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Doctor of Philosophy with a Major in City and Regional Planning

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https://hdl.handle.net/1853/73257
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Publication Search Results

Now showing 1 - 6 of 6
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    Breaking Myths behind "Bikelash": Empirical analyses on the role of protected bike lanes on creating a sustainable, equitable, and safer transportation environment
    (Georgia Institute of Technology, 2023-12-10) Hwang, Uijeong
    While cycling is recognized as an eco-friendly alternative to carbon-emitting vehicles and a facilitator of physical activity, opposition to cyclists and bike lanes—termed “bikelash”—persists. This resistance stems from various concerns, ranging from the belief that bike lanes are underutilized and thus a waste of public space to fears that they may exacerbate traffic congestion or even contribute to urban gentrification. Through three detailed studies, this dissertation aims to provide empirical evidence challenging common perceptions and to reshape the narrative surrounding bike lanes. The first study investigates how bike lanes sway individuals' transportation choices, especially favoring non-automotive travel. It employs a novel approach by conducting a route-level analysis using origin-destination data from household travel surveys to simulate potential cycling routes. This study finds that bike lanes significantly encourage the use of walking, cycling, and public transit, thereby reducing car dependency. Notably, it demonstrates the potential of bike lanes to bridge mobility gaps in diverse socio-economic settings, even in underserved neighborhoods. The second study examines how the perception of streetscapes and different types of bike lanes interact to influence cycling behaviors. Utilizing computer vision to interpret perceptual attributes from crowdsourced street view images, the research reveals that the presence and type of bike lanes significantly influence cycling frequency, moderated by the visual perception of streetscape safety. Protected bike lanes are shown to be more effective in areas perceived as less safe, emphasizing their role in equitable transportation. The final paper delves into the safety impact of bike lanes, particularly focusing on near-miss incidents. It utilizes crowdsourced data to examine the risk of near misses in relation to street design and types of bike lanes. The findings indicate that protected bike lanes significantly reduce the risk of near misses, while striped lanes adjacent to street parking increase safety hazards. This challenges existing assumptions about bike lane safety and calls for a strategic shift towards protected bike lanes. This dissertation contributes to practical applications by providing critical empirical evidence to guide urban infrastructure planning and policymaking. The findings support a shift towards protected bike lanes, emphasizing their role in enhancing safety and increasing cycling frequency. The dissertation advocates for a holistic approach in urban transportation planning, promoting inclusive, sustainable, and safer urban environments.
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    Investigating Travel Behavior in Transit-Oriented Development: Toward Sustainable and Multimodal Mobility
    (Georgia Institute of Technology, 2021-12-14) Choi, Yunkyung
    An extensive literature has shown that transit-oriented development residents (TOD) have lower automobile use and diverse travel modes due to easy access to transit, better walkability, and proximity to various amenities. While such benefits of TOD are generally expected, the degree to which TODs influence travel behavior is still debatable. Besides, TOD implementation differs by context, and not all transit areas are developed along TOD principles. This variation in transit areas leads to different impacts on transportation outcomes. Although different TOD typologies have been developed in past studies, they are limited to a particular city or region. The other ongoing debate in land use and travel behavior field is the emergence of new mobility services that enable users to utilize a mode of transport on an as-needed basis. Recent advances in information technologies have facilitated new mobility services that meet travelers’ diverse needs, such as transportation network companies (TNCs), ridesharing, car sharing, bike sharing, microtransit, and shared autonomous vehicles. While new mobility services are expected to play an important role—either positive or negative—in planning how TODs can be implemented, the impacts and consequences of such services on traditional modes of transport such as public transit are still not well understood. In doing so, this dissertation investigates different modes of transportation in TOD areas by posing the following research questions: 1) do people walk more in transit-oriented developments? 2) are residents more multimodal in transit-oriented developments? and 3) what is the potential impact of new mobility services on public transit demand? For the first question, this dissertation addresses the effect of rail transit access on walking behavior in TOD areas. TODs are compared to other similar areas without rail transit access to determine whether people are more likely to walk in TODs for purposes other than transit use in Atlanta. The second question is addressed by identifying different TOD types on their impacts on residents’ multimodal behavior to capture various conditions of existing TODs and their heterogeneous outcomes. This research identifies different types of 4,400 transit areas—a half-mile buffer area from rail station—in the U.S. and develops several analytic models to explain the multimodal traveler behavior in the 2017 NHTS. The third question examines the potential impacts of TNCs on transit demand in Chicago, with a particular focus on understanding heterogeneity in the effects by employing fixed effects panel regression models. By investigating various travel behavior around transit station areas, this dissertation provides insights on how TODs can be better implemented to promote sustainable and multimodal travel behavior.
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    Assessing mental wellbeing in urban areas using social media data: understanding when and where urbanites stress and de-stress
    (Georgia Institute of Technology, 2021-12-10) Dutt, Florina
    Are Americans more stressed out by living in dense, urbanized areas or less dense, car-oriented areas? To answer this question, can we use people's expressions of stress in different environments to understand what kinds of spaces help them de-stress? This study uses stress levels of geolocated tweets to help us answer such inquiries and resolve the longstanding disparities between the field of psychology and urban planning about mental health impacts of cities. This is important because more than 75 percent of Americans are moderately stressed. Long-term stress is associated with mental health disorders, including sleeplessness, anxiety, and depression. Additionally, chronic stress is linked to physical ailments, including high blood pressure, cardiovascular diseases, and diabetes. The psychology literature claims that urban areas witness elevated levels of mental health problems, manifested as stress, mood disorders, and anxiety issues. Density, crowding, traffic, crime, and pollution are identified as stressors associated with urban living conditions. Contending this claim, the urban planning literature positions stress in the context of longer commutes, lack of accessibility, and social isolation that comes with suburban living conditions. Urban Planners and urban designers have advocated for density. With rapid urbanization, 60 percent of the world population will live in urban areas by 2030, making it crucial for urban planners to address these disparities to support the mental wellbeing of the urbanites. This research uses multi-headed attention transformer model to classify tweets (token sequences), and assesses the stress levels of custom-defined assessment grids of ten acres within the city area of Atlanta and Boston. The assessed stress level of these assessment grids is called the mental wellbeing score (MWS). Mental wellbeing score is defined in this research as a measure of `mental wellbeing' of any given grid (higher score is better). Using this measure, the research investigates the relationship between mental wellbeing and built environment characteristics in urban areas to uncover the impact of long-term stress triggered by the conditions of the built environment in urban settings. In summary, the results of the exploration shed light on three critical aspects: 1. Mental wellbeing score increases with increasing urbanness. 2. The mental wellbeing score increases with the increase in the diversity of escape facilities, including green parks, open spaces, and other points of interest. 3. The mental wellbeing score is positively impacted by accessible high-density spaces with high symbolic value. The research also investigates the impact of safety perception and socio-economic status on mental wellbeing scores. The results show that addressing socio-economic disparity, crime, and investment in green infrastructure can improve mental wellbeing of urbanites. The methods and findings of the research show that 'urban areas' can positively impact mental health if designed appropriately. Furthermore, this study can empower urban planners and policymakers to develop tools to assess the mental wellbeing of urbanites, adjust infrastructure needs, and improve the urban amenities that support mental wellbeing.
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    Measuring Street-Level Walkability through Big Image Data and Its Associations with Walking Behavior
    (Georgia Institute of Technology, 2021-07-26) Koo, Bon Woo
    The built environment characteristics associated with walkability range from neighborhood-level urban form factors to street-level urban design factors. However, many existing walkability measures are primarily based on neighborhood-level factors and lack consideration for street-level factors. Neighborhood-level factors alone can be limited in representing various needs of pedestrians. While pedestrians seek to fulfill their needs for accessibility, safety, comfort, and pleasurability, neighborhood-level factors tend to be limited to capturing the accessibility of the built environment (i.e., having places to go to and being physically connected to those places). The high-order needs (i.e., safety from crime, comfort from vehicular traffic, and aesthetic pleasurability) can be more closely proxied by street-level factors. Also, past studies suggested that certain street-level factors may weaken (or strengthen) the effect of neighborhood-level factors on walking behavior, which can be particularly important for disadvantaged populations who tend to be less responsive to neighborhood-level factors. However, measuring street-level factors often requires extensive manual labor and tends to be resource-intensive, resulting in the omission of street-level factors in widely used walkability measures such as Walk Score. This dissertation uses street view images and computer vision to overcome these challenges in measuring street-level factors and expands the literature by examining their association with walking mode choice. This dissertation first applies a pre-trained computer vision model to street view images and measure mesoscale (i.e., a midlevel spatial scale between macro and microscale) factors of walkability. It finds that the mesoscale factors have a significant contribution to walking mode choice models, and the contribution is greater than that from neighborhood-level factors. Next, the dissertation develops a method for automatically auditing walkability factors in microscale (i.e., the smallest spatial scale that pertains to the most fine-grain design details and their qualities) using the combination of computer vision, street view images, and geographic information systems. The validation results demonstrate moderate to high reliability between audit results by automated audit method and a trained human auditor. Finally, the dissertation uses automatically audited microscale factors to unpack the reasons for the weaker relationship between neighborhood-level factors and disadvantaged populations’ walking behavior. The result shows that microscale factors play a sizable role in moderating the effect of neighborhood-level factors. Collectively, this dissertation demonstrates the potential of using street view images and computer vision for research on the built environment-walking relationship and for collecting data on street-level factors over expansive geographic areas, a task that has traditionally been prohibitively expensive. The theoretical and methodological contributions of this dissertation help urban planners and designers understand the physical condition of their cities at street-level and make targeted interventions that are effective and equitable.
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    Will millennials stay in cities and travel without cars?
    (Georgia Institute of Technology, 2018-08-24) Lee, Yongsung
    Will millennials stay in cities and travel without cars? To answer this question, this dissertation examines heterogeneity in modality styles and residential preferences in a sample of millennials and members of Generation X in California in 2015. It finds that both sociodemographic/ economic characteristics and attitudes about various dimensions (e.g., preferred built environments, travel modes, and car ownership) account for the heterogeneous behavioral and choice patterns in the sample. These findings provide insights on the ways millennials may switch their modality styles or residential preferences in response to changes in sociodemographic/economic conditions or attitudes in the coming years. This dissertation highlights the use of latent-class approaches as effective for the identification of heterogeneity in tastes related to the travel behaviors and location choices of millennials. Researchers are advised to apply these approaches to longitudinal analyses. This research also informs planners and policymakers of dynamic changes in the form or share of latent classes in their region.
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    The interaction between land use and transportation in the era of shared autonomous vehicles: a simulation model
    (Georgia Institute of Technology, 2017-05-19) Zhang, Wenwen
    The promising Shared Autonomous Vehicle (SAV) system will inevitably lead to changes in urban land use. Despite recent proliferating studies regarding SAVs, it remains unclear how this affordable and environmentally friendly travel mode will influence residential and commercial location choices and potentially transform urban form. This dissertation develops a discrete event based SAV simulation and implements the model using the transportation network, travel demand, and land use data from Atlanta Metropolitan area. The model is then integrated with residential and employment (re)location choice models to explore how the SAV system will affect urban parking, residential land use, as well as employment agglomeration patterns. The results suggest SAV can significantly reduce parking demand by over 90%. Additionally, the simulation results also indicate the system will not induce residential sprawl into rural areas. Finally, it appears that SAV will accelerate the existing deindustrialization process in cities. The results of this study can provide implications for devising more sustainable land use policies in the era of SAVs.