Dynamic load balancing of parallel road traffic simulation

The objective of this research was to investigate, develop and evaluate dynamic load-balancing strategies for parallel execution of microscopic road traffic simulations. Urban road traffic simulation presents irregular, and dynamically varying distributed computational load for a parallel processor system. The dynamic nature of road traffic simulation systems lead to uneven load distribution during simulation, even for a system that starts off with even load distributions. Load balancing is a potential way of achieving improved performance by reallocating work from highly loaded processors to lightly loaded processors leading to a reduction in the overall computational time. In dynamic load balancing, workloads are adjusted continually or periodically throughout the computation. In this thesis load balancing strategies were evaluated and some load balancing policies developed. A load index and a profitability determination algorithms were developed. These were used to enhance two load balancing algorithms. One of the algorithms exhibits local communications and distributed load evaluation between the neighbour partitions (diffusion algorithm) and the other algorithm exhibits both local and global communications while the decision making is centralized (MaS algorithm). The enhanced algorithms were implemented and synthesized with a research parallel traffic simulation. The performance of the research parallel traffic simulator, optimized with the two modified dynamic load balancing strategies were studied

Smart mobility: opportunity or threat to innovate places and cities

The concept of the “smart mobility” has become something of a buzz phrase in the planning and transport fields in the last decade. After a fervent first phase in which information technology and digital data were considered the answer for making mobility more efficient, more attractive and for increasing the quality of travel, some disappointing has grown around this concept: the distance between the visionarypotentialthatsmartness is providingis too far from the reality of urban mobility in cities. We argue in particular that two main aspects of smart mobility should be eluded: the first refers to the merely application to technology on mobility system, what we called the techo-centric aspect; the second feature is the consumer-centric aspect of smart mobility, that consider transport users only as potential consumers of a service. Starting from this, the study critics the smart mobility approach and applications and argues on a“smarter mobility” approach, in which technologies are only oneaspects of a more complex system. With a view on the urgency of looking beyond technology and beyond consumer-oriented solutions, the study arguments the need for a cross-disciplinary and a more collaborative approach that could supports transition towards a“smarter mobility” for enhancing the quality of life and the development ofvibrant cities. The article does not intend to produce a radical critique of the smart mobility concept,denying a priori its utility. Our perspectiveisthat the smart mobility is sometimes used as an evocativeslogan lacking some fundamental connection with other central aspect of mobility planning and governance. Main research questions are: what is missing in the technology-oriented or in the consumers-oriented smart mobility approach? What are the main risks behind these approaches? To answer this questions the paper provides in Section 2 the rationale behind the paper;Section 3 provides a literature review that explores the evolution on smart mobility paradigm in the last decades analysing in details the “techno-centric”and the “consumer-centric” aspects. Section 4proposes an integrated innovative approach for smart mobility, providing examples and some innovative best practices in Belgium. Some conclusions are finally drawnin Section 5, based on the role of smart mobility to create not only virtual platforms but high quality urban places

Simulation-based Validation for Autonomous Driving Systems

Simulation is essential to validate autonomous driving systems. However, a simple simulation, even for an extremely high number of simulated miles or hours, is not sufficient. We need well-founded criteria showing that simulation does indeed cover a large fraction of the relevant real-world situations. In addition, the validation must concern not only incidents, but also the detection of any type of potentially dangerous situation, such as traffic violations. We investigate a rigorous simulation and testing-based validation method for autonomous driving systems that integrates an existing industrial simulator and a formally defined testing environment. The environment includes a scenario generator that drives the simulation process and a monitor that checks at runtime the observed behavior of the system against a set of system properties to be validated. The validation method consists in extracting from the simulator a semantic model of the simulated system including a metric graph, which is a mathematical model of the environment in which the vehicles of the system evolve. The monitor can verify properties formalized in a first-order linear temporal logic and provide diagnostics explaining their non satisfaction. Instead of exploring the system behavior randomly as many simulators do, we propose a method to systematically generate sets of scenarios that cover potentially risky situations, especially for different types of junctions where specific traffic rules must be respected. We show that the systematic exploration of risky situations has uncovered many flaws in the real simulator that would have been very difficult to discover by a random exploration process

Advancing Urban Flood Resilience With Smart Water Infrastructure

Advances in wireless communications and low-power electronics are enabling a new generation of smart water systems that will employ real-time sensing and control to solve our most pressing water challenges. In a future characterized by these systems, networks of sensors will detect and communicate flood events at the neighborhood scale to improve disaster response. Meanwhile, wirelessly-controlled valves and pumps will coordinate reservoir releases to halt combined sewer overflows and restore water quality in urban streams. While these technologies promise to transform the field of water resources engineering, considerable knowledge gaps remain with regards to how smart water systems should be designed and operated. This dissertation presents foundational work towards building the smart water systems of the future, with a particular focus on applications to urban flooding. First, I introduce a first-of-its-kind embedded platform for real-time sensing and control of stormwater systems that will enable emergency managers to detect and respond to urban flood events in real-time. Next, I introduce new methods for hydrologic data assimilation that will enable real-time geolocation of floods and water quality hazards. Finally, I present theoretical contributions to the problem of controller placement in hydraulic networks that will help guide the design of future decentralized flood control systems. Taken together, these contributions pave the way for adaptive stormwater infrastructure that will mitigate the impacts of urban flooding through real-time response.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163144/1/mdbartos_1.pd

Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping

Acknowledgments We thank Johan Havelaar, Aeryon Labs Inc., AeronVironment Inc. and Aeronautics Inc. for kindly permitting the use of materials in Fig. 1.Peer reviewedPublisher PD

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

Sem resumo disponível.publishe

Safety impact of connected and autonomous vehicles on motorways: a traffic microsimulation study

Connected and Autonomous Vehicles (CAVs) promise to improve road safety greatly. Despite the numerous CAV trials around the globe, their benefit has yet to be proven using real-world data. The lack of real-world CAV data has shifted the focus of the research community from traditional safety impact assessment methods to traffic microsimulation in order to evaluate their impacts. However, a plethora of operational, tactical and strategic challenges arising from the implementation of CAV technology remain unaddressed. This thesis presents an innovative and integrated CAV traffic microsimulation framework that aims to cover the aforementioned shortcomings.A new CAV control algorithm is developed in C++ programming language containing a longitudinal and lateral control algorithm that for the first time takes into consideration sensor error and vehicle platoon formulation of various sizes. A route-based decision-making algorithm for CAVs is also developed. The algorithm is applied to a simulated network of the M1 motorway in the United Kingdom which is calibrated and validated using instrumented vehicle data and inductive loop detector data. Multiple CAV market penetration rate, platoon size and sensor error rate scenarios are formulated and evaluated. Safety evaluation is conducted using traffic conflicts as a safety surrogate measure which is a function of time-to-collision and post encroachment time. The results reveal significant safety benefit (i.e. 10-94% reduction of traffic conflicts) as CAV market penetration increases from 0% to 100%; however, it is underlined that special focus should be given in the motorway merging and diverging areas where CAVs seem to face the most challenges. Additionally, it is proven that if the correct CAV platoon size is implemented at the appropriate point in time, greater safety benefits may be achieved. Otherwise, safety might deteriorate. However, sensor error does not affect traffic conflicts for the studied network. These results could provide valuable insights to policy makers regarding the reconfiguration of existing infrastructure to accommodate CAVs, the trustworthiness of existing CAV equipment and the optimal platoon size that should be enforced according to the market penetration rate.Finally, in order to forecast the conflict reduction for any given market penetration rate and understand the underlying factors behind traffic conflicts in a traffic microsimulation environment in-depth, a hierarchical spatial Bayesian negative binomial regression model is developed, based on the simulated CAV data. The results exhibit that besides CAV market penetration rate, speed variance across lanes significantly affects the production of simulated conflicts. As speed variance increases, the safety benefit decreases. These results emphasize the importance of speed homogeneity between lanes in a motorway as well as the increased risk in the motorway merging/diverging areas.</div

Energy Efficient Route Planning Using VANET

One of the key challenges in conducting dynamic route planning is the process of collecting and disseminating instantaneous travel data in real time. Recent studies are evaluating VANET (Vehicular Ad Hoc Network) and its associated WAVE (Wireless Access in Vehicular Environment) standards to facilitate this process. In these studies, travel data accumulated from vehicle OBUs (on board unit) are shared with other vehicles over DSRC (dedicated short- range communication) medium using centralized or distributed approach. In most studies, data collection and dissemination process are not scalable enough for high density traffic environment. Specifically, with a centralized approach, if traffic management center (TMC) or Road Side Unit (RSU) performs route planning for vehicles, there will be many bidirectional communications between the centralized entity and vehicles, leading to higher channel congestion in heavy traffic areas. With a distributed approach, information shared by other vehicles might not be useful or pertinent for some vehicles, leading to wastage of channel bandwidth. Methods used for data collection also need to be intelligent to count in nontraditional circumstances to achieve accuracy. In this thesis, we have proposed a three tiered architecture for data collection, analysis and dissemination. In addition, 1) we demonstrated the concept of queuing delay at intersection for travel time calculation and developed a hybrid metric that considers average travel time and occupancy rate, 2) we offload the computation of route planning to vehicle OBUs and 3) we developed an algorithm that determines the area of propagation for data that needs to be disseminated. We evaluated the performance of our approach progressively using VEINS, SUMO and OMNET++ simulators