly implemented real-time bus arrival information system for UMD's Shuttle-UM service, as a case for empirical study. In Part 1 analysis, using panel datasets derived from three-waved online campus transportation surveys, fixed-effects OLS models and random-effects ordered probit models are estimated to sort out causal relations between ShuttleTrac information use and general/cumulative behavioral and psychological outcomes. In addition, a two-stage instrumental variable model was estimated to examine the potential change in habitual mode choices due to real-time transit information use. The results show that with a few months of adjustment, travelers may increase their trip-making frequency as a result of real-time transit information use, and positive psychological outcomes are more prominent in both short and longer terms. In Part 2 analyses, using the cross-sectional dataset derived from the onboard survey, OLS models and ordered logit models were estimated to examine the trip-specific psychological effects of real-time transit information. The results show that these trip-specific psychological effects of real-time transit information do exist in expected directions and they vary among user groups and in different scenarios. A finding consistent across two parts of analyses is that accuracy of information plays a greater role in determining traveler behavior and psychology than the mere presence. This research contributes to the general discussion on traveler behavior under advanced information by 1) developing an integrative conceptual framework; and 2) providing useful insights into the issue with much empirical evidences obtained with revealed-preference data and sophisticated modeling techniques

The development of a generic step-wise framework for achieving a multimodal platform in a development country environement

Paper presented at the 33rd Annual Southern African Transport Conference 7-10 July 2014 "Leading Transport into the Future", CSIR International Convention Centre, Pretoria, South Africa.With information and technology becoming such a vital commodity in everyday life, it can be argued that informed travellers are the key to successful future transport services. Fortunately, it is recognised that the development of a multimodal transport system is needed in providing integrated traveller information. The relating challenges and the applicable considerations in attaining such an integrated system were researched. Following from this, a generic sequential framework that facilitates multimodal data integration and traveller information as a precursor to a fully integrated multimodal system was developed. In this framework four focus areas, related to the implementation requirements of the application environment considered, were identified. These areas are based on the premise that current technological evolvements need to be exploited in order to breach the missing intelligent link between the various application environments. The focus areas are: 1) the multimodal transport network and the design and modelling thereof, 2) the role of Intelligent Transport Systems (ITS) in achieving a multimodal platform, 3) the need for and the design criteria of a centralised database, and 4) the need for and the travel information requirements of a multimodal Journey Planner (JP). The establishment of such a concise framework (along with its associated steps in attaining multimodal information) will go a long way towards providing the impetus, and eradicate the barriers, in achieving sustainable traveller information services. Ideally, South Africa (SA) will be able to empower a better transport service that spans across the nation’s social barriers.This paper was transferred from the original CD ROM created for this conference. The material was published using Adobe Acrobat 10.1.0 Technology. The original CD ROM was produced by CE Projects cc. Postal Address: PO Box 560 Irene 0062 South Africa. Tel.: +27 12 667 2074 Fax: +27 12 667 2766 E-mail: [email protected]

The intention to use real-time multimodal information to change travel behaviour. The use of psychosocial variables for the market segmentation.

Advanced Traveller Information Systems (ATIS) have been developed to encourage citizens to make better choices by making their travel more efficient and reliable. Another goal is to make mobility more sustainable. More precisely, the deployment of ATIS, especially multimodal real-time information systems, aims to induce a modal shift from the car to public transports (PT) or soft modes. This Ph.D. thesis assesses the impact on travel behaviour of an ATIS, TUeTO, developed for the city of Torino within the European project Opticities. To reach this objective, a mixed method analysis has been adopted, allowing the use of both quantitative and qualitative data gathered before and after the test of TUeTO. Psychosocial constructs were defined to segment the market, together with socioeconomic and travel characteristics, to understand which variables can induce a change of travel habits towards sustainable mobility. To this end, an exploratory factor analysis (EFA) was conducted on two questionnaires (one designed for the ex-ante phase of the Opticities project and the second designed ad hoc within the thesis work) to find psychosocial constructs related to the sample of 76 participants out of the 150 recruited within the project. A cluster analysis was subsequently performed to define different categories of people according to their willingness to use real-time multimodal information system to change travel behaviour. In addition, the use of qualitative data gathered through focus group discussions before and after the test of the app made possible to complete statistical analysis and investigate the cognitive mechanisms related to the use of ATIS. The textual analysis was made to verify the coherence of the clusters and gain insight regarding the issues related to the use of ATIS. The innovative methodology of this thesis using both qualitative and quantitative data had for aim to validate, determine, and characterize the clusters created thanks to the cluster analysis method. The quantitative data from the cluster analysis defined reliable categories of people willing to use ATIS to change travel behaviour after the test period, while the use of qualitative data was successful in deepening the understandings of the issue, although it did not validate all clusters created so far. Segmentation better characterized the attitudes of people towards the use of ATIS. In contrast to the literature, the statistical analysis showed that people who had the intention to use TUeTO before the test, were not willing to change their travel behaviour after. On the other hand, although it was expected that people willing to use an ATIS would be mainly car users, the analysis pointed out that public transport users were more interested in using the information. However, the shift of mode from the car to more sustainable alternatives might be limited since a small amount of people willing to change travel behaviour for the most frequent trip use a car. Content analysis opened a new perspective regarding the deployment of ATIS as a policy to change travel behaviours. While some participants pointed out the need to improve the reliability of TUeTO, others would have preferred an improvement of the public transport infrastructure either along with or instead of the deployment of the ATIS

On Transport Monitoring and Forecasting during COVID-19 Pandemic in Rome

This paper presents the results of a study on the Rome mobility system aiming at estimating the impacts of the progressive lockdown, imposed by the government, due to the Covid-19 pandemic as well as to support decision makers in planning the transport system for the restart towards a post-Covid "new normal". The analysis of data obtained by the transport monitoring system has been fundamental for both investigating effects of the lockdown and feeding transport models to predict the impacts on future actions. At first, the paper focuses on the so-called transport analytics, by describing mobility trends for the multimodal transportation system of Rome. Then, the results of the simulated scenarios to design public transport services, able to ensure passengers social distancing required in the first post-Covid months, are presented and discussed

Providing Customized Real Time Traffic Information Through the Internet: Implementation Using GIS

For my Masters thesis I implement a web enabled GIS application for presenting personalized real-time traffic condition information. Due to the dynamic nature of traffic condition reporting, often large amounts of data have to be reported. The process of introducing personalization to traffic condition reporting hopes to reduce the amount of such data transmitted to users. The personalization of the presented traffic condition information is achieved by storing geographic definitions of routes and travel zones frequently traveled by the client. Since traffic update areas frequently requested for daily travel routes are often geographically identical, stored routes or zones can be used within a Geographic Information System (GIS) environment to retrieve traffic volume information and visualize the intended route before the start of the client\u27s routine daily trip. This saves both browsing time and data uploading for the client. The research implements such tools in a server-side (most of the processing done on the server) environment. The research concludes that existing GIS tools can be enhanced to implement the concept of using customized traffic profiles to transmit user specific traffic data