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School of Civil and Environmental Engineering

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College of Engineering

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Georgia Water Resources Institute

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Georgia Transportation Institute

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Environmental Microbial Genomics Laboratory

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Publication Search Results

Now showing 1 - 10 of 48

Accessibility To Healthcare Via Public Transit: A Case Study Of The Atlanta Metropolitan Area

(Georgia Institute of Technology, 2023-12-12) Baral, Ivee

Access to transportation is one of the major social determinants of health (SDOH). Environmental conditions where people are born, live, learn, work, play, worship, and age have an impact on a variety of health, functioning, and quality-of-life outcomes and risks; these conditions are known as social determinants of health (American Hospital Association, 2023). For households without cars, public transportation is essential for accessing healthcare (Liu et al., 2022). Adequate public transportation can help ensure patients are able to attend their healthcare appointments as scheduled and decrease the number of missed appointments. On the other hand, a lack of public transit could disrupt health outcomes by leading to delayed diagnoses or exacerbating existing conditions (American Hospital Association, 2023). Due to varying socioeconomic factors such as race, ethnicity, and car ridership, different households have unequal access to healthcare, so transit is their only way of reaching healthcare facilities (Liu et al., 2022). This study will investigate the accessibility of healthcare in the Atlanta Metropolitan Area via MARTA bus routes to understand how accessible healthcare is for transit-dependent individuals. Transit dependency constitutes individuals who have limited access to other modes of transportation, such as those above 65, below 18, and people with disabilities (American Public Transit Association, 2017). The goal of this study is to identify the census tracts in the study area that have limited access to healthcare facilities via transit, especially for transit-dependent people. The study will focus on the MARTA bus routes rather than the MARTA rail, as there is greater reach through the bus network.
An Assessment of EV Adoption and Potential Growth under Evolving Techno-policy Scenarios

(Georgia Institute of Technology, 2023-05-22) Dai, Ziyi

Reaching net-zero carbon emissions by 2050 is an overarching goal for sustainable development in the United States. Electric Vehicles (EVs) are being developed to reduce energy consumption during on-road operations and have become the cornerstone of sustainable transportation systems. This study explores how has EV adoption increased over the past years and predicts how will the EV market continue to expand and penetrate in the future years using a case study with regional granular travel survey data. This study first conducted a comprehensive and detailed analysis of the historical EV ownership and use from the year 2000 to 2021 under the Puget Sound region, covering aspects of household demographics and travel pattern differences between EV-households and non-EV-households, as well as the trend identification and quantification of multiple factors in deciding EV adoption and use. Following evidence from the analysis a modeling framework was developed and experimented for future year EV growth prediction at both macroscale and microscale levels, the macroscale model forecasts the total number of EV sales in specific future years based on EV technology development, EV market production and supply, the existence of supporting policy and rebates program, as well as external environmental factors, then the microscale model identifies the candidate EV-purchasing households through the measurement of similarities between EV-households and non-EV-households, with the dynamics of the factor influence reflected in the rescaling of the weights while calculating the similarity. Following the model outputs multiple scenarios regarding technology, policy and market were designed and proposed for model sensitivity analysis, specifically, how are the outputs affected based on changes in different input components. The proposed research methodology will supplement the existing studies on EV expansion and penetration over time, and will specifically account for parameter-driven preference dynamics. The analysis results will provide substantial details on the identification of influential factors on EV adoption, while prediction results will also provide substantial research findings on understanding the future EV market and possible impacts and turbulences.
Metro Atlanta Northwest Corridor Commuter Survey Results - Assessing Express Lane Impacts on Increased Corridor Throughput

(Georgia Institute of Technology, 2023-05-02) Morgan, William Taylor

In 2022, a survey was developed to try to gain insight into why a significant increase in morning peak traffic volumes was observed on the I-75/I-575 Northwest Corridor (NWC) in the Atlanta metropolitan area after the opening of the Express Lanes. It seemed unlikely that the increase was due to induced demand (increased total vehicle miles traveled (VMT) that was suppressed due to congestion), as most morning peak trips are generally mandatory trips such as work trips, school trips, and daycare trips. The previous research team suspected that the increase may have come from a diversion of commute traffic from arterials onto the freeway corridor, or from a shift of traffic from the shoulders of the peak to the center of the peak once the Express Lanes opened and congestion declined. Where the significant increase in traffic volumes on the NWC came from is an important question, especially for transportation planners, because it helps decision-makers get a better understanding of what the effects of opening new managed lane capacity along a corridor might be on traffic patterns around that corridor. Survey invitations were distributed through two different channels: email invitations and postcard invitations. Email invitations got a response rate of 3.26% and postcard invitations got a response rate of 2.88%. It was found that 53.4% of the observed 35% increase in vehicle throughput came from sources that would not be expected to increase total VMT (39.7% from route reassignment and 13.7% from departure time of day shift). The other 46.5% of the observed increase in vehicle throughput came from sources that could increase total VMT (1.7% from changes in mode split, 5.7% from trip redistribution, and the remaining 39.1% from trip generation). These numbers assume that the sample of respondents is representative of the NWC service area, which may not be the case. Additionally, the behavior of the relatively large number of Express Lanes users is not likely to be representative of the corridor users overall. In future work, this should be checked and adjusted for. These numbers also fail to capture non-commuters who may have used the NWC in the morning peak hours, which may be a source of error causing trip generation to be overestimated. Assuming that the induced demand numbers presented herein are correct, after the opening of the NWC Express Lanes, total VMT in the corridor’s service area may have increased by 16.3%. One method that many agencies and governments are using to try and reduce total VMT is paying for incentives to encourage carpooling. This survey’s findings that 49.0% of adult carpools are fampools (carpools consisting of only family members, who are likely to have carpooled anyway without the incentive) confirm others’ findings that a substantial proportion of carpools are fampools on urban highways (Poole and Balaker, 2005; Li et al, 2007). Induced demand generating an increase in total VMT is of particular concern to transportation planners because it reduces the congestion reduction benefit that can result from increased road capacity and it increases external costs (Litman, 2001), such as parking demand, uncompensated crash damages, and environmental impacts (such as emissions and pollution). Induced demand is something that transportation planners should investigate when considering new transportation infrastructure or substantial expansions to existing transportation infrastructure.
Distributive justice impact assessment using activity-based modeling with path retention

(Georgia Institute of Technology, 2021-01-25) Zhao, Yingping

Sustainable transportation policies seek to change people’s travel behavior by modifying the travel environment. However, the impacts of many such policies are complex and difficult to evaluate. Policy benefits and burdens may take the form of changes in transportation costs, changes in travel time, changes in energy or pollutant burden, etc. In traditional equity assessments, the distribution of costs and benefits across traditional demographic groups, such as low-income household, minority groups, individuals that do not or cannot own personal vehicles, etc. are often considered. Being able to provide an unbiased assessment of how these benefits and burdens are likely to be distributed across demographic groups of interest serves as the starting point for distributive justice (and environmental justice) analysis in transportation planning and policy assessment. The research objectives of this dissertation are to develop a modeling framework within an advanced activity-based travel demand model that can be used to assess the distribution of transportation cost and benefits across communities of interest and implement a data structure and variable tracking system that can be implemented within and across various modeling tools to ensure that data needed for equity assessment transfer between models, and that outputs from combined models can be used to assess equity impacts. The findings are that the ABM with path retention can be used to assess the distributive impact in terms of benefits and costs such as mobility, energy use, and emissions. Demographic groups of any cut can be assessed and compared effectively using the framework (provided that the travel demand model carries the required demographic variables), providing policy makes more valuable information before decision making especially for potential equity assessment. The framework can also be linked to other models such as dispersion models for other distributive equity impacts assessment in the future.
Large-Scale, Dynamic, Microscopic Simulation for Region-Wide Line Source Dispersion Modeling

(Georgia Institute of Technology, 2020-05-05) Kim, Daejin

Although a variety of modeling tools have been developed to predict potential public exposure to harmful transportation emissions at regional and sub-regional scales, computational efficiency remains a critical concern in the design of modeling tools. Microscale dispersion models run at high resolution and require extremely long runtimes for larger roadway networks and high-resolution receptor grids. Motivated by the challenges encountered in the previous modeling efforts, this work develops an advanced modeling framework for region-wide applications of line source dispersion models that integrates a high-performance emission rate lookup system (i.e., MOVES-Matrix), link screening, and innovative receptor site selection routines to further accelerate model implementation within distributed computing modeling framework. The case study in the 20-county metropolitan Atlanta area accounts for an extremely large number of link-receptor pairs demonstrates that the modeling system generates comparable concentration estimates to extremely-high-resolution processes, but with very high computational efficiency. The comprehensive modeling methodology presented in this work will make comparison of air quality impacts across complex project scenarios (and transportation development alternatives over large geographic areas) much more feasible. All these aspects should be of interest to a broad readership engaged in near-road air quality modeling for transportation planning and air quality conformity and for environmental analysis under the National Environmental Policy Act.
Next generation electric vehicle energy modeling in transportation networks

(Georgia Institute of Technology, 2019-11-12) Xu, Xiaodan

Electric vehicles (EVs) will play a central role in future energy-efficient and sustainable transportation systems. Predicting the energy use for EVs is a complex issue because the onboard vehicle systems are trying to balance the provision of power to the wheels as well as manage the state of charge (SOC) of the battery pack. Traditional modeling methodologies for estimating real-world EV energy consumption either depend on numerical analysis of laboratory or on-road vehicle test data or the use of full-system EV simulation tools. Unfortunately, full-system simulation tools suffer from scaling problems in the context of large transportation network, necessitating the development of approaches that supports large transportation network projections of modal EV operations and applicable energy use rates. This study introduces an activity-based, bottom-up modeling approach to estimate EV energy consumption under the expected range of on-road operating conditions. The proposed system integrates outputs from a full EV simulation model called Autonomie. Three analytical efforts were undertaken to develop the activity-based approach for EV fleets using Autonomie simulation outputs. First, a sample of EV technologies was configured in Autonomie, and various operating conditions were simulated in Autonomie to generate a library for on-road operations by technology. Second, a grey box model design, referred to a Bayesian Network method in this study, was used to develop energy consumption models for the variety of EV technologies. This approach combines vehicle performance knowledge and data-driven energy inferences with on-road vehicle operation as inputs. Finally, the proposed EV energy models were verified using a separate testing dataset developed from Autonomie simulation results of another set of driving profiles. In addition, the real-world observed operation and energy use data were collected from select EV models using on-board diagnostic (OBD) devices to verify the energy prediction from the proposed model. The verification results suggested that the proposed model can predict energy use patterns under most driving conditions. The proposed approach was applied at aggregated-level, to a regional-level network, and at individual-level, to households and persons traveling within a region. The scalability of the proposed energy model framework was demonstrated in an Atlanta, GA case study. The results demonstrated that if 6.2% of urban VMT and 4.9% of rural VMT are driven by EVs, the network-level fuel savings are around 4.0% for a normal travel day in 2024. The energy model was also applied to daily trips predicted by the regional travel demand model. The results suggest the actual benefits of EV adoption depends on household travel patterns across deployment scenarios, as well as charger availability, electricity and fuel cost, and ambient environment conditions.
A framework for optimizing public transit fleet conversion to alternative fuels

(Georgia Institute of Technology, 2019-09-03) Li, Hanyan

Alternative fuel buses have great potential to reduce life-cycle energy use and criteria pollutant emissions from urban and rural transit fleets. However, market penetration of alternative fuel transit buses in the U.S. is currently below 50%. There are a number of barriers that discourages switching from traditional diesel vehicles. This study develops an analytical framework to automatically generate optimal plans for fleet operations and conversion based on fleet-specific characteristics. The goal of the framework is to allow agencies to minimize the fleet-wide energy use, life-cycle CO2 emissions, and/or economic costs when transitioning to alternative fuels. Energy use are modeled using advanced tools based on real-world second-by-second operations data. Machine learning models are developed by using vehicle-specific and operating-related features, and then applied to assess the fleet-wide energy use. Cost changes of adopting alternative fuel vehicles are evaluated comprehensively. The framework includes three sets of optimization models, focusing on optimizing existing fleet operations, fleet electrification, and long-term fleet conversion. Decisions are optimized for annual fleet purchase/replacement of different vehicle types, vehicle-route assignment, vehicle-depot assignment, charging scheduling, and/or charging station/depot location selection. The proposed models are applied to the bus fleet from Metropolitan Atlanta Rapid Transit Authority (MARTA), the largest transit agency in Metro Atlanta.
Economic and social sustainability of sidewalk infrastructure

(Georgia Institute of Technology, 2019-08-27) Patel, Deep

The presence of sidewalks and quality of sidewalk infrastructure are important indicators of perceived pedestrian safety and the walkability of neighborhoods. However, a wide gap exists between the accessibility and quality of infrastructure provided for pedestrians compared to the infrastructure provided for motorized vehicles. While there may be numerous reasons for poor quality of pedestrian infrastructure across cities and neighborhoods, one of the main reasons is the lack of sustained operation and maintenance programs among these local government agencies. This study outlines an approach to quantify sidewalk infrastructure costs over an 80-year life cycle period. Equivalent annual costs for three different scenarios are allocated in part directly to property owners, with the remaining costs in each scenario recovered over time through an equivalent increase in property tax millage rates. The four sidewalk management scenarios are then examined in more detail to assess how implementation may differentially impact Atlanta’s 244 neighborhoods and their residents across income and ethnicity groups. The two somewhat surprising findings of the study are: 1) even though sidewalk infrastructure may have a lifespan of more than 40-years, the costs of owning and operating this infrastructure over an 80-year period with replacement are high; and 2) low income neighborhoods are negatively impacted when portions of sidewalk infrastructure management costs are allocated directly to property owners, rather than handling sustainable management through traditional property tax assessment methods.
Estimating managed lanes door-to-door travel timesavings using shortest path algorithms

(Georgia Institute of Technology, 2019-08-27) Chang, Chia-Huai

Implementing managed lanes, such as high-occupancy toll lanes, within existing urban highway corridors has become increasingly common in cities that want to provide a reliable transportation option but lack sufficient right-of-way to construct new corridors. This study develops a framework that utilizes a shortest path algorithm to compare before and after commute routes and estimate the change in door-to-door travel time offered by managed lane facilities. Using this modeling approach, a case study is explored for the Northwest Corridor (NWC) managed lane facility located in the Atlanta, Georgia, region. The shortest path routines predict that the facility provides a 21.0% - 27.1% decrease in door-to-door travel time for the NWC managed lane users, and a 5.8% – 12.0% travel time decrease for non-NWC general-purpose lane users, for corridor travelers departing home between 6:30 and 8:30 A.M. (traversing the corridor between 6:30 A.M. and 10:00 A.M.). This framework can be easily customized and applied to any other commute route/time change assessment for major managed lane projects.
Sidewalk asset management: Financial modeling the total cost of sidewalks

(Georgia Institute of Technology, 2018-12-07) Boyer, David

Agencies are beginning to recognize transportation asset management benefits, either from starting programs by choice or because of federal and state requirements. Managing transportation facilities as assets helps reduce the total cost of ownership by funding and programming routine, preventative, and corrective maintenance through a facility’s lifecycle. This process also leads to increased benefits returned to stakeholders. Pedestrian infrastructure lacks such a centralized management practice, for both funding and maintenance. This research identifies pedestrian infrastructure elements as assets and defines construction costs, common issues, repair costs, and maintenance practices. Element costs are used to create a model estimating the total cost of ownership for pedestrian infrastructure. Next, various funding pedestrian infrastructure practices and sources are discussed. Finally, the total cost of ownership model is applied in a case study that investigates the implications of adequate centralized funding based on available funding practices.