京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21356号 / 工博第4515号 / 新制||工||1703(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 藤井 聡, 准教授 QURESHI,Ali Gul, 准教授 SCHMOECKER,Jan-Dirk / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

The advent of on-demand transport modes such as ride-hailing and microtransit has challenged forecasters to develop new methods of forecasting the use and impacts of such modes. In particular, there is some professional disagreement about the relative role of activity-based transportation behavior models -- which have detailed understanding of the person making a trip and its purpose -- and multi-agent demand simulations which may have a better understanding of the availability and service characteristics of on-demand services. A particular question surrounds how the relative strengths of these two approaches might be successfully paired in practice. Using daily plans generated by the activity-based model ActivitySim as inputs to the BEAM multi-agent simulation, we construct nine different methodological combinations by allowing the choice to use a pooled ride-hail service in ActivitySim, in BEAM with different utility functions, or in both. Within each combination, we estimate ride-hailing ridership and level of service measures. The results suggest that mode choice model structure drastically affects ride-hailing ridership and level of service. In addition, we see that multi-agent simulation overstates the demand interest relative to an activity-based model, but there may be opportunities in future research to implement feedback loops to balance the ridership and level of service forecasts between the two models.

Several vector-borne and zoonotic diseases have emerged or re-emerged in Europe over these last decades. Besides climate change that influences disease risk at a regional scale, landscape changes could be responsible for local heterogeneities in disease risk. Spatial epidemiology tries to understand and predict spatial variations in disease risk by using spatial tools and spatially-explicit modelling methods. This study investigated the impact of fine-grained landscape patterns on the transmission of vector-borne and zoonotic infections in terms of habitat suitability for vectors and/or hosts and of exposure of people to infectious agents. This was studied through three human diseases emerging or at risk of re-emergence in Europe: the rodent-borne Puumala hantavirus, the tick-borne Lyme borreliosis and the mosquito-borne malaria infections. Statistical models were first used to study the relationships between environmental variables and host abundance, host prevalence, and human cases of Puumala hantavirus. Environmental factors were also combined with socio-economic factors to explain Puumala hantavirus and Lyme borreliosis incidence rates. The combination of factors explaining disease transmission and the complexity of such systems led to the development of an innovative, spatially-explicit modelling method: multi-agent simulation (MAS). The MALCAM simulation model was developed to assess the risk of malaria re-emergence in southern France and simulates spatial and temporal variations in contact rate between people and potential malaria vectors. The effect of changes in potential drivers of malaria re-emergence was also simulated. The different case studies showed that fine-grained landscape patterns influence the presence and abundance of vectors and hosts. Moreover, environmental conditions may also influence disease transmission through pathogen dispersal and the exposure of people to infectious agents. Finally, this study showed that people-vector contacts not only depend on the spatial distribution of people and potential vectors, but also on their behaviours and interactions.

The Tropical Andes continue to suffer the most radical climatic changes in South America. These changes generate alterations in its ecosystems, and therefore affect local populations, whose livelihoods are dependent on its diversity and functioning. This is particularly true for rural populations who rely on agriculture as their primary source of food and income. Although the biophysical pathways through which climate change can affect these populations have received extensive scientific attention, it is urgent to study the socioeconomic pathways, at scales that allow the development of vulnerability reduction strategies at the local level. The present study is part of the INCA project (International Network on Climate Change), which is a research network that analyses the local strategies of farmers under a changing climate in the Tropical Andes (Lindner et al. 2017). To contribute to this goal this study investigates climate-related vulnerability and climate change adaptation at local scales. First, the current vulnerability of farm household systems (FHSs) to climate-related hazards is assessed. This is done by looking at determinants that are internal (adaptive capital) and external (climate-related hazards) to the FHSs. Based on the recurrence of internal factors, FHSs are categorized into different groups. These groups are validated by observing the effects of climatic events that are specific to each group. The result of the analysis are different typologies or archetypes of climate-related vulnerability. The analysis adopts an archetype approach and develops methods based on multivariate analysis techniques. Second, the study analyzes the impacts of climate change, expressed as an increase in temperature conditions, at local levels. For this purpose, a multi-agent systems model of land-use/cover change is used, specifically the software package MPMAS. The model is the first attempt at a detailed representation of agents-environment interactions in the framework of climate change in the Tropical Andes. The simulation outcomes report on the adaptation of different farm household groups and the effects of climate change on the agricultural landscape. The research was conducted in selected communities in the Central Andes of Peru. The active integration of empirical data with secondary literature in the application of the research methods provided a suitable way to analyze the vulnerability and adaptive capacity of FHSs in the Tropical Andes in a comprehensive manner. Moreover, the use of participatory assessment techniques to obtain empirical data provided an additional perspective for the analysis and improved the understanding of the problem, contributing to deriving analytical generalizations that could hardly be obtained using only quantitative methods. The research results for the study area identify five archetypes of farm household’s vulnerability to climate-related hazards. For each archetype, distinct vulnerability-creating mechanisms are observed. For example, most vulnerable farm households have a very limited amount of adaptive capital: low levels of off-farm employment, few farm animals, small agricultural area, mostly rainfed, and low use of agro-ecological zones. In addition, they occupy predominantly the higher, and therefore less-productive, agro-ecological zones of the watershed. The analysis also makes it possible to derive spatial and thematic priorities for vulnerability reduction that are specific to each archetype. The modeling approach applied proved to be suitable for simulating the impacts of climate change at the local level. In particular, regarding the explicit simulation of FHSs, the productive landscape, and the way in which they interrelate and change in response to an increase in temperature conditions. The incorporation of heterogeneity and dynamics in the modeled population, the use of optimization techniques to simulate decision making, and the multi-periodicity of the model produce non-linearity, uncertainty and trajectory dependence. In addition, the use of vulnerability archetypes is a novel and robust way of creating a heterogeneous population for the initialization of the model. Simulation results show dynamic changes in the agricultural landscape as temperature increases. The area allocated to corn and olluco expands, while potato and oat areas diminish. Investment in tree plantations is largely unaffected. The effects of rising temperatures on farm households’ welfare show a general persistence of poverty in the study area. However, the effect on FHSs income is predominantly positive, allowing some to improve their food poverty position. The FHSs that manage to benefit from an increase in temperature have, on average, larger agricultural and forest areas, a greater amount of savings in the form of animals, hire more salaried labor and practice more mechanized agriculture than the FHSs whose situation did not improve. The results show that, in addition to the effects of climate change on crop productivity, there are other factors influencing land use decisions that deserve more attention in the analysis of vulnerability and climate change impacts. A better understanding of heterogeneity in climate vulnerability and climate impacts is an important step in meeting this demand.

The onset of Industry 4.0 has provided considerable benefits to Intelligent Cyber-Physical Systems (ICPS), with technologies such as internet of things, wireless sensing, cognitive computing and artificial intelligence to improve automation and control. However, with increasing automation, the “human” element in industrial systems is often overlooked for the sake of standardization. While automation aims to redirect the workload of human to standardized and programmable entities, humans possess qualities such as cognitive awareness, perception and intuition which cannot be automated (or programmatically replicated) but can provide automated systems with much needed robustness and sustainability, especially in unstructured and dynamic environments. Incorporating tangible human skills and knowledge within industrial environments is an essential function of “Human-in-the-loop” (HITL) Systems, a term for systems powerfully augmented by different qualities of human agents. The primary challenge, however, lies in the realistic modelling and application of these qualities; an accurate human model must be developed, integrated and tested within different cyber-physical workflows to 1) validate the assumed advantages, investments and 2) ensure optimized collaboration between entities. Agricultural Robotic Systems (ARS) are an example of such cyber-physical systems (CPS) which, in order to reduce reliance on traditional human-intensive approaches, leverage sensor networks, autonomous robotics and vision systems and for the early detection of diseases in greenhouse plants. Complete elimination of humans from such environments can prove sub-optimal given that greenhouses present a host of dynamic conditions and interactions which cannot be explicitly defined or managed automatically. Supported by efficient algorithms for sampling, routing and search, HITL augmentation into ARS can provide improved detection capabilities, system performance and stability, while also reducing the workload of humans as compared to traditional methods. This research thus studies the modelling and integration of humans into the loop of ARS, using simulation techniques and employing intelligent protocols for optimized interactions. Human qualities are modelled in human “classes” within an event-based, discrete time simulation developed in Python. A logic controller based on collaborative intelligence (HUB-CI) efficiently dictates workflow logic, owing to the multi-agent and multi-algorithm nature of the system. Two integration hierarchies are simulated to study different types of integrations of HITL: Sequential, and Shared Integration. System performance metrics such as costs, number of tasks and classification accuracy are measured and compared for different collaboration protocols within each hierarchy, to verify the impact of chosen sampling and search algorithms. The experiments performed show the statistically significant advantages of HUB-CI based protocol over traditional protocols in terms of collaborative task performance and disease detectability, thus justifying added investment due to the inclusion of HITL. The results also discuss the competitive factors between both integrations, laying out the relative advantages and disadvantages and the scope for further research. Improving human modelling and expanding the range of human activities within the loop can help to improve the practicality and accuracy of the simulation in replicating an HITL-ARS. Finally, the research also discusses the development of a user-interface software based on ARS methodologies to test the system in the real-world.<br>

No description available.

Titre: Simulation multi-agent de l’information des voyageurs dans les transports en commun.Résumé:Avec la généralisation de l'information temps-réel, le comportement des réseaux de transport modernes devient de plus en plus difficile à analyser et à prévoir. Le rôle de l'information est de plus en plus critique, particulièrement en cas de dysfonctionnement des réseaux, et l’information devient de plus en plus personnalisée et individuelle. Plusieurs phénomènes tels que la saturation, la concentration et la sur-réaction peuvent être observés après l’utilisation de systèmes d’information voyageurs. En effet, sans contrôle, la diffusion massive d'informations, à travers les panneaux à messages variables, les annonces dans les médias ainsi que les dispositifs de guidage individuel peut avoir des effets pervers et créer de nouvelles congestions. Ainsi, il est devenu important de développer des outils de simulation pour les décideurs de politiques de mobilité, prenant en compte ce nouvel environnement informationnel.Dans ce travail de thèse, nous proposons une simulation multi-agent pour mesurer l'impact de la fourniture d'informations sur la qualité des voyages en transports en commun, notamment dans des situations perturbées, en prenant en compte un environnement informationnel hétérogène. Dans un premier temps, nous concevons une simulation qui assure le déplacement de voyageurs sur un réseau de transport en commun. Ensuite, nous l’enrichissons par l’intégration de l’information des voyageurs et des taux d’équipements des voyageurs en smartphones, de telle manière qu’il puisse représenter les voyageurs connectés et être capable de distinguer l’impact des informations personnelles de celui des informations générales. Pour ce faire, nous nous fondons sur le paradigme multi-agent, qui est un modèle puissant pour la conception et l'implantation d'applications de transport. Pour répondre aux besoins de l’intégration de l’information des voyageurs, en particulier l’information individuelle, nous adoptons une approche centrée-environnement où l’environnement spatio-temporel multi-agent est l’interlocuteur des agents voyageurs et représente l’évolution dans le temps de l’état du réseau de transport en commun.Afin de tester notre simulateur dans un contexte réaliste de déplacement, nous utilisons les données réelles du réseau de Toulouse. Pour évaluer l’impact de la provision d’information voyageur sur le réseau, nous testons différents scénarios en fonction du pourcentage de voyageurs connectés représentés par des agents. Ces scénarios simulés sont analysés suivant leur impact sur les temps de parcours moyens des voyageurs, connectés et non connectés. Les résultats montrent que le nombre de voyageurs connectés a un impact positif sur les temps de parcours jusqu’à un certain seuil, au delà duquel l’impact devient relativement négatif / Title: Multiagent simulation of traveler information on transit networks.Abstract:With the generalization of real-time traveler information, the behavior of modern transport networks becomes harder to analyze and to predict. Advanced traveler Information systems play a major role in modern transportation system, mainly in case of disturbances, and the information is becoming more personalized and individual. Different phenomena such as over-saturation, concentration and over-reaction can be observed after the use of advanced traveler information systems. In fact, without control, the massive spread of information via billboards, radio announcements and individual guidance may have perverse effects and create new traffic jams. It is now critical to develop simulation tools for mobility policies makers, taking into account this new information environment to observe these effects and to consider the proper methods to deal with them.In this PHD work, we propose a multiagent simulation to measure the impact of information provision on the quality of passengers’ travels, notably in case of disturbances, taking into account a heterogeneous information environment. First, we design and implement a simulation to ensure travelers movement in a transit network. Then, we enrich our model to integrate traveler information system and to represent travelers equipped with smart phones. It allows us to evaluate separately personal and general information. To this end, we use the multi-agent paradigm, which is proven to be a powerful model to design and implement transportation applications. To deal with the integration of the traveler information system in the simulator, we adopt an environment-centered approach, where the space-time multiagent environment is the privileged interlocutor of the agents and represent the evolution of the transit network state over time.To test our simulator in a real context, we use real data on the city of Toulouse, France. To assess the impact of information provision, we simulate different scenarios in function of the percentage of connected travelers, represented as agents. These simulated scenarios are analyzed following their impact on the average travel times of the travelers (connected and no-connected). Results show that the number of connected travelers has a positive impact on overall travel times up until a certain threshold before becoming relatively negative

This term project desribes simulator of multiagent system implementation. It explains basic concepts of agent and multiagent systems. Simulator is implemented as a separated object, which allows to join various models of environment and agents acting in this environment. Evaluation of several types of agent cooperation behavior is in last section of this project.

Les travaux conduits dans le cadre de cette thèse ont pour but d’étendre le champ de validité des simulations de trafic en milieu urbain et péri-urbain, avec notamment une meilleure prise en compte du contexte de conduite, de l’hétérogénéité des véhicules (deux-roues motorisés, véhicule léger, poids lourd, etc.) et des comportements des conducteurs en termes d’occupation de l’espace au sol. Le but est de produire en simulation des comportements observés en situation réelle tels que les phénomènes de faufilement (deux-roues, véhicules d’urgence, cas des intersections). Nous abordons, la simulation du trafic routier en considérant une approche comportementale fondée sur les systèmes multi-agents. Des travaux précédents ont proposé des solutions non génériques, notamment pour le cas particulier des deux-roues motorisés. Nous proposons un nouveau modèle d’agent permettant de mieux prendre en compte le contexte de conduite et les comportements des conducteurs en termes d’occupation de l’espace. Nous dotons l’agent d’une représentation ego-centrée de l’environnement fondée sur le concept d’affordance. Nous utilisons ainsi les affordances pour identifier les actions possibles, en termes d’occupation de l’espace, offertes par l’environnement. Nous les utilisons en- suite pour construire une représentation ego-centrée de la situation. Le modèle d’agent proposé a été implémenté avec Archisim. Cette implémentation a permis de reproduire une situation de trafic réelle et de comparer les données simulées avec celles recueillies sur le terrain. Différentes expérimentations ont été menées afin d’évaluer la qualité de la solution proposée. / The work described in this thesis aims to improve the validity of traffic simulations in urban and suburban areas. We propose to consider the driving context, the heterogeneity of vehicles (motorized two-wheelers, vehicle, truck, etc.) and the driver behavior in terms of space occupation. The purpose is to produce in simulation the observed real world phenomena such as faufilement behavior (two-wheelers, emergency vehicles, cases of intersections). We address the traffic simulation byconsidering a behavioral approach based on multi-agent systems. Previous work have proposed non- generic solutions, for the particular case of motorized two-wheelers. We propose a new agent model to better take into account the context of driving and driver behavior in terms of space occupation. We endow agent driver with an ego-centered representation of the environment based on the concept of affordances. We use the affordances to identify the possible actions, in terms of space occupation, afforded by environment. The proposed agent model was implemented with ArchiSim. This implementation has allowed to reproduce a real traffic situation and to compare the results to real data. Different experiments were conducted to assess the quality of the proposed solution.

Nous explorons le lien entre partage d'information (PI) et gestion collective de ressources naturelles (GRN). Pour déterminer ce lien, nous nous sommes interessé aux ostréiculteurs, acteurs sensibles à la qualité de l'eau et touchés par un virulent virus. Ces acteurs ont développé et utilisent divers artéfacts de PI. Ces artifacts sont destinés à faire face à des menaces potentielles. L'identification de ce point fondamental a mené à développer le concept de menaces. Elles sont définies par le modèle <A,C,I,D,E> (Acteurs, Caractéristiques, Infrastructure, Décisions, Environnement). Elles sont organisées le long de deux axes: l'internalité et l'exclusivité. Formuler la situation des ostréiculteurs en utilisant ce concept permet une caractérisation des enjeux pour les artéfacts de PI pour la lutte contre les menaces. Nous avons utilisé le cadre ENCORE pour une analyse qualitative et la SMA pour une quantitative de l’impact des artéfacts. La recherche montre qu'ils peuvent avoir des buts, media et contenus variés, améliorer la réflexivité, ou mener à peu, voire aucun changement. Ces améliorations sont liées au processus de création de l'artéfact. / I explore how information sharing (IS) and natural resources management (NRM) are linked.To determine this link, I focused on oyster farmers, actors sensitive to water quality and impacted by a virulent virus. Those actors implemented and use diverse IS artifacts. Those artifacts are meant to face potential threats. Realizing this focal point led to develop the threat concept. They are defined by the <A,C,I,D,E> (Actors, Characteristics, Infrastructure, Decisions, Environnement) model. They are organized along two axes: internality and excludability. Framing oyster farmers’ situation with this concept allow a characterization of stakes for IS artifacts to tackle threats.I used the ENCORE framework for qualitative assessment of IS artifacts impacts and a MAS for a quantitative one. The research shows that they have various goals, media and contents; can increase reflexivity or have little to no impact. Those changes are linked to artifact creation process.