Uncertainty and Variability Analysis of Agent-Based Transport Models

This paper presents an analysis of the output variability of agent-based transport models. We simulated a MATSim model of the city of Hanover multiple times with identical input and evaluated the resulting travel times on different level of aggregation. On a global level, we observed minor variations of travel times. However, the results show an increased variation when examining the output on the level of districts or for individual agents. A recommendation for estimating the required number of simulation runs for a stable output of travel time for the purposed aggregation level is derived from our case study

Microsimulation models incorporating both demand and supply dynamics

There has been rapid growth in interest in real-time transport strategies over the last decade, ranging from automated highway systems and responsive traffic signal control to incident management and driver information systems. The complexity of these strategies, in terms of the spatial and temporal interactions within the transport system, has led to a parallel growth in the application of traffic microsimulation models for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a difficulty that remains is the question of how the secondary impacts, specifically the effect on route and departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation framework. The paper describes a modelling approach to road network traffic, in which the emphasis is on the integrated microsimulation of individual trip-makers’ decisions and individual vehicle movements across the network. To achieve this it represents directly individual drivers’ choices and experiences as they evolve from day-to-day, combined with a detailed within-day traffic simulation model of the space–time trajectories of individual vehicles according to car-following and lane-changing rules and intersection regulations. It therefore models both day-to-day and within-day variability in both demand and supply conditions, and so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed above. The full model specification is given, along with details of its algorithmic implementation. A number of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-day variability; an application to the evaluation of alternative signal control policies; and the evaluation of the introduction of bus-only lanes in a sub-network of Leeds. Our experience demonstrates that this modelling framework is computationally feasible as a method for providing a fully internally consistent, microscopic, dynamic assignment, incorporating both within- and between-day demand and supply dynamic

Mathematical modelling and simulation of stochastic collisional exchange processes

Collision-exchange processes play a prominent role in a variety of natural systems where system members interact to engender the change of quantities, material transfer and information exchange over a population, driving a macroscopic evolution of system state in time. Mathematical modelling provides a useful approach to the quantitative characterisation of collision-exchange processes in order to assist the identification of change of system state. The established mathematical model that involves state variables and model parameters is expected to be identified based on the relevant experimental observations. Although collision-exchange processes have been extensively studied in many systems, especially in particulate systems, by formulating models based on discrete element methods, these models still suffer from several limitations, in particular the significant computational intensity required by simulations that restrict the further research into the models, leading to the difficulty using these models in model-based tasks, including design of experiments and optimisation. This project focuses on the investigation of a stochastic modelling approach for collision-exchange processes and the development of identification strategies for stochastic models. The work addresses the following challenges: i) the development of a stochastic model to simulate the collision-exchange process and predict the dynamical evolution of system state within tractable computational time; ii) the design and execution of experiments in an industrial seed coating process for the verification of the established stochastic model; iii) the development of parameter estimation and model-based design of experiments techniques suitable for stochastic models, i.e. the model outputs with uncertainty. The work presented in the Thesis facilitates an alternative modelling approach for collision-exchange processes, providing a systematic methodology for the identification and optimisation of stochastic systems with higher accuracy in prediction and less computational intensity

Deep Impact: Geo-Simulations as a Policy Toolkit for Natural Disasters

Adverse post-natural disaster outcomes in low-income regions, like elevated internal migration levels and low consumption levels, are the result of market failures, poor mechanisms for stabilizing income, and missing insurance markets, which force the affected population to respond, and adapt to the shock they face. In a spatial environment, with multiple locations with independent but inter-connected markets, these transitions quickly become complex and highly non-linear due to the feedback loops between the micro individual-level decisions and the meso location-wise market decisions. To capture these continuously evolving micro–meso interactions, this paper presents a spatially explicit bottom-up agent-based model to analyze natural disaster-like shocks to low-income regions. The aim of the model is to temporally and spatially track how population distributions, income, and consumption levels evolve, in order to identify low-income workers that are “food insecure”. The model is applied to the 2005 earthquake in northern Pakistan, which faced catastrophic losses and high levels of displacement in a short time span, and with market disruptions, resulted in high levels of food insecurity. The model is calibrated to pre-crisis trends, and shocked using distance-based output and labor loss functions to replicate the earthquake impact. Model results show, how various factors like existing income and saving levels, distance from the fault line, and connectivity to other locations, can give insights into the spatial and temporal emergence of vulnerabilities. The simulation framework presented here, leaps beyond existing modeling efforts, which usually deals with macro long-term loss estimates, and allows policy makers to come up with informed short-term policies in an environment where data is non-existent, policy response is time dependent, and resources are limited

Modelling plant trait variability in changing arid environments

Modellierung der Variabilität von Pflanzen-Traits auf Populations- und Lebensgemeinschaftsebene in ariden Gebieten mit Umweltveränderungen. Lebensgemeinschaften in ariden Gebieten sind angesichts globaler Umweltveränderungen besonders anfällig, da sie höchst unvorhersagbaren Umweltbedingungen ausgesetzt sind. Das Schicksal von Gemeinschaften in einer ungewissen Zukunft kann durch das Verständnis der Triebkräfte dieser Gemeinschaften aufgeklärt werden. Das Zusammenspiel der Triebkräfte der Gemeinschaften kann mit Hilfe von Ansätzen entschlüsselt werden, die auf funktionalen Merkmalen (Traits) basieren, weil sie Pflanzenstrategien und die Reaktionen der Gemeinschaften auf Umweltveränderungen beschreiben können. Darüber hinaus liefert die inter- und intraspezifische Variabilität der Traits die notwendigen Anhaltspunkte für die Identifizierung von Überlebensstrategien von Wüstenpflanzen unter wechselhaften Umweltbedingungen. Die Erforschung von Wüstenpflanzengemeinschaften könnte jedoch aufgrund der räumlichen und zeitlichen Heterogenität der ariden Umweltbedingungen eine Herausforderung darstellen. Modellierungsansätze unterstützen und ergänzen empirische, trait-basierte Ansätze bei der Erforschung von Wüstenpflanzengemeinschaften und ihrer Triebkräfte und Dynamik in sich verändernden ariden Gebieten. Das Gesamtziel dieser Arbeit war es, die intra- und interspezifische Variabilität der funktionalen Traits in ariden Umgebungen zu erforschen und zu untersuchen, wie sich diese Variabilität auf die Fähigkeit von Pflanzen auswirkt, Trockenstress zu tolerieren und in der Konkurrenz mit ihren Nachbarn erfolgreich zu sein. Um dieses Ziel zu erreichen, habe ich ein räumlich-explizites individuen- und trait-basiertes Simulationsmodell entwickelt, implementiert und analysiert, ein Simulationsexperiment durchgeführt, Daten aus empirischen Experimenten analysiert und einen Überblick der Literatur zu trait-basierten Modellen und Metamodellierungsansätzen zusammengestellt. Meine Forschung basiert auf Daten zu annuellen Pflanzengemeinschaften in der Wüste Negev in Israel, die von der Echte Rose von Jericho (Anastatica hierochuntica) dominiert werden. Die Literaturzusammenschau in Kapitel 1 offenbart, dass trait-basierte Modelle eine geeignete Methode sind, um Veränderungen in den Mustern von Gemeinschaften unter globalen Veränderungen vorherzusagen und die zugrunde liegenden Mechanismen der Zusammensetzung und Dynamik von Lebensgemeinschaften zu verstehen. Durch die Kombination von Modellierung und trait-basierten Ansätzen lassen sich technische Herausforderungen, Skalierungsprobleme und Datenknappheit überwinden. Insbesondere wurde eine Kombination aus trait-basierten Ansätzen und individuenbasierter Modellierung empfohlen, um die Parametrisierung der Modelle zu vereinfachen, Interaktionen zwischen Pflanzen auf individueller Ebene zu erfassen und die Gemeinschaftsdynamik zu erklären. Eine Forderung aus Kapitel 1 umsetzend wurde in Kapitel 2 das räumlich-explizite, trait- und individuenbasierte ATID-Modell entwickelt, implementiert und analysiert, um zu untersuchen, wie Gemeinschaftsdynamiken aus Pflanzentraits und Interaktionen von Pflanzen untereinander und mit ihrer Umwelt entstehen. Die Sensitivitätsanalyse des Modells hob die funktionalen Traits von Pflanzen als Schlüsselfaktoren der Gemeinschaftsdynamik hervor, wobei den Umweltfaktoren im Modell eine relativ geringere Bedeutung zugewiesen wurde. Die sensitivitätverursachenden Traits umfassten sowohl solche Traits, die an den Pflanze-Pflanze-Interaktionen beteiligt waren, wie zum Beispiel die relative Wachstumsrate und maximale Biomasse, als auch solche, die die Toleranz gegenüber abiotischem Stress fördern, wie die Keimruhe und Keimungswahrscheinlichkeit. Unter den Umweltfaktoren waren die Verfügbarkeit von Bodenwasser und Niederschlag die einflussreichsten Faktoren. Die besondere Rolle von funktionalen Traits in der Gemeinschaftsdynamik einjähriger Wüstenpflanzen zeigt die Bedeutung trait-basierter Strategien als Anpassung an die harschen Bedingungen in ariden Gebieten. Kapitel 3 befasst sich mit den Ergebnissen eines Simulationsexperiments, das mit dem ATID-Modell durchgeführt wurde. Dieses Experiment untersuchte den Einfluss funktionaler Traits auf die Gemeinschaftsdynamik, die bei zwei Überlebensstrategien eine Rolle spielen, die in der Studie in einem neuen Strategiekonzept als "Schutz-Konkurrenz"- und "Flucht-Kolonisierungs"-Strategien definiert wurden. Diese Strategien unterschieden sich nicht nur in der Samengröße und der Anzahl der Samen, sondern auch in bestimmten Pflanzentraits, die mit Konkurrenz und Überleben zusammenhängen und die in der Sensitivitätsanalyse des Modells aus Kapitel 2 hervorgehoben worden waren. Die Integration der Konzepte des Kolonisierung-Konkurrenz-Trade-offs und des Entkommens in Zeit und Raum in einem neuen Strategiekonzept ergab eine realistischere Darstellung der Arten, da die integrierten Strategien den gesamten Lebenszyklus der Pflanze berücksichtigen. Um ein besseres Verständnis empirischer Trait-Verteilungen zu erlangen, wurden in Kapitel 4 Daten zur intraspezifischen Traitvariabilität und zu Trait-Räumen der annuellen Wüstenpflanze A. hierochutica aus einem Gewächshausversuch analysiert. Hohe Salzkonzentrationen hatten signifikante Auswirkungen auf die Durchschnittswerte der funktionalen Traits der Pflanzen. Zusätzlich beeinflusste Salzstress die intraspezifischen Trait-Räume unterschiedlich in Bezug auf die Umweltbedingungen des Ursprungsortes der Pflanzen. Die Trait-Räume der Populationen, die vom gleichen Standort stammten, aber unterschiedlichen Salzstress-Niveaus ausgesetzt waren, wurden mit zunehmender Aridität unähnlicher. Daher erwiesen sich die intraspezifische Trait-Variabilität und die Salzeffekte als wesentlich für die Aufdeckung von Prozessen auf Populations- und Lebensgemeinschaftsebene in Wüsten und sollten in zukünftigen Versionen des ATID-Modells berücksichtigt werden. Zur Unterstützung der zukünftigen Entwicklung des in Kapitel 2 entwickelten ATID-Modells wurden in Kapitel 5 Metamodelltypen und ihre Anwendungsbereiche in der individuenbasierten Modellierung überprüft und bewertet. Die Überprüfung berücksichtigte 40 Metamodelle, die für die Sensitivitätsanalyse, Kalibrierung, Vorhersage und Skalierung von individuenbasierten Modellen eingesetzt werden können und als Leitfaden für die Implementierung und Validierung von Metamodellen dienen können. Insgesamt beleuchtet diese Arbeit und insbesondere die Analysen des ATID-Modells, wie trait-basierte Modellierungsansätze zum Verständnis des Zusammenspiels der Schlüsseltriebkräfte von Wüstenpflanzengemeinschaften in ariden Umgebungen beitragen können. Die begleitende Analyse des Gewächshausexperiments und die kritischen Literaturübersichten dienen als Grundlage für zukünftige Erweiterungen des Modells und die in dieser Arbeit identifizierten Wege zur Überwindung technischer Herausforderungen und Datenknappheit. Darüber hinaus empfiehlt diese Dissertation eine intensivere Untersuchung der Strategien annueller Wüstenpflanzen für das Überleben unter zeitlich und räumlich heterogenen Umweltbedingungen mit besonderem Schwerpunkt auf funktionalen Pflanzen-Traits. Somit bietet das in dieser Arbeit vorgestellte Grundmodell die Basis für zukünftige Forschungen über das Schicksal von Lebensgemeinschaften in ariden Gebieten unter dem Einfluss globaler Umweltveränderungen.Communities in arid environments are especially vulnerable to global change because they experience highly unpredictable environmental conditions. The fate of communities in an uncertain future may be elucidated by understanding the drivers of these communities. The interplay between community drivers may be unravelled by using approaches based on functional traits because traits describe plant strategies and the responses of communities to environmental changes. Furthermore, inter- and intraspecific trait variability provides the necessary cues to identify survival strategies of desert plants under fluctuating environmental conditions. However, studying desert plant communities is challenging due to the spatial and temporal heterogeneity of arid environments. Modelling approaches support and complement empirical trait-based approaches in exploring desert plant communities and their drivers and dynamics in changing arid environments. The overarching aim of this thesis was to explore intra- and inter-specific variability of functional traits in arid environments and to investigate how this variability affects the ability of plants to tolerate aridity stress and succeed in competition with their neighbours. To address this aim, I developed, implemented and analysed a spatially explicit individual- and trait-based simulation model, conducted a simulation experiment, analysed data from model simulations and empirical experiments and synthesized the literature on trait-based models and metamodelling approaches. My research was focused on annual plant communities dominated by the True Rose of Jericho (Anastatica hierochuntica L.) in the Negev desert in Israel. According to the review in chapter 1, trait-based models are a suitable method to predict changes in community patterns under global change and to understand the underlying mechanisms of community assembly and dynamics. Combining modelling and trait-based approaches overcomes technical challenges, scaling problems, and data scarcity. Specifically, a combination of trait-based approaches and individual-based modelling was recommended to simplify the parameterization of models and to capture plant-plant interactions at the individual level, and to explain community dynamics. In chapter 2, in line with the major claim of chapter 1, the spatially explicit trait- and individual-based ATID-model was developed, implemented and analysed to explore how community dynamics arise from plant traits and the interactions among plants and with their environment. The sensitivity analysis of the model highlighted plant functional traits as key drivers of community dynamics and indicated that environmental factors were less important in the model. The outlined traits included both those traits that are involved in plant-plant interactions, such as relative growth rate and maximum biomass, and those that promote tolerance to abiotic stress, such as dormancy and germination probability. Among the environmental factors, the most influential factors were soil water availability and precipitation. The special role of functional traits in the community dynamics of desert annual plants indicates the importance of trait-based strategies as an adaptation to the stressful arid environment. Chapter 3 addresses the results from a simulation experiment that was conducted in the ATID-model. This experiment explored the influence of functional traits involved in two survival strategies defined in the study as ‘protective-competition’ and ‘escape-colonization’ strategies on community dynamics. These strategies differed not only in seed size and the number of seeds, but also in the plant functional traits related to competition and survival, which were highlighted in the sensitivity analysis of the model from chapter 2. Merging the colonization-competition trade-off with escape in time and space into one strategy set provided a more realistic representation of species because the merged strategies related to the entire plant life cycle. To gain more understanding on empirical trait distributions, in chapter 4 data on intraspecific trait variability and trait spaces of the desert annual plant A. hierochutica from a nethouse experiment were analysed. High salinity had significant effects on the average values of plant functional traits. Additionally, salinity stress affected the intraspecific trait spaces differentially with respect to the environmental conditions of the site of origin. Trait spaces of the populations originating from the same site but exposed to different salt stress levels became more dissimilar with increasing environmental aridity. Thus, intraspecific trait variability and salinity effects turned out to be essential in revealing population- and community-level processes in deserts and should be considered in future versions of the ATID-model. In support of the future development of the ATID-model developed in chapter 2, common metamodel types and the purposes of their usage for individual-based models were reviewed and evaluated in chapter 5. The review considered 40 metamodels applied for sensitivity analysis, calibration, prediction and scaling-up of individual-based models and can be used as a guide for the implementation and validation of metamodels. Overall, this thesis, and particularly the ATID-model analyses, highlights how trait-based modelling approaches can contribute to understanding the interplay between key drivers of desert plant communities in arid environments. The accompanying analysis of the nethouse experiment and critical literature reviews outline future extensions of the model and the ways to overcome the technical challenges and data scarcity identified in this thesis. Moreover, this thesis advocates for more intensive studies of the strategies of desert annual plants to survive in temporally and spatially heterogeneous environments with a focus on plant functional traits. Thus, the modelling framework presented in this thesis provides the basis for future research on the fate of communities in arid environments under global change

Deep learning for real-time traffic signal control on urban networks

Real-time traffic signal controls are frequently challenged by (1) uncertain knowledge about the traffic states; (2) need for efficient computation to allow timely decisions; (3) multiple objectives such as traffic delays and vehicle emissions that are difficult to optimize; and (4) idealized assumptions about data completeness and quality that are often made in developing many theoretical signal control models. This thesis addresses these challenges by proposing two real-time signal control frameworks based on deep learning techniques, followed by extensive simulation tests that verifies their effectiveness in view of the aforementioned challenges. The first method, called the Nonlinear Decision Rule (NDR), defines a nonlinear mapping between network states and signal control parameters to network performances based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past. The NDR is implemented and tested within microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization procedure aiming to reduce delay, CO2 and black carbon emissions. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in optimizing different traffic and environmental objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The second method, called the Advanced Reinforcement Learning (ARL), employs the potential-based reward shaping function using Q-learning and 3rd party advisor to enhance its performance over conventional reinforcement learning. The potential-based reward shaping in this thesis obtains an opinion from the 3rd party advisor when calculating reward. This technique can resolve the problem of sparse reward and slow learning speed. The ARL is tested with a range of existing reinforcement learning methods. The results clearly show that ARL outperforms the other models in almost all the scenarios. Lastly, this thesis evaluates the impact of information availability and quality on different real-time signal control methods, including the two proposed ones. This is driven by the observation that most responsive signal control models in the literature tend to make idealized assumptions on the quality and availability of data. This research shows the varying levels of performance deterioration of different signal controllers in the presence of missing data, data noise, and different data types. Such knowledge and insights are crucial for real-world implementation of these signal control methods.Open Acces