SICStus MT - A Multithreaded Execution Environment for SICStus Prolog

The development of intelligent software agents and other complex applications which continuously interact with their environments has been one of the reasons why explicit concurrency has become a necessity in a modern Prolog system today. Such applications need to perform several tasks which may be very different with respect to how they are implemented in Prolog. Performing these tasks simultaneously is very tedious without language support. This paper describes the design, implementation and evaluation of a prototype multithreaded execution environment for SICStus Prolog. The threads are dynamically managed using a small and compact set of Prolog primitives implemented in a portable way, requiring almost no support from the underlying operating system

Doctor of Philosophy

dissertationThe Active Traffic and Demand Management (ATDM) initiative aims to integrate various management strategies and control measures so as to achieve the mobility, environment and sustainability goals. To support the active monitoring and management of real-world complex traffic conditions, the first objective of this dissertation is to develop a travel time reliability estimation and prediction methodology that can provide informed decisions for the management and operation agencies and travelers. A systematic modeling framework was developed to consider a corridor with multiple bottlenecks, and a series of close-form formulas was derived to quantify the travel time distribution under both stochastic demand and capacity, with possible on-ramp and off-ramp flow changes. Traffic state estimation techniques are often used to guide operational management decisions, and accurate traffic estimates are critically needed in ATDM applications designed for reducing instability, volatility and emissions in the transportation system. By capturing the essential forward and backward wave propagation characteristics under possible random measurement errors, this dissertation proposes a unified representation with a simple but theoretically sound explanation for traffic observations under free-flow, congested and dynamic transient conditions. This study also presents a linear programming model to quantify the value of traffic measurements, in a heterogeneous data environment with fixed sensors, Bluetooth readers and GPS sensors. It is important to design comprehensive traffic control measures that can systematically address deteriorating congestion and environmental issues. To better evaluate and assess the mobility and environmental benefits of the transportation improvement plans, this dissertation also discusses a cross-resolution modeling framework for integrating a microscopic emission model with the existing mesoscopic traffic simulation model. A simplified car-following model-based vehicle trajectory construction method is used to generate the high-resolution vehicle trajectory profiles and resulting emission output. In addition, this dissertation discusses a number of important issues for a cloud computing-based software system implementation. A prototype of a reliability-based traveler information provision and dissemination system is developed to offer a rich set of travel reliability information for the general public and traffic management and planning organizations

Is There Such a Thing as a Post-Apartheid City?

In an introductory section, this paper considers briefly the achievements and problems of urban governance in post-apartheid South Africa through an assessment of three categories: administrative reform, developmental issues and conflicts over service delurban studies, Durban, South Africa, local government, private-public

Real-Time Vehicle Emission Estimation Using Traffic Data

The current state of climate change should be addressed by all sectors that contribute to it. One of the major contributors is the transportation sector, which generates a quarter of greenhouse gas emissions in North America. Most of these transportation related emissions are from road vehicles; as result, how to manage and control traffic or vehicular emissions is therefore becoming a major concern for the governments, the public and the transportation authorities. One of the key requirements to emission management and control is the ability to quantify the magnitude of emissions by traffic of an existing or future network under specific road plans, designs and traffic management schemes. Unfortunately, vehicular traffic emissions are difficult to quantify or predict, which has led a significant number of efforts over the past decades to address this challenge. Three general methods have been proposed in literature. The first method is for determining the traffic emissions of an existing road network with the idea of measuring the tail-pipe emissions of individual vehicles directly. This approach, while most accurate, is costly and difficult to scale as it would require all vehicles being equipped with tail-pipe emission sensors. The second approach is applying ambient pollutant sensors to measure the emissions generated by the traffic near the sensors. This method is only approximate as the vehicle-generated emissions can easily be confounded by other nearby emitters and weather and environmental conditions. Note that both of these methods are measurement-based and can only be used to evaluate the existing conditions (e.g., after a traffic project is implemented), which means that it cannot be used for evaluating alternative transportation projects at the planning stage. The last method is model-based with the idea of developing models that can be used to estimate traffic emissions. The emission models in this method link the amount of emissions being generated by a group of vehicles to their operations details as well as other influencing factors such as weather, fuel and road geometry. This last method is the most scalable, both spatially and temporally, and also most flexible as it can meet the needs of both monitoring (using field data) and prediction. Typically, traffic emissions are modelled on a macroscopic scale based on the distance travelled by vehicles and their average speeds. However, for traffic management applications, a model of higher granularity would be preferred so that impacts of different traffic control schemes can be captured. Furthermore, recent advances in vehicle detection technology has significantly increased the spatiotemporal resolutions of traffic data. For example, video-based vehicle detection can provide more details about vehicle movements and vehicle types than previous methods like inductive loop detection. Using such detection data, the vehicle movements, referred to as trajectories, can be determined on a second-by-second basis. These vehicle trajectories can then be used to estimate the emissions produced by the vehicles. In this research, we have proposed a new approach that can be used to estimate traffic generated emissions in real time using high resolution traffic data. The essential component of the proposed emission estimation method is the process to reconstruct vehicle trajectories based on available data and some assumptions on the expected vehicle motions including cruising, acceleration and deceleration, and car-following. The reconstructed trajectories containing instantaneous speed and acceleration data are then used to estimate emissions using the MOVES emission simulator. Furthermore, a simplified rate-based module was developed to replace the MOVES software for direct emission calculation, leading to significant improvement in the computational efficiency of the proposed method. The proposed method was tested in a simulated environment using the well-known traffic simulator - Vissim. In the Vissim model, the traffic activities, signal timing, and vehicle detection were simulated and both the original vehicle trajectories and detection data recorded. To evaluate the proposed method, two sets of emission estimates are compared: the “ground truth” set of estimates comes from the originally simulated vehicle trajectories, and the set from trajectories reconstructed using the detection data. Results show that the performance of the proposed method depends on many factors, such as traffic volumes, the placement of detectors, and which greenhouse gas is being estimated. Sensitivity analyses were performed to see whether the proposed method is sufficiently sensitive to the impacts of traffic control schemes. The results from the sensitivity analyses indicate that the proposed method can capture impacts of signal timing changes and signal coordination but is insufficiently sensitive to speed limit changes. Further research is recommended to validate the proposed method using field studies. Another recommendation, which falls outside of this area of research, would be to investigate the feasibility of equipping vehicles with devices that can record their instantaneous fuel consumption and location data. With this information, traffic controllers would be better informed for emission estimation than they would be with only detection data

Stochastic spreading on complex networks

Complex interacting systems are ubiquitous in nature and society. Computational modeling of these systems is, therefore, of great relevance for science and engineering. Complex networks are common representations of these systems (e.g., friendship networks or road networks). Dynamical processes (e.g., virus spreading, traffic jams) that evolve on these networks are shaped and constrained by the underlying connectivity. This thesis provides numerical methods to study stochastic spreading processes on complex networks. We consider the processes as inherently probabilistic and analyze their behavior through the lens of probability theory. While powerful theoretical frameworks (like the SIS-epidemic model and continuous-time Markov chains) already exist, their analysis is computationally challenging. A key contribution of the thesis is to ease the computational burden of these methods. Particularly, we provide novel methods for the efficient stochastic simulation of these processes. Based on different simulation studies, we investigate techniques for optimal vaccine distribution and critically address the usage of mathematical models during the Covid-19 pandemic. We also provide model-reduction techniques that translate complicated models into simpler ones that can be solved without resorting to simulations. Lastly, we show how to infer the underlying contact data from node-level observations.Komplexe, interagierende Systeme sind in Natur und Gesellschaft allgegenwärtig. Die computergestützte Modellierung dieser Systeme ist daher von immenser Bedeutung für Wissenschaft und Technik. Netzwerke sind eine gängige Art, diese Systeme zu repräsentieren (z. B. Freundschaftsnetzwerke, Straßennetze). Dynamische Prozesse (z. B. Epidemien, Staus), die sich auf diesen Netzwerken ausbreiten, werden durch die spezifische Konnektivität geformt. In dieser Arbeit werden numerische Methoden zur Untersuchung stochastischer Ausbreitungsprozesse in komplexen Netzwerken entwickelt. Wir betrachten die Prozesse als inhärent probabilistisch und analysieren ihr Verhalten nach wahrscheinlichkeitstheoretischen Fragestellungen. Zwar gibt es bereits theoretische Grundlagen und Paradigmen (wie das SIS-Epidemiemodell und zeitkontinuierliche Markov-Ketten), aber ihre Analyse ist rechnerisch aufwändig. Ein wesentlicher Beitrag dieser Arbeit besteht darin, die Rechenlast dieser Methoden zu verringern. Wir erforschen Methoden zur effizienten Simulation dieser Prozesse. Anhand von Simulationsstudien untersuchen wir außerdem Techniken für optimale Impfstoffverteilung und setzen uns kritisch mit der Verwendung mathematischer Modelle bei der Covid-19-Pandemie auseinander. Des Weiteren führen wir Modellreduktionen ein, mit denen komplizierte Modelle in einfachere umgewandelt werden können. Abschließend zeigen wir, wie man von Beobachtungen einzelner Knoten auf die zugrunde liegende Netzwerkstruktur schließt