Matching Items (44)
Description
Recurring incidents between pedestrians, bicycles, and vehicles at the intersection of Rural Road and Spence Avenue led to a team of students conducting their own investigation into the current conditions and analyzing a handful of alternatives. An extension of an industry-standard technique was used to build a control case which alternatives would be compared to. Four alternatives were identified, and the two that could be modeled in simulation software were both found to be technically feasible in the preliminary analysis.
ContributorsFellows, Christopher Lee (Author) / Lou, Yingyan (Thesis director) / Zhou, Xuesong (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Gathering the necessary information required to tackle traffic congestion problems is generally time consuming and challenging but is an important part of city planners’ work. The purpose of this paper is to describe the methodology used when analyzing potential solutions for the Arizona State Route 89A and Highway 179 roundabout in Sedona, Arizona; which is currently experiencing significant congestion. The oversaturated condition is typically applied to signalized intersections but its application to roundabouts requires further exploration for future management of similar transportation systems. The accompanying Quick Estimation and Simulation model (QESM) spreadsheet was calibrated using an iterative process to optimize its level of adaptability to various scenarios. This microsimulation modeling program can be used to predict the outcome of possible roadway improvements aimed at decreasing traffic congestion. The information provided in this paper helps users understand traffic system problems, as a primary to visual simulation programs.
ContributorsBrunetti, Isabel (Co-author) / Tran, Adam (Co-author) / Zhou, Xuesong (Thesis director) / Carreon, Adam (Committee member) / Civil, Environmental and Sustainable Eng Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Commuting is a significant cost in time and in travel expenses for working individuals and a major contributor to emissions in the United States. This project focuses on increasing the efficiency of an intersection through the use of "light metering." Light metering involves a series of lights leading up to an intersection forcing cars to stop further away from the final intersection in smaller queues instead of congregating in a large queue before the final intersection. The simulation software package AnyLogic was used to model a simple two-lane intersection with and without light metering. It was found that light metering almost eliminates start-up delay by preventing a long queue to form in front of the modeled intersection. Shorter queue lengths and reduction in the start-up delays prevents cycle failure and significantly reduces the overall delay for the intersection. However, frequent deceleration and acceleration for a few of the cars occurs before each light meter. This solution significantly reduces the traffic density before the intersection and the overall delay but does not appear to be a better emission alternative due to an increase in acceleration. Further research would need to quantify the difference in emissions for this model compared to a standard intersection.
ContributorsGlavin, Erin (Author) / Pavlic, Theodore (Thesis director) / Sefair, Jorge (Committee member) / Industrial, Systems and Operations Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Every year, millions of guests visit theme parks internationally. Within that massive population, accidents and emergencies are bound to occur. Choosing the correct location for emergency responders inside of the park could mean the difference between life and death. In an effort to provide the utmost safety for the guests of a park, it is important to make the best decision when selecting the location for emergency response crews. A theme park is different from a regular residential or commercial area because the crowds and shows block certain routes, and they change throughout the day. We propose an optimization model that selects staging locations for emergency medical responders in a theme park to maximize the number of responses that can occur within a pre-specified time. The staging areas are selected from a candidate set of restricted access locations where the responders can store their equipment. Our solution approach considers all routes to access any park location, including areas that are unavailable to a regular guest. Theme parks are a highly dynamic environment. Because special events occurring in the park at certain hours (e.g., parades) might impact the responders' travel times, our model's decisions also include the time dimension in the location and re-location of the responders. Our solution provides the optimal location of the responders for each time partition, including backup responders. When an optimal solution is found, the model is also designed to consider alternate optimal solutions that provide a more balanced workload for the crews.
ContributorsLivingston, Noah Russell (Author) / Sefair, Jorge (Thesis director) / Askin, Ronald (Committee member) / Industrial, Systems and Operations Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
The stable and efficient operation of the transmission network is fundamental to the power system’s ability to deliver electricity reliably and cheaply. As average temperatures continue to rise, the ability of the transmission network to meet demand is diminished. Higher temperatures lead to congestion by reducing thermal limits of lines while simultaneously reducing generation potential. Furthermore, they contribute to the growing frequency and ferocity of devasting weather events. Due to prohibitive costs and limited real estate for building new lines, it is necessary to consider flexible investment options (e.g., transmission switching, capacity expansion, etc.) to improve the functionality and efficiency of the grid. Increased flexibility, however, requires many discrete choices, rendering fully accurate models intractable. This dissertation derives several classes of structural valid inequalities and employs them to accelerate the solution process for each of the proposed expansion planning problems. The valid inequalities leverage the variability of the cumulative capacity-reactance products of parallel simple paths in networks with flexible topology, such as those found in transmission expansion planning problems. Ongoing changes to the climate and weather will have vastly differing impacts a regional and local scale, yet these effects are difficult to predict. This dissertation models the long-term and short-term uncertainty of rising temperatures and severe weather events on transmission network components in both stochastic and robust mixed-integer linear programming frameworks. It develops a novel test case constructed from publicly available data on the Arizona transmission network. The models and test case are used to test the impacts of climate and weather on regional expansion decisions.
ContributorsSkolfield, Joshua Kyle (Author) / Escobedo, Adolfo R (Thesis advisor) / Sefair, Jorge (Committee member) / Mirchandani, Pitu (Committee member) / Hedman, Mojdeh (Committee member) / Arizona State University (Publisher)
Created2022
Description
The rank aggregation problem has ubiquitous applications in operations research, artificial intelligence, computational social choice, and various other fields. Generally, rank aggregation is utilized whenever a set of judges (human or non-human) express their preferences over a set of items, and it is necessary to find a consensus ranking that best represents these preferences collectively. Many real-world instances of this problem involve a very large number of items, include ties, and/or contain partial information, which brings a challenge to decision-makers. This work makes several contributions to overcoming these challenges. Most attention on this problem has focused on an NP-hard distance-based variant known as Kemeny aggregation, for which solution approaches with provable guarantees that can handle difficult large-scale instances remain elusive. Firstly, this work introduces exact and approximate methodologies inspired by the social choice foundations of the problem, namely the Condorcet criterion, to decompose the problem. To deal with instances where exact partitioning does not yield many subsets, it proposes Approximate Condorcet Partitioning, which is a scalable solution technique capable of handling large-scale instances while providing provable guarantees. Secondly, this work delves into the rank aggregation problem under the generalized Kendall-tau distance, which contains Kemeny aggregation as a special case. This new problem provides a robust and highly-flexible framework for handling ties. First, it derives exact and heuristic solution methods for the generalized problem. Second, it introduces a novel social choice property that encloses existing variations of the Condorcet criterion as special cases. Thirdly, this work focuses on top-k list aggregation. Top-k lists are a special form of item orderings wherein out of n total items only a small number of them, k, are explicitly ordered. Top-k lists are being increasingly utilized in various fields including recommendation systems, information retrieval, and machine learning. This work introduces exact and inexact methods for consolidating a collection of heterogeneous top- lists. Furthermore, the strength of the proposed exact formulations is analyzed from a polyhedral point of view. Finally, this work identifies the top-100 U.S. universities by consolidating four prominent university rankings to assess the computational implications of this problem.
ContributorsAkbari, Sina (Author) / Escobedo, Adolfo (Thesis advisor) / Byeon, Geunyeong (Committee member) / Sefair, Jorge (Committee member) / Wu, Shin-Yi (Committee member) / Arizona State University (Publisher)
Created2022
Description
The emerging multimodal mobility as a service (MaaS) and connected and automated mobility (CAM) are expected to improve individual travel experience and entire transportation system performance in various aspects, such as convenience, safety, and reliability. There have been extensive efforts in the literature devoted to enhancing existing and developing new methodologies and tools to investigate the impacts and potentials of CAM systems. Due to the hierarchical nature of CAM systems and associated intrinsic correlated human factors and physical infrastructures from various resolutions, simply considering components across different levels into a single model may be practically infeasible and computationally prohibitive in operation and decision stages. One of the greatest challenges in existing studies is to construct a theoretically sound and computationally efficient architecture such that CAM system modeling can be performed in an inherently consistent cross-resolution manner. This research aims to contribute to the modeling of CAM systems on layered transportation networks, with a special focus on the following three aspects: (1) layered CAM system architecture with a tight network and modeling consistency, in which different levels of tasks can be efficiently performed at dedicated layers; (2) cross-resolution traffic state estimation in CAM systems using heterogeneous observations; and (3) integrated city logistics operation optimization in CAM for improving system performance.
ContributorsLu, Jiawei (Author) / Zhou, Xuesong (Thesis advisor) / Pendyala, Ram (Committee member) / Xue, Guoliang (Committee member) / Mittelmann, Hans (Committee member) / Arizona State University (Publisher)
Created2022
Description
The process of learning a new skill can be time consuming and difficult for both the teacher and the student, especially when it comes to computer modeling. With so many terms and functionalities to familiarize oneself with, this task can be overwhelming to even the most knowledgeable student. The purpose of this paper is to describe the methodology used in the creation of a new set of curricula for those attempting to learn how to use the Dynamic Traffic Simulation Package with Multi-Resolution Modeling. The current DLSim curriculum currently relates information via high-concept terms and complicated graphics. The information in this paper aims to provide a streamlined set of curricula for new users of DLSim, including lesson plans and improved infographics.
ContributorsMills, Alexander (Author) / Zhou, Xuesong (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description
In this dissertation, a cyber-physical system called MIDAS (Managing Interacting Demand And Supply) has been developed, where the “supply” refers to the transportation infrastructure including traffic controls while the “demand” refers to its dynamic traffic loads. The strength of MIDAS lies in its ability to proactively control and manage mixed vehicular traffic, having various levels of autonomy, through traffic intersections. Using real-time traffic control algorithms MIDAS minimizes wait times, congestion, and travel times on existing roadways. For traffic engineers, efficient control of complicated traffic movements used at diamond interchanges (DI), which interface streets with freeways, is challenging for normal human driven vehicular traffic, let alone for communicationally-connected vehicles (CVs) due to stochastic demand and uncertainties. This dissertation first develops a proactive traffic control algorithm, MIDAS, using forward-recursion dynamic programming (DP), for scheduling large set of traffic movements of non-connected vehicles and CVs at the DIs, over a finite-time horizon. MIDAS captures measurements from fixed detectors and captures Lagrangian measurements from CVs, to estimate link travel times, arrival times and turning movements. Simulation study shows MIDAS’ outperforms (a) a current optimal state-of-art optimal fixed-cycle time control scheme, and (b) a state-of-art traffic adaptive cycle-free scheme.
Subsequently, this dissertation addresses the challenges of improving the road capacity by platooning fully autonomous vehicles (AVs), resulting in smaller headways and greater road utilization. With the MIDAS AI (Autonomous Intersection) control, an effective platooning strategy is developed, and optimal release sequence of AVs is determined using a new forward-recursive DP that minimizes the time-loss delays of AVs. MIDAS AI evaluates the DP decisions every second and communicates optimal actions to the AVs.
Although MIDAS AI’s exact DP achieves optimal solution in almost real-time compared to other exact algorithms, it suffers from scalability. To address this challenge, the dissertation then develops MIDAS RAIC (Reinforced Autonomous Intersection Control), a deep reinforcement learning based real-time dynamic traffic control system for AVs at an intersection. Simulation results show the proposed deep Q-learning architecture trains MIDAS RAIC to learn a near-optimal policy that minimizes the total cumulative time loss delay and performs nearly as well as the MIDAS AI.
ContributorsPotluri, Viswanath (Author) / Mirchandani, Pitu (Thesis advisor) / Ju, Feng (Committee member) / Zhou, Xuesong (Committee member) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2023
Description
Assembly lines are low-cost production systems that manufacture similar finished units in large quantities. Manufacturers utilize mixed-model assembly lines to produce customized items that are not identical but share some general features in response to consumer needs. To maintain efficiency, the aim is to find the best feasible option to balance the lines efficiently; allocating each task to a workstation to satisfy all restrictions and fulfill all operational requirements in such a way that the line has the highest performance and maximum throughput. The work to be done at each workstation and line depends on the precise product configuration and is not constant across all models. This research seeks to enhance the subject of assembly line balancing by establishing a model for creating the most efficient assembly system. Several realistic characteristics are included into efficient optimization techniques and mathematical models to provide a more comprehensive model for building assembly systems. This involves analyzing the learning growth by task, employing parallel line designs, and configuring mixed models structure under particular constraints and criteria. This dissertation covers a gap in the literature by utilizing some exact and approximation modeling approaches. These methods are based on mathematical programming techniques, including integer and mixed integer models and heuristics. In this dissertation, heuristic approximations are employed to address problem-solving challenges caused by the problem's combinatorial complexity. This study proposes a model that considers learning curve effects and dynamic demand. This is exemplified in instances of a new assembly line, new employees, introducing new products or simply implementing engineering change orders. To achieve a cost-based optimal solution, an integer mathematical formulation is proposed to minimize the production line's total cost under the impact of learning and demand fulfillment. The research further creates approaches to obtain a comprehensive model in the case of single and mixed models for parallel lines systems. Optimization models and heuristics are developed under various aspects, such as cycle times by line and tooling considerations. Numerous extensions are explored effectively to analyze the cost impact under certain constraints and implications. The implementation results demonstrate that the proposed models and heuristics provide valuable insights.
ContributorsAlhomaidi, Esam (Author) / Askin, Ronald G (Thesis advisor) / Yan, Hao (Committee member) / Iquebal, Ashif (Committee member) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2023