Master of Science in Engineering, University of Akron, 2013, Mechanical Engineering

Parking congestion has always been a problem which faces urban planners and cities governments across the United States. One area where this issue is extremely relevant is in the planning and expansion of urban colleges and universities which have a commuter population. This research contains a case study of the parking dynamics at the University of Akron in Akron, Ohio. Data will be collected which will provide the researcher with a better understanding of the parking dynamics at the university. Once the data is compiled it will be used to construct a discrete event simulation model which replicates the current state of the parking scenario at the university. Hypothetical scenarios will be run with the model once the current state model has been validated. The purpose of this research is to show the advantage of using discrete event simulation in parking analysis.

Committee: Shengyong Wang Dr. (Advisor); Daniel Deckler Dr. (Committee Member); Scott Sawyer Dr. (Committee Member); Jared Coleman Mr. (Committee Member) Subjects: Engineering; Urban Planning

MARCH, University of Cincinnati, 2023, Design, Architecture, Art and Planning: Architecture

Henry Ford's assembly line allowed for the mass production of automobiles. This advancement birthed a need for a new typology: the parking garage. The excitement over this new type of structure brought about an original building style that excited architects and designers. Evolution of the car's role in American society has shifted how parking garages should be designed, but unfortunately, no further progress in garage design has been made since garage ubiquity in the modern era of the 1950s. Parking garage organization shows little concern for the mingling of the pedestrian in relation to the car. In the urban environment, parking garages have become exclusively places to park, and act as an accessory to other urban generators, making them pieces of infrastructure, not architecture. Designers today should be concerned with transforming the current infrastructure to architecture by making the pedestrian experience personal and impactful while in the parking garage. The garage should be multi-programmed, encourage the pedestrian to linger, and make it known that the garage is a place for the pedestrian just as much as it is for the car. By understanding the necessary elements of parking garages, a strategy may be produced that demonstrates these ideals of bettering the human experience in parking garages. A prototype example of how to bring these ideas to life is expressed through this essay and a series of drawings, models, and diagrams. This information is used to convey the urgency of encouraging parking garages to go beyond the singular function of parking, while simultaneously stitching together the urban fabric in the eyes of the beholder. This prototype will help encourage architects, planners, policy makers, and designers to make better informed decisions for the enrichment of the pedestrian and urban fabric through the lens of parking garages.

Committee: Rebecca Williamson Ph.D. (Committee Member); Michael McInturf M.Arch. (Committee Chair) Subjects: Architecture

Master of Science (MS), Bowling Green State University, 1966, MBA

Committee: Warren C. Waterhouse (Advisor) Subjects: Higher Education

Master of Science (MS), Bowling Green State University, 1965, MBA

Committee: Maurice I. Mandell (Advisor) Subjects: Marketing

Doctor of Philosophy (PhD), Ohio University, 2020, Electrical Engineering & Computer Science (Engineering and Technology)

In this dissertation, the problem of trajectory design for autonomous wheeled ground vehicles are investigated. Several line-of-sight (LOS) based trajectory design approaches are developed to solve the problem in various practical scenarios. For path planning of on-road driving, a LOS pure pursuit guidance (PPG) path planner is designed. Stability analysis for LOS PPG along a general reference path is conducted based on Lyapunov stability theory. By using the theoretical analysis results, a design guideline for the selection of the guidance parameters is derived. The geometric interpretation of LOS PPG for general guidance parameters is provided. Then, for a given feasible, collision-free path, the problem of converting the geometric path to a time-parameterized trajectory is studied. A novel receding-horizon type sub-optimal path-to-trajectory conversion algorithm is developed which is able to take into account dynamic constraints and has high computational efficiency. For the problem of path planning for autonomous car-like ground vehicle parking, a novel four-phase path planning algorithm is developed. The algorithm is able to cope with various parking scenarios in a unified, scalable manner with low computational cost. The four-phase algorithm is extended to standard N-trailer parking and a novel cascade path planning algorithm is developed. Besides the advantages inherited from the four-phase algorithm, the cascade algorithm for standard N-trailer parking is able to prevent jackknife phenomenon.

Committee: Jim Zhu (Advisor); Douglas Lawrence (Committee Member); Robert Williams II (Committee Member); Frank Van Graas (Committee Member); Xiaoping Shen (Committee Member); Sergio Ulloa (Committee Member) Subjects: Automotive Engineering; Electrical Engineering; Engineering

Master of Science, University of Akron, 2014, Mechanical Engineering

In a university setting, parking service plays a critical role in complementing education. It is important to provide and manage university parking services that balance the resources of the university, market conditions, and the safety of all members of the campus community, and the convenience of the users. The complexity of the parking facility distribution, the dynamic nature of the traffic flows, and the randomness of the parking access times is indicative of both the challenge of this work as well as the urgency with which it is needed. Previous literature in parking system analytics has mainly focused on using proper parking capacity to user ratios to allocate parking resources. However, this approach doesn't offer much operational details in dealing and managing regional parking congestion issues, which are the major concerns in both the design and continuous improvement process of the parking system. This research, therefore, proposes to use a scientific based, data-driven approach to systematically model, validate, and simulate alternative parking strategies for optimal system redesign. Specifically, discrete event simulation model is used to capture the parking systems dynamics in granular details to enable deeper understanding and subsequent continuous improvement of the parking systems.

Committee: Shengyong Wang Dr. (Advisor); Jared Coleman Mr. (Committee Member); Sergio Felicelli Dr. (Committee Member) Subjects: Mechanical Engineering

Master of City and Regional Planning, The Ohio State University, 2006, City and Regional Planning

This study sought to discover the kinds of social behavior among pedestrians in a shopping center parking lot. A pilot study looked for social behaviors in three shopping center parking lots. Systematic unobtrusive observation revealed actual patterns of social behavior among moving and stationary pedestrians. Typical behaviors were noted and a coding sheet was developed for the final study at one parking lot. The kinds of social behavior observed included conversations, talking on cell phones, and playing. Many planners promote fostering social behavior in pedestrian environments. Some have argued the best places to enhance behavior are places where people attempt the behavior naturally. Planners disagree on whether parking lots should be promoted as civic spaces, and the question is open whether pedestrian-oriented site design can foster social behavior in shopping center parking lots.

Committee: Jack Nasar (Advisor); Jennifer Evans-Cowley (Other) Subjects:

Master of Science, The Ohio State University, 2024, Electrical and Computer Engineering

This thesis addresses the growing challenges of traffic and parking management at Ohio State University's Columbus campus due to increased student enrollment as well as interdisciplinary research center in west campus. By leveraging traffic data from CampusParc and the Campus Area Bus Service (CABS), a detailed traffic simulation model was developed using the Simulation of Urban Mobility (SUMO) platform, enhanced with the Traffic Control Interface (TraCI). The model is designed to replicate and analyze real-world traffic patterns on campus, with a specific focus on high-density road sections and peak congestion areas. The research objectives include the collection and analysis of traffic data, the creation of a high-fidelity traffic simulation, and its integration into a digital twin of the campus. The model's accuracy was validated by comparing simulated vehicle counts and parking occupancy levels with actual data, showing minimal error and confirming the model's reliability. This study also explores various mobility solutions such as optimized bus routes, smart traffic signals, and shared mobility services, assessing their impact on campus traffic flow. The integration of the simulation model into the digital twin framework provides a comprehensive view of campus mobility, aiding in effective traffic management and planning. The results of this research demonstrate the potential of the developed tools and methodologies to enhance campus transportation efficiency and sustainability. This work not only contributes to the immediate needs of OSU but also offers a scalable approach for urban mobility management in similar environments.

Committee: Shawn Midlam-Mohler (Advisor) Subjects: Computer Engineering; Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2023, Civil Engineering

The main objective of this study is to provide designers, manufacturers, and owners of new parking facilities with best practices and design choices considering lifecycle costs and extreme loading scenarios for several selected parking structures in Ohio. To achieve this overall goal, an interactive tool was developed using Python software to perform lifecycle cost analysis while considering various parameters like joint sealant, flange-to-flange connectors, and general repairs due to corrosion after environmental exposure. Also, snow load effects were investigated when a plow pushes all the uniform snow accumulated on the top of the roof slabs of thirteen parking garage structures to the corners or edges. Furthermore, the additional live load that could come from large numbers of driverless cars on cast-in-place and precast concrete parking structures was investigated. In this dissertation, a lifecycle assessment methodology is proposed for cast-in-place and precast concrete parking structures to identify and address durability and structural performance issues with the objective of answering these specific questions: (1) how to perform overall lifecycle assessment of parking structures, (2) how to perform performance assessment of double-tee beam flange-to-flange connections and joint leakage, and (3) how to investigate a parking structure's ability to carry unexpected loads. The author had access to design, repair, and maintenance data from several existing concrete parking structures. Historical maintenance and repair records were used to assess the impact of design changes to improve the durability and structural performance. An interactive tool is developed in Python software to perform lifecycle cost analysis considering various parameters including joint sealants, flange-to-flange connector, periodical damage repairs, and general maintenance due to environmental exposure. The new program also evaluates the fatigue stress conditions considering the design li (open full item for complete abstract)

Committee: Halil Sezen (Advisor); Abdollah Shafieezadeh (Committee Member); Jieun Hur (Committee Member) Subjects: Civil Engineering

Master of Science, University of Toledo, 2022, Civil Engineering

Two models are presented in the report, the first model is focused on computing the traffic flow characteristics of vehicles passing through a roadway section or an intersection. Collection of traffic data is essential for the planning, design, and operation of a safe and efficient transportation system. The proposed model utilizes Artificial Intelligence/ Computer Vision that detects vehicles and determines the flow rate and time headways between vehicles. AI based computer vision is a new technology popular for collecting and processing real time data, which is widely used in transportation research. To estimate the traffic flow at the macroscopic level we use YOLO v4 for detecting vehicles and Deep SORT for tracking vehicles. A vehicle count is registered when it crosses the Region of Interest, a virtual line defined by the user and that count is further used for computing the traffic flow rate and time headway. This part of research helps in efficient traffic control by developing a model that calculates traffic data which can be used for traffic analysis. The second model is created with the focus on contributing to the betterment of parking efficiency, especially in the urban areas. An artificial neural network model was trained in Machine Learning with a large amount of data sets of vehicles in parking lots in multiple orientations and time of the day, to improve the accuracy of the detection to the fullest. The developed model detects and counts the number of vehicles in the parking lot. The total count of vacant parking slots in the parking lot is then computed by calculating the difference between the number of vehicles and number of parking slots in the parking lot. This model is created with the aim of aiding drivers for an easy parking experience, as the data can be used to transfer to the drivers through a user interface or a mobile application that can help the drivers navigate to the right parking lot.

Committee: Dr.Eddie Chou (Committee Chair) Subjects: Civil Engineering

Doctor of Philosophy, The Ohio State University, 2021, Electrical and Computer Engineering

The issue of identifying available vacant parking spaces in a parking structure proves to be a prevailing challenge. Several methods implemented in parking structures across the nation have been successful in determining the available spaces and alerting drivers towards such spaces. These methods use overhead light waves and under the pavement pressure sensors to measure each parking space. Regardless of their success in providing an occupancy map, the cost of implementing and maintaining such sensors prove to be burdensome. On the other hand, autonomous guidance and navigation technology implemented in autonomous vehicles have been improving rapidly in the past decade. Autonomous vehicles are now capable of scanning, identifying, and avoiding obstacles. Hence, they are theoretically capable of recognizing available parking spaces and performing parking maneuvers. This study employs autonomous vehicles to map public utility spaces, particularly structured and semi-structured parking areas. The goal is to have an accurate and updated occupancy map of the parking structure. Each autonomous vehicle is assigned a specific zone in the parking structure to map and send the data to a map server. Here, a zone refers to a partition in the occupancy map containing the information about the number of available parking spaces. The autonomous vehicles periodically scan the zones assigned by the optimal allocation problem, resulting in a fully updated occupancy map. This work uses historical data to generate two dynamic models that predicts the time-varying number of available parking spaces. The two models used are a linear dynamic system model and a Gaussian Process Regression (GPR) model. However, the number of spaces must be known for individual zones within the map. Therefore, the probability of human drivers parking in each zone is calculated by utilizing the zone's “attractiveness score”. The score is a zone-dependent measure that captures its appeal to human drivers ba (open full item for complete abstract)

Committee: Mrinal Kumar (Advisor) Subjects: Electrical Engineering

Doctor of Philosophy, University of Akron, 2021, Civil Engineering

A leading cause of traffic congestion in urban cities nowadays is drivers' search for vacant parking spaces. This research investigates the effectiveness of utilizing autonomous vehicles (AV) in detecting vacant parking spaces at any parking facility and sharing the findings with beneficiaries. A theoretical mathematical model for detection and communication processes using AV technologies is proposed to improve parking management, reduce traffic congestion, advance city trafficking, and develop smart cities. This model identifies the Effective Detection Area (EDA) of AVs as the area that considers several detected spots and can fulfil the constraint requirements of detection factors (DF). It requires a specific number of AVs and consequently, for effective detection purposes, reserves certain parking spots for them. A simulation is conducted to validate its calculations, which thus results in signifying the EDA as three rows, two in front of the AV and one behind, and verifying the AV detection efficiency with about 150 detected parking spots. Communicating the detected information is essential to process it accurately. AVs communicate processed data using vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) connected through a wireless 5G system, dedicated short-range communication (DSRC), and a cellular network. Accordingly, AV drivers receive parking facility information directly, whereas ordinary vehicle drivers obtain it through a phone application, website or short message service (SMS). This study has several recommendations for future studies, including identifying the relationship between DA and the height of the LiDAR sensor, and investigating the effectiveness of including mobile AVs in the detection process.

Committee: Ping Yi (Advisor); David Roke (Committee Member); Qindan Huang (Committee Member); Yilmaz Sozer (Committee Member); Jun Ye (Committee Member) Subjects: Civil Engineering

Master of Science, The Ohio State University, 2020, Computer Science and Engineering

Parking availability information can help drivers make decisions on where to park, reduce road congestion, and balance the parking demand. This study utilizes the historic parking meter transactions to estimate the parking occupancy aggregated by parking zones in the Short North, Columbus. Daily and weekly recurring patterns are identified from the parking availability time series. Clustering algorithms are used to cluster the average weekly time series of each parking zone to identify similar parking patterns. And machine learning algorithms are trained to make predictions for each cluster of zones based on input features, including time of the day, day of the week, and month. The study compares different clustering algorithms and machine learning algorithms to choose the model with the best performance. Agglomerative clustering shows more solid outcomes than k-means clustering. Multilayer perceptron (MLP) with two hidden layers and 50 hidden units each layer outperforms other machine learning algorithms and MLPs with other parameters. This study shows the potential of using historical transaction data and machine learning algorithms to make parking availability predictions. The pipeline of data collection, data cleaning, data exploration, data transformation, feature engineering, model selection, modeling, and model evaluations in this study could be reproduced to apply to other areas.

Committee: Dave Ogle (Advisor); Thomas Bihari (Committee Member) Subjects: Computer Science

Master of Science in Engineering, Youngstown State University, 2018, Department of Civil/Environmental and Chemical Engineering

The Lincoln Parking Deck at Youngstown State University has been serving as one of the most important structures on campus since the 1970s. It is a multi-level parking deck consisting of two entries and exit points from Lincoln Ave and Arlington St. Due to aggressive environmental conditions in Northeast Ohio, this structure has undergone deterioration, which ranges from mild in some places to extreme in certain locations. This research delves into the potential decrease in the safety of the structure due to corrosion in reinforcement and other structural damages. Even after undergoing routine maintenance checks, the structure shows signs of corrosion within the embedded concrete, which are now visible from the outside. This research mainly focuses on detecting the causes of corrosion in reinforcement and suggesting remediation to overcome corrosion in both new and existing structures. Use of advanced non-destructive techniques, such as Ground Penetrating Radar and the Profoscope, has revealed crucial details that could help understand corrosion in various structures. Visual inspection also led to the discovery of corrosion on the deck which was not mentioned in the renovations. The data obtained from the structural drawings were incorporated into probabilistic equations to determine the present condition of the parking deck using corrosion modelling. A structural analysis was performed on the parking deck to evaluate the strength of the materials and the results obtained were found to be satisfactory. The results indicate the parking garage to be structurally adequate at the time of the experiments. Based on the findings, appropriate remedial measures were suggested to avoid further corrosion. Detecting corrosion and undertaking proper rehabilitation would help YSU on maintenance. It may also help commuters use the parking deck with ease and comfort. A considerable number of students and staff use the Lincoln Parking Deck daily. If the corrosion becomes severe, (open full item for complete abstract)

Committee: AKM Anwarul Islam PhD (Advisor); Richard. A. Deschenes PhD (Committee Member); Jai. K. Jung PhD (Committee Member) Subjects: Civil Engineering

Master of Science, University of Toledo, 2016, Electrical Engineering

This thesis demonstrates development of a complete suite of path planning, elevator scheduling and resource allocation algorithms to manage multiple concurrent requests, in real time and in a dynamic context, for storage and retrieval of vehicles loaded onto robotic carts for a robotic, fully-automated, multi-story and driving-free parking structure. The objective is to utilize, for parking, the available spaces across the floors of a parking structure that does not have any driving lanes at a much higher percentage rate which is greater than or equal to 80% in all cases while keeping the customer waiting times at minimum. Path search and planning employs the incremental informed search algorithm D* Lite with domain-specific heuristics, and the uninformed search algorithm Uniform Cost Search in a completely-automated framework. An optimization algorithm based on nested partitions and genetic algorithm is adapted for scheduling of a group of elevators in the multi-story parking structure environment. A small percentage of parking spots are reserved as “blank cells” to facilitate movement of roller beds carrying a vehicle to its storage or retrieval destination. Resource allocation and management is accomplished using statistical models employing queueing theory for structural resources such as blank cells and elevators while minimizing customer waiting time. Lower bounds on the number of elevators needed for a specific floor count and number of parking spaces per floor are derived using statistical modeling. Multiple vehicles are considered to be potentially moving from one parking space to another by roller bed pallets moving along tracks mounted on the surface of each storage cell. A software simulator based on multi-threaded Java code and unified modeling language was developed to perform empirical testing and validation of the performance of the proposed integration framework for the set of path search, elevator scheduling and resource management algorithm (open full item for complete abstract)

Committee: Gursel Serpen (Committee Chair); Kevin Xu (Committee Member); Ahmad Javaid (Committee Member) Subjects: Artificial Intelligence

MARCH, University of Cincinnati, 2016, Design, Architecture, Art and Planning: Architecture

Ever since the advent of the automobile, people started moving out to the suburbs to escape the dense and undesirable conditions of city life. The suburbs represented the American Dream; having your own house, white-picket-fenced yard, with a car and a couple of kids. In the United States, individualism and privacy are cherished qualities and the suburbs provide that. One setback to this dream was the nation would be heavily reliant on the personal automobile as a primary source of transportation. People were willing to commute between ten and thirty miles to and from work in order to have that American Dream; sprawl became the method for providing this type of lifestyle. Over the following decades as automotive technology was increasing and the symbolism of the car carried over into garage design; parking garages began to transform. As automobile design improved and vehicles became operable in all weather conditions; garages' concern to protect the automobile from the environment began to decrease, effecting parking garage design. Just as the car was an icon for freedom, people thought that parking garages should operate similarly. As the notion “to come and go” dominated, the concept of waiting for your vehicle via valet or elevator, clashed with that mindset. This led to self-park garages that have been dominant for the past half century. These garages have evolved into banal and sterile archetypes that only serve a function, and not how they interface with the context. The intent of this thesis is to propose a new perspective toward the design of parking garages within the urban context to a capacity that promotes urban growth. This proposal will make assessments on the drawbacks to urban parking garages as well as identify obstacles that impede urban development. The idea is by bringing these drawbacks to the foreground; solutions may be developed in a manner that not only improve qualities to both parking and urban progress, but also (open full item for complete abstract)

Committee: William Williams M.Arch. (Committee Chair); Udo Greinacher M.Arch. (Committee Member) Subjects: Architecture

Master of Science, The Ohio State University, 2016, Electrical and Computer Engineering

Accurate localization is essential to the safe and effective functioning of an autonomous vehicle. In an autonomous valet parking system, the vehicle must be able to estimate its position in the global coordinate frame in order to plan its path and avoid obstacles. Furthermore, precise localization information is necessary for feedback for the control algorithms governing both general parking lot navigation as well as various parking maneuvers. This thesis explores the application of a real-time LIDAR-based landmark sensing scheme combined with a popular simultaneous localization and mapping method known as FastSLAM. The sensing algorithm extracts vertical objects from a 3D Velodyne lidar scan by applying a connected components algorithm to a 2D occupancy grid that is built from the scan. These landmarks are associated robustly from frame to frame in FastSLAM, which is essentially a Rao-Blackwellized particle filter where each particle uses 2D Kalman Filters to estimate the positions of known landmarks. The localization algorithm is tested using data collected from driving and performing parking maneuvers in a typical parking lot. Simulated data is also generated to verify the algorithm and to test its ability to handle varying levels of sensor error and landmark density.

Committee: Umit Ozguner (Advisor); Andrea Serrani (Committee Member); Keith Redmill (Committee Member) Subjects: Electrical Engineering

Master of Science, University of Toledo, 2014, Civil Engineering

Recently, a pre-stressed spandrel L beam of a parking garage at Northwest Ohio failed. Upon visiting the site and inspecting the beam, it was illustrated that the beam had experienced a sudden, brittle failure. This was concluded due to the fact that there was only one dominant crack which had propagated through the depth of the beam at the mid-span. There was no sign of any other equivalently distributed micro-cracks, which would normally form in ductile structures prior to failure. In order to further investigate and understand the sudden failure of the pre-stressed spandrel L-shaped parking garage beam, it was decided to perform analytical and numerical analysis of the beam. Primary, hand calculations was performed by use of MathCAD to compute ultimate moment capacity and the cracking moment of the beam based on PCI and ACI 318 codes. According to ACI 318-11, if the ratio of ultimate moment capacity over cracking moment exceeds 1.2, then the structure should be ductile enough to undergo considerable deflection before failure. Moreover, in a ductile member, equivalently distributed cracks visible to naked eye would warn when the member's nominal strength is approached, so that immediate occupancy and safety regulations could be applied. Secondly, RESPONSE2000 was used for cross-sectional analysis of the beam and validation of hand calculation results by MathCAD in order to conduct the rest of the study in a more efficient, less time consuming manner. Moreover, a few damage scenarios have been proposed for the beam; in which, a number of strands have been considered fully corroded by removing them from the model. For each scenario, using RESPONSE2000, the ultimate moment capacity over cracking moment ratio have been calculated to figure out the exact number of corroded strands needed for the beam to experience brittle failure. Crack patterns throughout the length of the beam and the existence of a dominant macro-crack was also studied and validated. (open full item for complete abstract)

Committee: Douglas K. Nims (Committee Chair); Mark A. Pickett (Committee Member); Liangbo Hu (Committee Member) Subjects: Engineering

MS, University of Cincinnati, 2012, Engineering and Applied Science: Environmental Engineering

Traffic-related air pollutants are under great emphasis with the increasing traffic volume in recent years. In this study, traffic-related air pollutants are investigated at a busy urban intersection and a parking garage in Cincinnati, Ohio. These two settings are representative of typical inner-city environments with frequent and concentrated activities and thus substantial human exposure. Ambient concentrations of black carbon (BC), organic carbon (OC), elemental carbon (EC), carbon monoxide (CO), and ultrafine particles (UFP), were monitored and related to traffic patterns, volume, delay, and meteorology at the intersection in four seasons throughout years 2009-2011. The same air pollutant constituents also were characterized for a public parking garage during some special events. At the traffic intersection, concentrations of the air emissions were measured and the seasonal and diurnal trends of the emissions were observed. Air concentrations were highest during the fall season and lowest during winter. Air emission concentrations were found to be highest during the AM periods, compared with noon and PM periods, but no significant differences between the noon and PM sampling periods were found. In the measurement of light-absorbing carbon (EC or BC) in particular, two aethalometers consistently agreed with each other during the sampling periods. Concentrations of BC and EC were compared to investigate the agreement between two different monitoring methods, the continuous measurement and the intermittent (time-integrated filter) measurement. A consistent 30-35% increase was observed for BC over EC. Relationships between air emission concentrations and traffic and meteorology factors were investigated. Black carbon concentrations were found to be positively associated with the volume of heavy-duty vehicles in 85% of the measurements. Carbon monoxide and ultrafine particles were found to be positively associated with the volume of total traffic in about 70% of the (open full item for complete abstract)

Committee: Mingming Lu PhD (Committee Chair); Eileen Birch PhD (Committee Member); Jeffrey Yang PhD (Committee Member); Heng Wei PhD (Committee Member) Subjects: Environmental Engineering

MS, University of Cincinnati, 2010, Engineering and Applied Science: Environmental Engineering

Carbon monoxide levels were monitored at intersections, bus stops, in enclosed parking garages, and in vehicles. Variation in CO levels was then compared with traffic variables. The effect of traffic volume, traffic delay, site location, time of day and meteorological variables were investigated during ambient testing. Incoming and outgoing vehicle volume as well as the effect of the time of day were studied during garage testing. Finally CO variation with vehicle speed, acceleration, road grade and vehicle specific power (VSP), a variable that measures a vehicles engine load per unit mass were investigated during in vehicle tests. The type of vehicle, the surrounding environment and time of day were also considered. Two studies were performed at two different locations. One study was done in Singapore during the fall of 2009 and one in Cincinnati where tests were done from the winter to the summer of 2010. Similar tests were performed at both locations. Ambient monitoring in Singapore was performed around the NUS (National University of Singapore) campus at bus stops within the campus and around the perimeter of the campus. Ambient testing in Cincinnati was done during winter and spring time at a large intersection. An enclosed parking garage was studied at both locations as well as in vehicle tests. Singapore buses were studied while personal vehicles and city buses were studied in Cincinnati. Consistent correlations between CO and traffic counts were not seen for the most part at ambient testing sites. A 5 minute interval was used and test periods were typically 1 to 2 hours long. Bus delay at busy bus stops showed consistent positive correlations with CO at the Singapore site. Vehicle delay counted by hand at intersections showed a positive correlation in some cases but was not consistently over each test period. The most consistent pattern around CO concentrations was a peak just after an acceleration period of a traffic cycle (after a green light for an appro (open full item for complete abstract)

Committee: Mingming Lu PhD (Committee Chair); Anna Kelley BA (Committee Member); Heng Wei PhD (Committee Member); Timothy Keener PhD (Committee Member) Subjects: Environmental Engineering