A scalable methodology for modeling cities as systems of systems

Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 144-146).As cities evolve in size and complexity, their component systems become more interconnected. Comprehensive modeling and simulation is needed to capture interactions and correctly assess the impact of changes. This thesis presents a methodology for modeling cities from a systems of systems perspective. The framework supplies general modeling guidelines and key steps. Also addressed are the importance of stakeholder interactions, creating the model structure, using smart city sensor data, and applying the methodology to larger, traditional cities. As an initial step, four city modeling including CityNet, CityOne, Sim City 4, and SoSAT software programs were evaluated from both a user and mathematical perspective. From the assessments, a list was developed of features critical to successful city modeling software including visualization, a streamlined user interface, accurate mathematics, the ability to specify systems and attributes, and the ability to model interconnections between systems. SoSAT was selected as the modeling tool for the case study, which involved modeling the Army's Base Camp Integration Laboratory. A model of the camp's baseline configuration was built and the camp was simulated for 30 days with results recorded at one hour intervals. 100 trials were run with averaged results presented by time intervals and for the total simulation time. Results were presented at all levels of structural aggregation. Two sensitivity analyses were conducted to analyze the impact of maintenance personnel and the frequency of potable water deliveries. Adding or subtracting a maintenance person impacted the availability of the generator systems that were being serviced, in turn impacting the performance of the micro grid. Extending the time between deliveries by 24 and 48 hours revealed two systems experienced resource depletions. Lastly, two technology insertions cases were conducted to assess the impact of adding a laundry water reuse system (LWRS) and a solar powered hot water heater (SHWH). The LWRS provided 70% of the laundry system's water needs, significantly reducing dependency upon deliveries. The SHWH was expected to decrease electricity consumption and increase fuel consumption. However, the reduction in energy demand meant fewer generators were needed to power the micro grid and both electricity and fuel consumption decreased.by Amanda M. Wachtel.S.M

Une approche pour supporter l'analyse qualitative des suites d'actions dans un environnement géographique virtuel et dynamique : l'analyse " What-if " comme exemple

Nous proposons une approche basée sur la géosimulation multi-agent et un outil d’aide à la décision pour supporter l’analyse « What-if » durant la planification des suites d’actions (plans) dans un environnement géographique dynamique. Nous présentons les caractéristiques du raisonnement « What-if » en tant 1) que simulation mentale 2) suivant un processus en trois étapes et 3) basé sur du raisonnement causal qualitatif. Nous soulignons les limites de la cognition humaine pour appliquer ce raisonnement dans le cadre de la planification des suites d’actions dans un environnement géographique dynamique et nous identifions les motivations de notre recherche. Ensuite, nous présentons notre approche basée sur la géosimulation multi-agent et nous identifions ses caractéristiques. Nous traitons en particulier trois problématiques majeures. La première problématique concerne la modélisation des phénomènes géographiques dynamiques. Nous soulignons les limites des approches existantes et nous présentons notre modèle basé sur le concept de situation spatio-temporelle que nous représentons en utilisant le formalisme de graphes conceptuels. En particulier, nous présentons comment nous avons défini ce concept en nous basant sur les archétypes cognitifs du linguiste J-P. Desclés. La deuxième problématique concerne la transformation des résultats d’une géosimulation multi-agent en une représentation qualitative exprimée en termes de situations spatio-temporelles. Nous présentons les étapes de traitement de données nécessaires pour effectuer cette transformation. La troisième problématique concerne l’inférence des relations causales entre des situations spatio-temporelles. En nous basant sur divers travaux traitant du raisonnement causal et de ses caractéristiques, nous proposons une solution basée sur des contraintes causales spatio-temporelles et de causalité pour établir des relations de causation entre des situations spatio-temporelles. Finalement, nous présentons MAGS-COA, une preuve de concept que nous avons implémentée pour évaluer l’adéquation de notre approche comme support à la résolution de problèmes réels. Ainsi, les principales contributions de notre travail sont: 1- Une approche basée sur la géosimulation multi-agent pour supporter l’analyse « What-if » des suites d’actions dans des environnements géographiques virtuels. 2- L’application d’un modèle issu de recherches en linguistique à un problème d’intérêt pour la recherche en raisonnement spatial. 3- Un modèle qualitatif basé sur les archétypes cognitifs pour modéliser des situations dynamiques dans un environnement géographique virtuel. 4- MAGS-COA, une plateforme de simulation et d’analyse qualitative des situations spatio-temporelles. 5- Un algorithme pour l’identification des relations causales entre des situations spatio-temporelles.We propose an approach and a tool based on multi-agent geosimulation techniques in order to support courses of action’s (COAs) “What if” analysis in the context of dynamic geographical environments. We present the characteristics of “What if” thinking as a three-step mental simulation process based on qualitative causal reasoning. We stress humans’ cognition limits of such a process in dynamic geographical contexts and we introduce our research motivations. Then we present our multi-agent geosimulation-based approach and we identify its characteristics. We address next three main problems. The first problem concerns modeling of dynamic geographical phenomena. We stress the limits of existing models and we present our model which is based on the concept of spatio-temporal situations. Particularly, we explain how we define our spatio-temporal situations based on the concept of cognitive archetypes proposed by the linguist J-P. Desclés. The second problem consists in transforming the results of multi-agent geosimulations into a qualitative representation expressed in terms of spatio-temporal situations and represented using the conceptual graphs formalism. We present the different steps required for such a transformation. The third problem concerns causal reasoning about spatio-temporal situations. In order to address this problem, we were inspired by works of causal reasoning research community to identify the constraints that must hold to identify causal relationships between spatio-temporal situations. These constraints are 1) knowledge about causality, 2) temporal causal constraints and 3) spatial causal constraints. These constraints are used to infer causal relationships among the results of multi-agent geosimulations. Finally, we present MAGS-COA, a proof on concept that we implemented in order to evaluate the suitability of our approach as a support to real problem solving. The main contributions of this thesis are: 1- An approach based on multi-agent geosimulation to support COA’s “What if” analysis in the context of virtual geographic environments. 2- The application of a model proposed in the linguistic research community to a problem of interest to spatial reasoning research community. 3- A qualitative model based on cognitive archetypes to model spatio-temporal situations. 4- MAGS-COA, a platform of simulation and qualitative analysis of spatio-temporal situations. 5- An algorithm to identify causal relationships between spatio-temporal situations