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Publication Search Results

Now showing 1 - 10 of 24
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Explore pedestrian route choice preferences by demographic groups: analysis of street attributes in Chicago

2023-05-15 , Lieu, Seung Jae

Traditional transit accessibility models often overlook travel behavior and fine-grained transit characteristics experienced during first and last-mile walking. Existing models typically assume travelers choose the shortest walking path to minimize travel time, but studies suggest pedestrians do not always follow this pattern. This study investigates pedestrian route choice preferences in Chicago, Illinois, using a diverse dataset of home-based work walking trajectories collected from a smartphone application. The impact of street attributes on route choice is examined, and a comparison is made of how built environment factors influence preferences among different demographic groups. A path-size logit model with a constrained enumeration approach-based choice set is employed for analysis. This study also addresses two gaps in pedestrian route choice research. First, unlike most studies that use data constrained to a particular study area or limited participant groups, this research employs a diverse dataset of actual walking trajectories covering a wide range of destinations and participant profiles. Second, this study utilizes GPS data, offering more accurate route choice analysis compared to questionnaires. Such surveys may suffer from recall bias, and they may not capture route choice variability across different times and days. The findings from this study indicate that factors such as distance, the number of amenities and establishments, sky visibility, greenery, and park accessibility along the route significantly influence route choice. While route distance and the number of establishments have a negative impact on preference, other factors positively affect route selection. To compare the effect of each variable across gender, age, and income, this study has operationalized the coefficients to use the concept of ‘equivalent walking distance.' This measure quantifies the incremental disutility resulting from various route attributes, represented as an equivalent increase or decrease in walking distance. The analysis shows that male pedestrians are more willing to walk further when there is greater sky visibility. Similarly, individuals aged over 30 years old tend to walk longer distances with increased sky visibility. Notably, we found no significant variables influencing route choice among different income groups.

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Re-Think the Streets: An Evaluation of Green Street Practices as a Method to Achieve Combined Sewer Separation

2022-04-19 , Krieger, Jenna Elizbeth

Older cities across the United States have been grappling with how to mitigate stormwater for decades. The ongoing trend of land development coupled with the heightened frequency and intensity of storm events has necessitated costly infrastructure improvements that are short-sighted and fail to address the underlying cause of increased runoff. Green stormwater infrastructure (GSI) has recently emerged as a popular stormwater mitigation tool that mimics and restores the natural environment while providing the same functional benefits as conventional systems. The purpose of this research is to evaluate the effectiveness of GSI in roadside applications (i.e., “Green Streets”) to reduce combined sewer dependency and provide an alternative solution to sewer separation. Typically, roadways reach the end of their design life after 40 years, at which point, they are fully reconstructed. Reconstruction provides an opportunity to re-imagine the right-of-way (ROW) and shift away from conventional drainage design. The Green Street Toolkit presented in this research provides a planning and design framework that can be utilized prior to reconstruction to integrate green infrastructure into the ROW, which has the potential to eliminate stormwater runoff from the combined sewer system along the reconstructed segment. The Toolkit is applied under three design storm scenarios to evaluate the feasibility of a green street approach for varying storm intensities. Although green streets may not eliminate combined sewer dependency in every case, this work shows their potential in removing a substantial amount of stormwater runoff from the combined sewer system while providing secondary benefits not offered by conventional infrastructure.

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AN ATLANTA-BASED ANALYSIS ON THE FEASIBILITY OF EMPLOYEE COMMUTE OPTIONS PROGRAMS AND SWITCHING FROM DRIVING ALONE TO ALTERNATIVE COMMUTE MODES

2021-08-02 , Ling, Sharon

Employee commute options programs – also known as employer-based transportation demand management (TDM) programs – are rooted in the philosophy of TDM and trip reduction. There is a long history of TDM policies and efforts undertaken by both the public and private sectors in the United States, although the name and shape of such efforts has varied over time. However, a common goal has persisted throughout, which is to reduce employees’ reliance on gasoline-powered single-occupant vehicles (i.e. traditional cars) for traveling to and from work. To this end, employee commute options programs today often focus on incentivizing employees to switch from driving alone to using an alternative commute mode. These alternative modes range from public transit (e.g. rail or bus), ridesharing (e.g. carpooling or vanpooling), “active commuting” (e.g. biking or walking), to even alternative work hour arrangements (e.g. telecommuting) where possible (Griffin 2020). Carrot-and-stick approaches are often used to motivate employees to make the switch – such as rewarding alternative mode users with financial incentives and/or workplace perks, or even imposing charges for driving and parking. In addition, the benefits of adopting alternative modes are often extolled to the employee audience to make these options appear more attractive to potential users. Commonly cited benefits of alternatives to driving alone include reducing travel times and commute-related stress, saving commute costs, improving commuter satisfaction, creating a more sustainable environment, and so on. Employer-based TDM proponents and enthusiasts tend to emphasize, perhaps overtly so, that employee commute options programs can and will help create lasting behavioral changes. All parties involved in this enterprise – namely employers, employees, and society at large – are assumed to reap rewards from adopting TDM approaches and goals.

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Evaluating the Costs and Benefits of Implementing a MARTA Youth Fare

2020-12-10 , Todd, Kara Grace

Unlike many transit systems in the United States, the Metropolitan Atlanta Rapid Transit Authority (MARTA) does not offer a discounted youth fare. Such a fare policy creates a financial disincentive to choosing transit for many families traveling with children or youth traveling independently. Instead, most parents chauffeur their children by car, adding to the well-known traffic congestion in the Atlanta region. Encouraging the use of more sustainable travel modes, including public transit, has benefits for the physical health of travelers as well as the economic and environmental well-being of the region. The purpose of this research is to evaluate the costs and benefits, financial and otherwise, that might result if MARTA were to offer a reduced or even free youth fare. Using data from the 2011 Regional Household Travel Survey conducted by the Atlanta Regional Commission, a multinomial logit model of youth mode choice for non-school trips is developed. Various youth fare policies are then tested, including reduced and free fares for all youth as well as reduced and free fares available to only low-income youth, to estimate their potential to attract additional young riders. The policies are evaluated based on their estimated impacts on ridership and farebox revenue, as well as the socioeconomic characteristics of the individuals predicted to choose public transit in each scenario. Although offering a discounted youth fare may not be profitable to MARTA in the short-term, the positive impacts it could have on the community as a whole could outweigh the financial costs, making it worth further consideration by city and regional officials.

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Evaluating Long–Term MARTA Ridership Effects of the 2017 I–85 Bridge Collapse

2022-05-25 , Brandel-Tanis, Freyja Alice

In March 2017, an overpass on I–85 in Atlanta caught fire and collapsed, disrupting traffic for 43 days while the Georgia Department of Transportation (GDOT) rebuilt the bridge. During this time, transit ridership increased as commuters reacted to the changes in travel time, thanks in part to concerted efforts to expand service by the Metropolitan Atlanta Rapid Transit Authority (MARTA). Ridership declined after GDOT restored service but remained higher than pre-disaster levels, requiring further research to understand how long the effect lasted. Multiple linear regression models are used to investigate the relationship between 2019 ridership and origins and destinations affected by the bridge collapse. Travel time matrices from the Atlanta Regional Commission’s (ARC) Activity Based Model (ABM) are used to identify Traffic Analysis Zones (TAZs) with notable service impacts and choose comparable regions. The weighted trip counts from the ARC’s 2010 and 2019 onboard transit surveys map transit trips to origin and destination TAZs. When controlling for MARTA’s service quantity, residential and employment population, and the percent of households without access to a vehicle (choice riders), the models found a significant relationship between the region impacted by the bridge collapse and an increase in MARTA rail trips and MARTA trips by patrons who could have used a vehicle. A significant increase in choice rail ridership from the impacted TAZs, those most likely to have switched during the network disruption of 2017, suggests that the bridge collapse’s impact on MARTA riders lasted until at least the fall of 2019, over two years after the inciting network disruption.

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Machine Learning Driven Emotional Musical Prosody for Human-Robot Interaction

2021-11-18 , Savery, Richard

This dissertation presents a method for non-anthropomorphic human-robot interaction using a newly developed concept entitled Emotional Musical Prosody (EMP). EMP consists of short expressive musical phrases capable of conveying emotions, which can be embedded in robots to accompany mechanical gestures. The main objective of EMP is to improve human engagement with, and trust in robots while avoiding the uncanny valley. We contend that music - one of the most emotionally meaningful human experiences - can serve as an effective medium to support human-robot engagement and trust. EMP allows for the development of personable, emotion-driven agents, capable of giving subtle cues to collaborators while presenting a sense of autonomy. We present four research areas aimed at developing and understanding the potential role of EMP in human-robot interaction. The first research area focuses on collecting and labeling a new EMP dataset from vocalists, and using this dataset to generate prosodic emotional phrases through deep learning methods. Through extensive listening tests, the collected dataset and generated phrases were validated with a high level of accuracy by a large subject pool. The second research effort focuses on understanding the effect of EMP in human-robot interaction with industrial and humanoid robots. Here, significant results were found for improved trust, perceived intelligence, and likeability of EMP enabled robotic arms, but not for humanoid robots. We also found significant results for improved trust in a social robot, as well as perceived intelligence, creativity and likeability in a robotic musician. The third and fourth research areas shift to broader use cases and potential methods to use EMP in HRI. The third research area explores the effect of robotic EMP on different personality types focusing on extraversion and neuroticism. For robots, personality traits offer a unique way to implement custom responses, individualized to human collaborators. We discovered that humans prefer robots with emotional responses based on high extraversion and low neuroticism, with some correlation between the humans collaborator’s own personality traits. The fourth and final research question focused on scaling up EMP to support interaction between groups of robots and humans. Here, we found that improvements in trust and likeability carried across from single robots to groups of industrial arms. Overall, the thesis suggests EMP is useful for improving trust and likeability for industrial, social and robot musicians but not in humanoid robots. The thesis bears future implications for HRI designers, showing the extensive potential of careful audio design, and the wide range of outcomes audio can have on HRI.

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Composing and Decomposing Electroacoustic Sonifications: Towards a Functional-Aesthetic Sonification Design Framework

2021-05-01 , Tsuchiya, Takahiko

The field of sonification invites musicians and scientists for creating novel auditory interfaces. However, the opportunities for incorporating musical design ideas into general functional sonifications have been limited because of the transparency and communication issues with musical aesthetics. This research proposes a new design framework that facilitates the use of musical ideas as well as a transparent representation or conveyance of data, verified with two human subjects tests. An online listening test analyzes the effect of the structural elements of sound as well as a guided analytical listening to the perceptibility of data. A design test examines the range of variety the framework affords and how the design process is affected by functional and aesthetic design goals. The results indicate that the framework elements, such as the synthetic models and mapping destinations affect the perceptibility of data, with some contradictions between the designer's general strategies and the listener's responses. The analytical listening nor the listener's musical background show little statistical trends, but instead imply complex relationships of types of interpretations and the structural understanding. There are also several contrasting types in the design and listening processes which indicate different levels of structural transparency as well as the applicability of a wider variety of designs.

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Sustainable Funding Mechanisms for Recreational Trails

2022-05-03 , Bray, Vincent Micah

Recreational trails are a crucial component of public infrastructure in communities across the United States, having significant social, environmental, and economic impacts in urban, suburban, and rural settings. In addition to benefits derived from recreation, trails also play an important role for commuters seeking alternative modes to motorized vehicles. Furthermore, recreational trails have provided communities with outdoor space to foster resiliency in the face of lockdowns and quarantines during the COVID-19 pandemic. Despite growing national importance, deficits in funding for operations and maintenance (O&M) have led to deferred maintenance backlogs that reduce the value added by recreational trails while worsening negative environmental impacts. This work proposes a value capture (VC) approach to funding trail O&M that dedicates state sales tax revenue generated from retail trade and food and accommodation services supported by recreational trails to trust funds that can be distributed to state and local trail managing entities. State-level estimates are generated using economic data from the Outdoor Recreation Satellite Account of the U.S. Bureau of Economic Analysis to estimate sales tax revenue from activities and industries supported by trails. VC estimates are further analyzed to assess the scale and stability of this funding approach before identifying methods for implementation of this approach. Findings of this study include: 1) total estimated state sales tax revenue of $10.64 billion in 2020 USD suggests that VC could greatly expand O&M funding for trails at the state level; 2) not all states may see growth in economic activity supported by recreational trails as demographic shifts occur between states; and 3) successful implementation of VC would likely require legislative protections to prevent reduction of appropriations or diversion of funds to other state accounts.

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Shared E-scooter Adoption and Mode Substitution Patterns

2021-08-30 , Chen, Yun-Hsuan

This thesis explores the adoption and mode substitution patterns of e-scooters using survey data from four metropolitan areas in the southern United States, obtained from Fall 2019 to Spring 2020. For adoption patterns, we find a positive correlation between the use of ridehailing services and being an e-scooter user, as well as observed higher multimodality for e-scooter users compared to non-users (N =2,914). E-scooters are found to be used by people with lower income, higher racial diversity, and certain disabilities. For substitution patterns, we examine heterogeneity in trip attributes, substitution patterns, and rider characteristics in a sample of e-scooter rides (N=295). With a latent-class cluster analysis, we identify three distinctive classes of e-scooter rides and associated users. The off-to-nightlife class (39.9%) captures many rides for social and recreational trips at night, many of which substitute for private vehicles, ridehailing, or taxis. Many users associated with this class are college-educated and middle-aged with middle-to-high household income, convenient access to cars, and positive attitudes toward density, technology, and environmental policies. The weekend-fun class (31.9%) includes many trips made “just for fun” by users, many of which would not have been made otherwise. Riders taking this type of trip rarely use e-scooters, live in the least dense suburbs with auto-oriented lifestyles, and are more likely to be female, older (relative to the other classes), well-educated, and wealthy. The commutes class (28.2%) tends to involve short rides during weekday daytime for work/school-related trips, most of which would replace active modes. Most commutes users are low-income young students with diverse racial backgrounds and limited access to cars. These tend to reside in the densest neighborhoods and are the most multimodal in the sample. For each class, we discuss behavioral mechanisms and policy options for sustainable transportation. In brief, this thesis fills important literature gaps by identifying heterogeneous e-scooter rides and users, incorporating attitudes, and focusing on the southern U.S.

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Learning to manipulate latent representations of deep generative models

2021-01-14 , Pati, Kumar Ashis

Deep generative models have emerged as a tool of choice for the design of automatic music composition systems. While these models are capable of learning complex representations from data, a limitation of many of these models is that they allow little to no control over the generated music. Latent representation-based models, such as Variational Auto-Encoders, have the potential to alleviate this limitation as they are able to encode hidden attributes of the data in a low-dimensional latent space. However, the encoded attributes are often not interpretable and cannot be explicitly controlled. The work presented in this thesis seeks to address these challenges by learning to manipulate and design latent spaces in a way that allows control over musically meaningful attributes that are understandable by humans. This in turn can allow explicit control of such attributes during the generation process and help users realize their compositional goals. Specifically, three different approaches are proposed to investigate this problem. The first approach shows that we can learn to traverse latent spaces of generative models to perform complex interactive music composition tasks. The second approach uses a novel latent space regularization technique which can encode individual musical attributes along specific dimensions of the latent space. The third approach attempts to use attribute-informed non-linear transformations over an existing latent space such that the transformed latent space allows controllable generation of data. In addition, the problem of disentanglement learning in the context of symbolic music is investigated systematically by proposing a tailor-made dataset for the task and evaluating the performance of several different methods for unsupervised and supervised disentanglement learning. Together, the proposed methods will help address critical shortcomings of deep music generative models and pave the path towards intuitive interfaces which can be used by humans in real compositional settings.

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