Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

Displaying 1 - 2 of 2
Filtering by

Clear all filters

157491-Thumbnail Image.png

GeoSparkSim: A Scalable Microscopic Road Network Traffic Simulator Based on Apache Spark

Description
Researchers and practitioners have widely studied road network traffic data in different areas such as urban planning, traffic prediction and spatial-temporal databases. For instance, researchers use such data to evaluate the impact of road network changes. Unfortunately, collecting large-scale high-quality urban traffic data requires tremendous efforts because participating vehicles must…

Researchers and practitioners have widely studied road network traffic data in different areas such as urban planning, traffic prediction and spatial-temporal databases. For instance, researchers use such data to evaluate the impact of road network changes. Unfortunately, collecting large-scale high-quality urban traffic data requires tremendous efforts because participating vehicles must install Global Positioning System(GPS) receivers and administrators must continuously monitor these devices. There have been some urban traffic simulators trying to generate such data with different features. However, they suffer from two critical issues (1) Scalability: most of them only offer single-machine solution which is not adequate to produce large-scale data. Some simulators can generate traffic in parallel but do not well balance the load among machines in a cluster. (2) Granularity: many simulators do not consider microscopic traffic situations including traffic lights, lane changing, car following. This paper proposed GeoSparkSim, a scalable traffic simulator which extends Apache Spark to generate large-scale road network traffic datasets with microscopic traffic simulation. The proposed system seamlessly integrates with a Spark-based spatial data management system, GeoSpark, to deliver a holistic approach that allows data scientists to simulate, analyze and visualize large-scale urban traffic data. To implement microscopic traffic models, GeoSparkSim employs a simulation-aware vehicle partitioning method to partition vehicles among different machines such that each machine has a balanced workload. The experimental analysis shows that GeoSparkSim can simulate the movements of 200 thousand cars over an extensive road network (250 thousand road junctions and 300 thousand road segments).
ContributorsFu, Zishan (Author) / Sarwat, Mohamed (Thesis advisor) / Pedrielli, Giulia (Committee member) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2019
161785-Thumbnail Image.png

Disaster Analytics for Critical Infrastructures : Methods and Algorithms for Modeling Disasters and Proactive Recovery Preparedness

Description
Natural disasters are occurring increasingly around the world, causing significant economiclosses. To alleviate their adverse effect, it is crucial to plan what should be done in response to them in a proactive manner. This research aims at developing proactive and real-time recovery algorithms for large-scale power networks exposed to weather events considering uncertainty.…

Natural disasters are occurring increasingly around the world, causing significant economiclosses. To alleviate their adverse effect, it is crucial to plan what should be done in response to them in a proactive manner. This research aims at developing proactive and real-time recovery algorithms for large-scale power networks exposed to weather events considering uncertainty. These algorithms support the recovery decisions to mitigate the disaster impact, resulting in faster recovery of the network. The challenges associated with developing these algorithms are summarized below: 1. Even ignoring uncertainty, when operating cost of the network is considered the problem will be a bi-level optimization which is NP-hard. 2. To meet the requirement for real-time decision making under uncertainty, the problem could be formulated a Stochastic Dynamic Program with the aim to minimize the total cost. However, considering the operating cost of the network violates the underlying assumptions of this approach. 3. Stochastic Dynamic Programming approach is also not applicable to realistic problem sizes, due to the curse of dimensionality. 4. Uncertainty-based approaches for failure modeling, rely on point-generation of failures and ignore the network structure. To deal with the first challenge, in chapter 2, a heuristic solution framework is proposed, and its performance is evaluated by conducting numerical experiments. To address the second challenge, in chapter 3, after formulating the problem as a Stochastic Dynamic Program, an approximated dynamic programming heuristic is proposed to solve the problem. Numerical experiments on synthetic and realistic test-beds, show the satisfactory performance of the proposed approach. To address the third challenge, in chapter 4, an efficient base heuristic policy and an aggregation scheme in the action space is proposed. Numerical experiments on a realistic test-bed verify the ability of the proposed method to recover the network more efficiently. Finally, to address the fourth challenge, in chapter 5, a simulation-based model is proposed that using historical data and accounting for the interaction between network components, allows for analyzing the impact of adverse events on regional service level. A realistic case study is then conducted to showcase the applicability of the approach.
ContributorsInanlouganji, Alireza (Author) / Pedrielli, Giulia (Thesis advisor) / Mirchandani, Pitu (Committee member) / Reddy, T. Agami (Committee member) / Ju, Feng (Committee member) / Arizona State University (Publisher)
Created2021