Case Study Of Austin Research Articles

Uncovering electric vehicle ownership disparities using K-means clustering analysis: A case study of Austin, Texas

Uncovering electric vehicle ownership disparities using K-means clustering analysis: A case study of Austin, Texas

Comparing Machine Learning and Time Series Approaches in Predictive Modeling of Urban Fire Incidents: A Case Study of Austin, Texas

This study examines urban fire incidents in Austin, Texas using machine learning (Random Forest) and time series (Autoregressive integrated moving average, ARIMA) methods for predictive modeling. Based on a dataset from the City of Austin Fire Department, it addresses the effectiveness of these models in predicting fire occurrences and the influence of fire types and urban district characteristics on predictions. The findings indicate that ARIMA models generally excel in predicting most fire types, except for auto fires. Additionally, the results highlight the significant differences in model performance across urban districts, indicating an impact of local features on fire incidence prediction. The research offers insights into temporal patterns of specific fire types, which can provide useful input to urban planning and public safety strategies in rapidly developing cities. In addition, the findings also emphasize the need for tailored predictive models, based on local dynamics and the distinct nature of fire incidents.

Adaptation of SWAT Watershed Model for Stormwater Management in Urban Catchments: Case Study in Austin, Texas

Computer simulation models are a useful tool in planning, enabling reliable yet affordable what-if scenario analysis. Many simulation models have been proposed and used for urban planning and management. Still, there are a few modeling options available for the purpose of evaluating the effects of various stormwater control measures (SCM), including LID (low-impact development) controls (green roof, rain garden, porous pavement, rainwater harvesting), upland off-line controls (sedimentation, filtration, retention–irrigation) and online controls (detention, wet pond). We explored the utility and potential of the Soil and Water Assessment Tool (SWAT) as a modeling tool for urban stormwater planning and management. This study demonstrates how the hydrologic modeling strategies of SWAT and recent enhancements could help to develop efficient measures for solving urban stormwater issues. The case studies presented in this paper focus on urban watersheds in the City of Austin (COA), TX, where rapid urbanization and population growth have put pressure on the urban stormwater system. Using the enhanced SWAT, COA developed a framework to assess the impacts on erosion, flooding, and aquatic life due to changes in runoff characteristics associated with land use changes. Five catchments in Austin were modeled to test the validity of the SWAT enhancements and the analytical framework. These case studies demonstrate the efficacy of using SWAT and the COA framework to evaluate the impacts of changes in hydrology and the effects of different regulatory schemes.

Community gardens as commons through the lens of the diverse economies framework: A case study of Austin, TX

Community gardening involves physical transformation of land, which then promotes community-identity formation and the production of place. This is a case study of community gardens in Austin, Texas (USA) as different types of commons with a focus on the Diverse Economies framework's principles of the successful governance and management. This paper argues that community gardens represent biophysical, cultural, social, and knowledge commons, therefore, their success depends on the social value they provide in addition to their physical yield. This qualitative research uses key-informant interviews with community gardens' representatives to draw evidence that: 1) community gardens represent different types of commons and 2) the governance approaches and management of community gardens include the principles of ‘ethical action’ described by the Diverse Economies framework. This analysis shows that 18 out of 26 community gardens in Austin follow all six principles of ethical action, and the degree of use of these principles depends on the garden's purposes and values, socioeconomic profile of its members, and its management structure. The results also shows that community gardens in Austin appear to be less aware or less concern with the ecological services related to gardening.

Modeling Dockless Shared E-Scooter Demand by Time of Day: A Case Study of Austin

The goal of the current study is to identify and quantify the influence of various contributing factors on dockless e-scooter demand. Drawing on high-resolution e-scooter trip level data for 2019 from Austin, Texas, we develop census tract (CT) level demand data for four time periods of the day. The time-period specific data is partitioned for weekdays and weekends. Using the prepared datasets, we develop a joint panel linear regression (JPLR) model framework that accommodates for the influence of unobserved factors at multiple levels-CT, month, day, and time period levels. The analysis results indicate that the proposed JPLR models outperform the independent linear regression models for both weekdays and weekends. The results also manifest a significant association between e-scooter demand and several independent variables including sociodemographic attributes, transportation infrastructure variables, land use and built environment variables, meteorological attributes, and situational attributes. Further, several panel-specific correlation effects are found to be significant across four dimensions highlighting the importance of accommodating the influence of common unobserved factors on e-scooter demand across different time-of-day dimensions. The model validation exercise results revealed that the proposed models performed well compared to the independent models. Finally, the estimated models are employed to conduct a policy exercise illustrating the value of the estimated models for understanding CT level e-scooter demand on weekdays and weekends. The results indicate that land use mix, proportion of commuters, and season are some of the most influential factors for e-scooter demand.

Assessing the Impacts of Air Quality Alerts on Micromobility Transportation Usage Behaviors

To address the severe risks imposed by air pollution, governments around the world have prioritized air quality information disclosure and alert dissemination with a goal to evoke awareness and ultimately encourage behavior changes. Daily transportation behavior not only contributes to air pollution formation but also impacts personal exposure. Previous studies have identified mixed results about the effectiveness of air quality alerts in encouraging transportation behavioral changes. However, little is known about the impacts on micromobility usage, a newly introduced transportation mode, which directly exposes the riders to the ambient environment and usually takes place in heavy traffic areas. In this study, we conducted a case study in Austin, TX, analyzing over 6.9 million trips collected between April 2018 and September 2019. A Poisson multivariate regression model was applied to assess the relationship between air quality alerts and usage of micromobility vehicles. The results indicate that air quality alerts in the form of Ozone Action Days do not alter micromobility usage behavior, while the public does significantly change their usage behaviors in response to actual ambient air quality for short duration trips. Numbers of longer distance micromobility trips were not found to be sensitive to actual air quality. The findings of this study have important implications for policymakers: government agencies should carefully consider timing, accuracy, and message clarity when delivering air quality information to the public.

AWWA Water Science Author Spotlight

Having recently published an article in AWWA Water Science, Hongfang Lu answered questions from the publication's editor-in-chief, Kenneth L. Mercer, about the research. A Hybrid Model for Monthly Water Demand Prediction: A Case Study of Austin, Texas Hongfang Lu, John Matthews, and Shuai Han I am now an associate professor at Southeast University in China. My current research focuses on trenchless technology and buried pipeline health monitoring. My research has been going on for nearly a decade, and my projects are all about pipelines. Since I have a background in oil and gas engineering, my initial research was on oil and gas pipelines. During my PhD program, I also did some research on water pipelines. During my PhD program, I was involved in a Water Research Foundation project on water pipeline condition assessment. During that time, I did some research on water. In addition, trenchless technology is more widely used in the water field. During his summer vacation in 2018, Hongfang went to New York City with one of his teachers and her family. So far, three teachers have greatly influenced my career. In chronological order, the first one is Professor Xiaonan Wu from Southwest Petroleum University (Chengdu, China), who was my tutor at the master's degree stage. She told me to have the courage to challenge complex problems in research. The second is my PhD research advisor, Dr. Tom Iseley, a professor at Purdue University (West Lafayette, Ind.) and a leader in the trenchless industry. He told me to keep an open mind in research and be mindful of the intersections between disciplines. The third is my current team leader, Professor Zhao-Dong Xu from Southeast University. He often reminds us to combine theory with practice and do research that is meaningful to people. This research is related to water demand prediction using machine learning methods. The motivation is to establish a hydraulic calculation model of the water supply network through the prediction of water demand. Many buried pipelines worldwide are aging or about to enter the aging stage. Health monitoring and trenchless repair of pipelines are essential; therefore, my research direction is related to these two points. Personally, I am very interested in the structure of pipes. Although pipeline structure is relatively simple compared with other forms, it also involves many scientific problems. The outbreak of COVID-19 in 2019 has triggered a lot of thinking among scholars. But even before the onset of its public health problems, using limited samples to predict future conditions and then using those results to take public measures was an important challenge in research. In June 2021, Hongfang participated in the biennial badminton competition at Southeast University, Nanjing, China. I was born in Nanyang, Henan Province, China, a place with abundant oil resources at that time. So I have been relatively familiar with oil since I was a child. When I was in college, my major was oil and gas storage and transportation engineering. In studying, I was very interested in pipelines, the vehicle for oil and gas resource transportation. To learn more about Hongfang's research, visit the article, available online at https://doi.org/10.1002/aws2.1175.

Who loses and who wins in the ride-hailing era? A case study of Austin, Texas

Ride-hailing has redefined vehicle access and has the potential to reduce travel difficulties for transit-poor areas and people with poor access to private and public transportation resources. Due to the lack of data, current studies lack a holistic understanding of how transportation resources serve different social groups and places in the ride-hailing era in low-density areas. This study uses multiple sources of data in Austin, Texas to understand: (1) how ride-hailing usage, transit supply, and vehicle ownership distribute across neighborhoods with different densities, income, and racial and ethnic compositions; (2) who are ride-hailing users among those with and without private vehicles, and how their ride-hailing usage and attitudes towards ride-hailing versus transit differ. Our study has shown that the ride-hailing services have provided residents living in low-income, low-density neighborhoods, and neighborhoods with a majority of Hispanics with an alternative transportation mode. However, residents living in low-density and low-income neighborhoods still use ride-hailing services less frequently than those living in high-density and high-income neighborhoods. The user survey further shows that ride-hailing users without private vehicles tend to be racial and minorities or younger people with higher education attainments. Ride-hailing services provide people with a convenient and safe transportation mode, regardless of their vehicle ownership. Our study shows the importance of ride-hailing in mitigating the social and spatial disparity and the opportunity of integrating ride-hailing and transit in transportation planning in low-density areas.

Modeling and Mapping Flood Hazard with a Flood Risk Assessment Tool: A Case Study of Austin, Texas

As a hazard, flood is an extremely important indicator of how a city is resilient to waterborne diseases and epidemics. Over many decades, flood as a hazard has been a major factor in inducing displacement of marginalized section of the people. Austin city within Central Texas has been identified as one of the major hotspots for flooding in recent decades. Thus, the objectives of the paper are two folded: 1) Empirically, we analyzed and mapped out the susceptibility levels from the factors of physical environments to assess the risk of urban flooding (rainfall data, surface water bodies and topography); in Austin, Texas and 2) Methodologically, we created a re-useable ArcGIS scripting tool that can be used by researchers to automate the process of flood risk modelling with certain criteria. The paper showcases a novel time sensitive building of a tool which will enable better visibility of flood within the city of Austin.

Impacts of shared automated vehicles on airport access and operations, with opportunities for revenue recovery: Case Study of Austin, Texas

With rising use of ridesourcing apps and, eventually, self-driving vehicles, demands for airport parking spaces, rental cars, and, ultimately, air travel is expected to fall everywhere, relative to background trends. This study uses publicly available ridesourcing demand data for the Austin, Texas area to pursue simulations of a fleet of shared autonomous vehicles or “SAVs”, and quantify airport revenue and operations impacts. Results suggest that dynamic ride-sharing (DRS) of centrally-dispatched vehicles across strangers (with current travel patterns) may reduce airport-related ground travel by up to 30% while reducing airport-access revenues by 46%, assuming ridesourcing permits continue to be charged $2 per trip. A time-varying zone-based toll around the airport can help offset lost parking and car-rental (but not seat-mile) revenues and potentially double present-day airport-access revenues. Such policies can come at the cost of adding non-revenue ridesourcing and SAV miles to the rest of the network, when incentivizing SAVs to leave the airport zone after a dropoff, in order to avoid curbside congestion. A combination of DRS and use of access fees on all commercial vehicles dropping off or picking up travelers can achieve a useful middle ground.

Impact of Transportation Network Companies on Ground Access to Airports: A Case Study in Austin, Texas

This study proposes using intelligent transportation systems (ITS) and open-data sources to evaluate the impact of transportation network companies (TNCs) on ground access to airports. The unexpected interruption of the TNCs services in Austin, Texas, U.S., in 2016, is used as a natural experiment to provide a before-and-after analysis of the changes in the traffic conditions of the access area to the Austin-Bergstrom International Airport (ABIA). An analysis of variance (ANOVA) is implemented to determine whether the difference in speeds across periods is statistically significant, and the value of time for TNC-induced delay is estimated, using values of passengers’ willingness to pay for airport access travel time savings. Furthermore, a speed linear model is developed to assess the impact of TNC demand on ground access areas using trip information from an Austin-based TNC service. The main results suggest that airport ground access speeds were higher during the period that the TNCs were out of the city. The re-introduction of the services resulted in a speed reduction of 9% for the airport morning and 18% for the afternoon peak hours, translating to a total passenger cost of approximately $150+ (morning) and $400+ (afternoon) per hour. Furthermore, it was found that the number of TNC pick-up trips is a predictor of airport access speed and that the flight schedule can potentially be used to develop predictive speed models.

The Impact of Homelessness in Social Vulnerability Assessment: A Case Study of Austin, Texas

Among existing research on social vulnerability, virtually no studies have considered homelessness as a variable in their vulnerability assessments. This study identified the relevance of homelessness as a key index in social vulnerability assessment to inform the public, policymakers and the broader body of literature of its impacts on shaping vulnerability patterns in cities. In this study, the 2018 Homeless data for Austin was disaggregated from the council district level to block group level using dasymetric mapping in Geographic Information System (GIS). Principal Component Analysis (PCA) was used to group highly correlated demographic and socioeconomic variables into factors, which were normalized and summed to model social vulnerability with homeless index (SOVI_H) and without homeless index (SOVI) for each Austin Blockgroup. The result revealed significant differences in geographic patterns between SOVI_H and SOVI. SOVI_H showed hotspots of vulnerabilities in Downtown and East-Austin neighborhoods, depicting a slight shift of social vulnerability westwards of the city. This finding differs from past results of social vulnerabilities in Austin where it used to be predominant in the East. This study showed that incorporating homelessness in identifying social vulnerability can help researchers and other associated organizations identify the most vulnerable groups when conducting social vulnerability assessments.

Simulation of green roofs and their potential mitigating effects on the urban heat island using an artificial neural network: A case study in Austin, Texas

The growing number of densely populated cities has resulted in a decrease in vegetation coverage, which in turn, has resulted in a temperature increase in urban areas. This phenomenon is known as urban heat island (UHI). Several strategies have been proposed to mitigate the effect of urban heat islands in recent years, including usage of green roof coverage. However, there is a need for a rigorous analysis of the relationship between UHI and different urban characteristics using advanced models for urban planners to make policy-decisions to mitigate the UHI effect. In this study, the cooling effect of the green roof strategy in the city of Austin, considering 2D/3D urban characteristic parameters was investigated. To begin with, land surface temperature (LST) was estimated from Landsat 8 TIRS data in July 2016. Also, 3D urban morphology parameters derived from light detection and ranging (LiDAR) data. To simulate the green roof strategy, Sentinel 2A satellite images were used to calculate the normalized difference vegetation index and the normalized difference built-up index because of higher spatial resolution than Landsat 8 OLI. Then a multilayer feed-forward neural network was applied as a nonlinear model to find a relationship between LST and various urban characteristic parameters simultaneously. Furthermore, the importance of the variables in LST modeling was evaluated using sensitivity analysis. After that, some downtown residential and office buildings, which had the potential to become a green roof, were selected to implement the green roof strategy. Finally, by analyzing the relationship between LST reduction due to green roof simulation and urban indicators, the best buildings for green roof implementation were determined. Results showed that the accuracy of the LST modeling was reached to R2 = 0.786 and RMSE = 0.956 °C. In addition, by greening 3.2% of total building roofs, the average of LST decreased by 1.96 °C. Moreover, the results indicated that the building green roofs with (i) heights of 15–25 m, (ii) the highest values of sky view factor and solar radiation, and (iii) the lowest distance to the water body, had the greatest cooling effects on LST. Consequently, these findings indicated that the green roof has a significant effect on temperature reduction, especially by selecting the buildings with the above mentioned characteristics.

Characterizing the 3-D urban morphology transformation to understand urban-form dynamics: A case study of Austin, Texas, USA

Since the 1990s, “new urbanism” and “smart growth” have been advocated in the United States, which promote compact urban forms to cope with the environmental and ecological problems caused by urban sprawl. However, there is no uniformity in terms of the spatiotemporal characterization of urban forms. Lack of detailed 3-D urban landscape information makes it difficult to evaluate urbanization trajectories. In this study, we focused on Austin, Texas, U.S., an area with the highest urbanization and the best ranked standard of living, to characterize its evolution of urban morphology and the transformations between urban morphology types (UMTs) over time. We first proposed a conceptual framework to study the UMT transition and urbanization process. Then, the UMT in 2006, 2011 and 2016 was mapped using a synthesis of data sources, which enables a comprehensive understanding of the urbanization process with analytical methods, considering “intensification,” “sprawling expansion,” and “efficient expansion.” Results indicate that Lidar facilitates the multidimensional characterization of urban morphology and its transformation between 2006 and 2016, as maps of UMT were in conformity with the building features of the study area. The urban expansion analysis in different periods (1992–2001, 2001–2006, 2006–2011, and 2011–2016) showed that there has been considerable “intensification” and “efficient expansion” after 2000, indicating the increasingly important role of compact development. In spite of the plans and policies on urban development since the 2000s, we found that there was more sprawl than intensification and efficient expansion. This study demonstrates the advantage of applying Lidar data to characterize urban 3-D morphology and understand its dynamics, which helps develop a comprehensive understanding of the urbanization process and provides a tool for planning intentions and policies evaluation on urban development over time.

A hybrid model for monthly water demand prediction: A case study of Austin, Texas

AbstractWater demand prediction is an essential prerequisite for urban water resource allocation and pipeline network management. This article uses a hybrid model to improve the accuracy of predicting water demand. The model combines a simulated annealing (SA) algorithm, genetic cross factor (GCF), fruit fly optimization algorithm (FFOA), and support vector machine (SVM) to predict monthly water demand. The monthly water consumption data from 2012 to 2017 in Austin, Texas, are used to develop and test the model. The prediction results are compared with other intelligent algorithms, such as the hybrid model combination particle swarm optimizer with SVM and back‐propagation neural network. The results show that the mean absolute percentage error of FFOA‐GCFSA‐SVM is the smallest, and it is reduced by 16% and 51.17%, respectively, compared with the other two models. Moreover, the influences of kernel function and input variables on prediction results are analyzed, and the effects of sliding window length, data classification, and forecasting period on prediction results are discussed. We concluded that (1) for the city of Austin, the model with a Gaussian kernel function and historical data period of 6 months has higher prediction accuracy; (2) the monthly water demand in Austin is mainly influenced by the local population; and (3) data classification by season can reduce the interference of temperature on prediction.

From Walkable Communities to Active Lifestyles: Exploring Causal Pathways through a Case Study in Austin, Texas

Walkable communities have been associated with improved physical activity, but the underlying causal pathways and related social benefits are understudied. This retrospective “pre–post” survey study ( N = 446) examined “how” moving into a walkable community in Austin, Texas, affected residents’ neighborhood walking, social interactions, and neighborhood cohesion using structural equation modeling. Results showed direct and indirect (mediated by environmental barriers or social support) impacts of neighborhood environments and residential self-selection on neighborhood walking; influences of pre-move conditions on their post-move counterparts; and positive associations between neighborhood walking, social interactions, and neighborhood cohesion. Findings demonstrated complex pathways for walkable environments to promote physical activity and social health.

Exploring Urban Dynamics from Bluetooth Tracking Data: A Case Study of Austin, Texas

Abstract. In recent decades, the growing availability of location-aware devices, such as Global Positioning System (GPS) receivers and smart phones, has provided new challenges and opportunities for policy makers to analyze, model, and predict human mobility patterns. However, previous studies on Bluetooth technologies have mainly focused on applying Bluetooth data to analyzing traffic and optimizing transportation networks or deploying new Bluetooth devices in civil engineering. The use of such datasets in understanding urban dynamics and real-time land use patterns is rather limited. This study develops an extendable workflow to explore urban dynamics from Bluetooth data based on a case study in Austin, Texas. We identified similar mobility patterns in different areas of Austin during various study periods, including the Memorial Day long weekend in 2016 and a national musical festival (South by Southwest). Our main goal is to prove the efficacy of this specific workflow and methodology to understand urban dynamics based on real-time Bluetooth data. The hypothesis is that Bluetooth data is sensitive to the daily patterns of human interactions and movements on the individual level, therefore it can capture detailed dynamic patterns. The proposed research also validates new concepts such as “human sensing” and “social sensing” in the field of geography and spatial sciences, which introduces new opportunities to monitor the human aspects of social life.

Deep decarbonization from electrified autonomous taxi fleets: Life cycle assessment and case study in Austin, TX

Although recent studies of autonomous taxis (ATs) have begun to explore potential environmental implications of fleet deployment, little is known about their impacts over the long term. We present a life-cycle assessment framework that incorporates both direct and indirect effects of ATs at the subsystem, vehicle, and mobility-system levels. Eco-driving and intersection connectivity are the direct effects analyzed along with indirect effects that include empty kilometers, parking, charging infrastructure, powertrain rightsizing, electric vehicle adoption, ride-sharing, and fleet-turnover rates. A case study of an AT fleet in Austin, Texas from 2020 to 2050 with constant travel demand indicates the strategic deployment of an electrified AT fleet can reduce cumulative energy and greenhouse gas (GHG) emissions by 60% in the base case, with a majority of this benefit resulting from electrified powertrains. Further reductions up to 87% can be achieved with accelerated electrical grid decarbonization, dynamic ride-share, longer vehicle lifetime, more energy efficient computer systems, and faster fuel efficiency improvements for new vehicles. We highlight the major opportunities for maximizing the environmental performance of AT fleets over the long term.

Teaching Social History through Locative Media: A Case Study in Austin, Texas

Este trabajo explora la combinación del aprendizaje formal e informal junto con los principios de los medios locativos; hemos observado el potencial de este tipo específico de medios para reforzar y expandir los objetivos de aprendizaje fuera del tiempo de clase, dotando a los estudiantes de actitudes de aprendizaje permanente. Presentamos los hallazgos y las observaciones sobre cómo una clase de estudiantes de licenciatura utilizó medios locativos por primera vez para aprender acerca de los efectos de la segregación espacial en un área específica desatendida de Austin, Texas (ee. uu.). Pedimos a los estudiantes que visitasen varios lugares y que produjesen historias multimedia centradas en la historia local. Los resultados del estudio muestran que el uso de medios locativos es altamente eficiente para fines de aprendizaje. Los estudiantes pueden experimentar el contenido aprendido en la clase en un entorno físico, lo que fomenta el ‘aprendizaje situado’, una teoría que utilizamos como marco teórico. Nuestros hallazgos también sugieren que, en lugar de la historia local o la historia en general, los problemas actuales de la sociedad, con un fuerte componente espacial, son más eficaces para involucrar a los estudiantes en la tarea fuera del aula. Por lo tanto, la segregación y la gentrificación se revelaron en este estudio como temas útiles para ser explorados y enseñados a través de los principios de los medios locativos.

The net effects of the built environment on household vehicle emissions: A case study of Austin, TX

Growing concerns over climate change have led to an increasing interest in the role of the built environment to reduce transportation greenhouse gas (GHG) emissions. Many studies have reported that compact, mixed-use, and well-connected developments reduce vehicle miles traveled (VMT). Others, however, argue that densification and mixture of land uses can slow down vehicle movements, and consequently generate more driving emissions. Methodologically, VMT is only a proxy, not an exact measure of emissions. This study quantifies the net effects of the built environment on household vehicle emissions through a case study of Austin, TX. The study employed structural equation modeling (SEM) techniques and estimated path models to improve understanding of the relationship between the built environment and vehicle emissions. The results show a rather complex picture of the relationship. Densification can reduce regional vehicle emissions despite its secondary effect of reduced vehicle travel speed. A 1% increase in density was found to reduce household vehicle emissions by 0.1%. However, intensification of the design feature of the built environment in developed areas may work in the opposite direction; the modeling results showed a 1% increase in grid-like network being associated with 0.8% increase in household vehicle emissions. Based on the results, the study addressed the potential of and the challenges to reducing vehicle emissions through modifying the built environment in local areas.