Browsing by Author "Zhang, Wenwen"
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- Changes in Travel Behavior, Attitudes, and Preferences among E-Scooter Riders and Non-Riders: A First Look at Results from Pre and Post E-Scooter System Launch Surveys at Virginia TechBuehler, Ralph; Broaddus, Andrea; Swenney, Ted; Mollenhauer, Michael A.; White, Elizabeth; Zhang, Wenwen (2021-04-22)Shared micromobility such as electric scooters (e-scooters) has potential to enhance the sustainability of urban transport by displacing car trips, providing more mobility options, and improving access to public transit. Most published studies on e-scooter ridership focus on cities and only capture data at one point in time. This study reports results from two cross-sectional surveys deployed before (n=462) and after (n=428) the launch of a fleet of shared e-scooters on Virginia Tech’s campus in Blacksburg, VA. This allowed for a pre-post comparison of attitudes and preferences of e-scooter riders and non-users. E-scooter ridership on campus follows patterns identified in other studies, with a greater share of younger riders—in particular undergraduate students. Stated intention to ride prior to system launch was greater than actual ridership after system launch. The drop-off between pre-launch intention to ride and actual riding was strongest for older age groups, women, and university staff. As in city surveys, the main reasons for riding e-scooters on campus were travel speed and fun of riding. About 30% indicated using e-scooters to ride to parking lots or to access public transport service—indicating e-scooters’ potential as connector to other modes of transport. Compared to responses prior to system launch, perceptions about the convenience, cost, safety, parking, rider behavior, and usefulness of the e-scooter systems were more positive among non-riders after system launch—indicating that pilot projects may improve public perception of e-scooters. Building more bike lanes or separate spaces for e-scooters to ride could help move e-scooter riders off sidewalks—a desire expressed by both pedestrians and e-scooter users.
- Combining expert and crowd-sourced training data to map urban form and functions for the continental USDemuzere, Matthias; Hankey, Steven C.; Mills, Gerald; Zhang, Wenwen; Lu, Tianjun; Bechtel, Benjamin (2020-08-11)Although continental urban areas are relatively small, they are major drivers of environmental change at local, regional and global scales. Moreover, they are especially vulnerable to these changes owing to the concentration of population and their exposure to a range of hydro-meteorological hazards, emphasizing the need for spatially detailed information on urbanized landscapes. These data need to be consistent in content and scale and provide a holistic description of urban layouts to address different user needs. Here, we map the continental United States into Local Climate Zone (LCZ) types at a 100 m spatial resolution using expert and crowd-sourced information. There are 10 urban LCZ types, each associated with a set of relevant variables such that the map represents a valuable database of urban properties. These data are benchmarked against continental-wide existing and novel geographic databases on urban form. We anticipate the dataset provided here will be useful for researchers and practitioners to assess how the configuration, size, and shape of cities impact the important human and environmental outcomes.
- The Dynamics of the Impacts of Automated Vehicles: Urban Form, Mode Choice, and Energy Demand DistributionWang, Kaidi (Virginia Tech, 2021-08-24)The commercial deployment of automated vehicles (AVs) is around the corner. With the development of automation technology, automobile and IT companies have started to test automated vehicles. Waymo, an automated driving technology development company, has recently opened the self-driving service to the public. The advancement in this emerging mobility option also drives transportation reasearchers and urban planners to conduct automated vehicle-related research, especially to gain insights on the impact of automated vehicles (AVs) in order to inform policymaking. However, the variation with urban form, the heterogeneity of mode choice, and the impacts at disaggregated levels lead to the dynamics of the impacts of AVs, which not comprehensively understood yet. Therefore, this dissertation extends existing knowledge base by understanding the dynamics of the impacts from three perspectives: (1) examining the role of urban form in the performance of SAV systems; (2) exploring the heterogeneity of AV mode choices across regions; and (3) investigating the distribution of energy consumption in the era of AVs. To examine the first aspect, Shared AV (SAV) systems are simulated for 286 cities and the simulation outcomes are regressed on urban form variables that measure density, diversity, and design. It is suggested that the compact development, a multi-core city pattern, high level of diversity, as well as more pedestrian-oriented networks can promote the performance of SAVs measured using service efficiency, trip pooling success rate, and extra VMT generation. The AV mode choice behaviors of private conventional vehicle (PCV) users in Seattle and Knasas City metropolitan areas are examined using an interpretable machine learning framework based on an AV mode choice survey. It is suggested that attitudes and trip and mode-specific attributes are the most predictive. Positive attitudes can promote the adoption of PAVs. Longer PAV in-vehicle time encourages the residents to keep the PCVs. Longer walking distance promotes the usage of SAVs. In addition, the effects of in-vehicle time and walking distance vary across the two examined regions due to distinct urban form, transportation infrustructure and cultural backgrounds. Kansas City residents can tolerate shorter walking distance before switching to SAV choices due to the car-oriented environment while Seattle residents are more sensitive to in-vehicle travel time because of the local congestion levels. The final part of the dissertation examines the demand for energy of AVs at disaggregated levels incorporating heterogeneity of AV mode choices. A three-step framework is employed including the prediction of mode choice, the determination of vehicle trajectories, and the estimation of the demand for energy. It is suggested that the AV scenario can generate -0.36% to 2.91% extra emissions and consume 2.9% more energy if gasoline is used. The revealed distribution of traffic volume suggests that the demand for charging is concentrated around the downtown areas and on highways if AVs consume electricity. In summary, the dissertation demonstrates that there is a dynamics with regard to the impacts and performance of AVs across regions due to various urban form, infrastructure and cultural environment, and the spatial heterogeneity within cities.
- The Fiscal Resilience of American CitiesSpencer, Samuel Summers (Virginia Tech, 2018-07-11)This paper brings together the concepts of fiscal health and resilience as they are understood in a contemporary context while seeking to establish whether a quantitative model of analysis can be meaningfully derived and applied to major American cities. Using major recessions from 1977 to 2015 as an exogenous shock, the values for fiscal health are assessed temporally to arrive at an assessment for whether a certain group of cities is inherently more resilient than others. Given subjective nature of the concepts used, this paper also grapples with the fact that any results must be analyzed within a local context. The end result is aimed to produce a tool for cities to compare how they performed in the wake of a recession and eventually work towards an understanding of what policy actions can be done to make a city more resilient.
- Infrastructure Condition Assessment and Prediction under Variable Traffic Demand and Management ScenariosAbi Aad, Mirla (Virginia Tech, 2022-11-08)Departments of Transportation (DOTs) are responsible for keeping their road network in a state of good repair while also aiming to reduce congestion through the implementation of different traffic control and demand management strategies. These strategies can result in changes in traffic volume distributions, which in turn affect the level of pavement deterioration due to traffic loading. To address this issue, this dissertation introduces an integrated simulation-optimization framework that accounts for the combined effects of pavement conditions and traffic management decision-making strategies. The research focuses on exploring the range of possible performance outcomes resulting from this integrated modeling approach. The research also applied the developed framework to a particular traffic demand management strategy and assessed the impact of dynamic tolls around the specific site of I-66 inside the beltway. The integrated traffic-management/pavement-treatment framework was applied to address both the operational and pavement performance of the network. Aimsun hybrid macro/meso dynamic user equilibrium experiments were used to simulate the network with a modified cost function taking care of the dynamic pricing along the I-66 tolled facility. Furthermore, the framework was expanded to include the development of a systematic and comprehensive methodology to optimize the allocation of networkwide pavement treatment work zones over space and time. The proposed methodology also contributed to the development of a surrogate function that reduces the optimization computation burden so that researchers would be able to conduct work zone allocation optimization without having to run expensive simulation work. Finally, in this dissertation, a user-friendly decision-support tool was developed to assist in the pavement treatment and project selection planning process. We use machine learning models to encapsulate the simulation optimization process.
- Land Use Regression models for 60 volatile organic compounds: Comparing Google Point of Interest (POI) and city permit dataLu, Tianjun; Lansing, Jennifer; Zhang, Wenwen; Bechle, Matthew J.; Hankey, Steven C. (2019-08-10)Land Use Regression (LUR) models of Volatile Organic Compounds (VOC) normally focus on land use (e.g., industrial area) or transportation facilities (e.g., roadway); here, we incorporate area sources (e.g., gas stations) from city permitting data and Google Point of Interest (POI) data to compare model performance. We used measurements from 50 community-based sampling locations (2013-2015) in Minneapolis, MN, USA to develop LUR models for 60 VOCs. We used three sets of independent variables: (1) base-case models with land use and transportation variables, (2) models that add area source variables from local business permit data, and (3) models that use Google POI data for area sources. The models with Google POI data performed best; for example, the total VOC (TVOC) model has better goodness-of-fit (adj-R-2: 0.56; Root Mean Square Error [RMSE]: 032 mu g/m(3)) as compared to the permit data model (0.42; 037) and the base-case model (0.26; 0.41). Area source variables were selected in over two thirds of models among the 60 VOCs at small-scale buffer sizes (e.g., 25 m-500 m). Our work suggests that VOC LUR models can be developed using community-based sampling and that models improve by including area sources as measured by business permit and Google POI data. (C) 2019 The Authors. Published by Elsevier B.V.
- Leveraging Street View and Remote Sensing Imagery to Enhance Air Quality Modeling through Computer Vision and Machine LearningQi, Meng (Virginia Tech, 2024-02-14)Air pollution is associated with various adverse health impacts and is identified as one of the leading risk factors for global disease burden. Further, air pollution is one of the pathways through which climate change could negatively impact health. Field studies have shown that air pollution has high spatiotemporal variability and pollutant concentrations vary substantially within neighborhoods. Characterizing air pollution at a fine-grained level is essential for accurately estimating human exposure, assessing its impact to human health, and further aiding localized air pollution policy. Air quality models are developed to estimate air pollution at locations and time periods without monitors, and these estimates are commonly used for exposure and health effects studies. Traditional land use regression [LUR] models are one of the cost-effective empirical air quality models. LUR typically relies on fixed-site measurements, GIS-derived variables with limited spatial resolution, and captures linear relationships. In recent years, innovative open-source imagery datasets and their associated features (e.g., street view imagery, remote sensing imagery) have emerged and show potential to augment or replace traditional LUR predictors. Such imagery data sources embody abundant information of natural and built environment features. Advanced computer vision techniques enable feature extraction and quantification through these extensive imagery datasets. The overarching objective of this dissertation is to investigate the feasibility of leveraging open-source imagery datasets (i.e., Google Street View [GSV] imagery, Landsat imagery, etc.) and advanced machine learning algorithms to develop image-based empirical air quality models at both local and national scale. The first study of this work established a pipeline of feature extraction through street view imagery sematic segmentation. The resulting street view features were used to predict street-level particulate air pollution for a single city. The results showed that solely using GSV-derived features can achieve comparable model fits as using traditional GIS-derived variables. Feature engineering improved model stability and interpretability through reducing spurious variables from potential misclassifications from computer vision algorithms. The second study further developed GSV-based models at national scale across multiple years. Random forest models were developed to capture the nonlinear relationship between air pollution and its impacting factors. The results showed that with sufficient street view images, GSV imagery alone may explain the variation of long-term national NO2 concentrations. Adding satellite-derived aerosol estimates (i.e., OMI column density) can significantly boost model performance when GSV images are insufficient, but the addition narrows when more GSV images are available. Our systematic assessment of the impact of image availability on model performance suggested that a parsimonious image sampling strategy (i.e., one GSV image per 100m grid) may be sufficient and most cost-effective for model development and application. Our third study explored the feasibility of combining street view and remote sensing derived features for national NO2 and PM2.5 modeling and projection at high spatial resolution. We found that GSV-based models captured both the highest and lowest pollutant concentrations while remote sensing features tended to smooth the air pollution variations. The results suggested that GSV features may have the capability to better capture fine-scale air pollution variability. The resulting air pollution prediction product may serve a variety of applications, including providing new insights into environmental justice and epidemiological studies due to its high spatial resolution (i.e., street level). Collectively, the result of this dissertation suggests that GSV imagery, processed with computer vision techniques, is a promising data source to develop empirical air quality models with high spatial resolution and consistent predictor variables processing protocol. Image-based features assisted with advanced ML approaches have the potential to greatly improve air quality modeling estimates, and successfully show comparable and even superior model performance than other modeling studies. Moreover, the ever-growing public imagery data sources are particularly promising for remote or less developed areas where traditional curated geodatabases are sparse or nonexistent.
- New Opportunities in Crowd-Sourced Monitoring and Non-government Data Mining for Developing Urban Air Quality Models in the USLu, Tianjun (Virginia Tech, 2020-05-15)Ambient air pollution is among the top 10 health risk factors in the US. With increasing concerns about adverse health effects of ambient air pollution among stakeholders including environmental scientists, health professionals, urban planners and community residents, improving air quality is a crucial goal for developing healthy communities. The US Environmental Protection Agency (EPA) aims to reduce air pollution by regulating emissions and continuously monitoring air pollution levels. Local communities also benefit from crowd-sourced monitoring to measure air pollution, particularly with the help of rapidly developed low-cost sampling technologies. The shift from relying only on government-based regulatory monitoring to crowd-sourced effort has provided new opportunities for air quality data. In addition, the fast-growing data sciences (e.g., data mining) allow for leveraging open data from different sources to improve air pollution exposure assessment. My dissertation investigates how new data sources of air quality (e.g., community-based monitoring, low-cost sensor platform) and model predictor variables (e.g., non-government open data) based on emerging modeling approaches (e.g., machine learning [ML]) could be used to improve air quality models (i.e., land use regression [LUR]) at local, regional, and national levels for refined exposure assessment. LUR models are commonly used for predicting air pollution concentrations at locations without monitoring data based on neighboring land use and geographic variables. I explore the use of crowd-sourced low-cost monitoring data, new/open dataset from government and non-government sponsored platforms, and emerging modeling techniques to develop LUR models in the US. I focus on testing whether: (1) air quality data from community-based monitoring is feasible for developing LUR models, (2) air quality data from non-government crowd-sourced low-cost sensor platforms could supplement regulatory monitors for LUR development, and (3) new/open data extracted from non-government sponsored platforms could serve as alternative datasets to traditional predictor variable sources (e.g., land use and geographic features) in LUR models. In Chapter 3, I developed LUR models using community-based sampling (n = 50) for 60 volatile organic compounds (VOC) in the city of Minneapolis, US. I assessed whether adding area source-related features improves LUR model performance and compared model performance using variables featuring area sources from government vs. non-government sponsored platforms. I developed three sets of models: (1) base-case models with land use and transportation variables, (2) base-case models adding area source variables from local business permit data (government sponsored platform), and (3) base-case models adding Google point of interest (POI) data for area sources. Models with Google POI data performed the best; for example, the total VOC (TVOC) model had better goodness-of-fit (adj-R2: 0.56; Root Mean Square Error [RMSE]: 0.32 µg/m3) as compared to the permit data model (0.42; 0.37) and the base-case model (0.26; 0.41). This work suggests that VOC LUR models can be developed using community-based samples and adding Google POI could improve model performance as compared to using local business permit data. In Chapter 4, I evaluated a national LUR model using annual average PM2.5 concentrations from low-cost sensors (i.e., PurpleAir platform) in 6 US urban areas (n = 149) and tested the feasibility of using low-cost sensor data for developing LUR models. I compared LUR models using only the PurpleAir sensors vs. hybrid LUR models (combining both the EPA regulatory monitors and the PurpleAir sensors). I found that the low-cost sensor network could serve as a promising alternative to fill the gaps of existing regulatory networks. For example, the national regulatory monitor-based LUR (i.e., CACES LUR developed as part of the Center for Air, Climate, and Energy Solutions) may fail to capture locations with high PM2.5 concentrations and the within-city spatial variability. Developing LUR models using the PurpleAir sensors was reasonable (PurpleAir sensors only: 10-fold CV R2 = 0.66, MAE = 2.01 µg/m3; PurpleAir and regulatory monitors: R2 = 0.85, MAE = 1.02 µg/m3). I also observed that incorporating PurpleAir sensor data into LUR models could help capture within-city variability and merit further investigation on areas of disagreement with the regulatory monitors. This work suggests that the use of crowd-sourced low-cost sensor networks for LUR models could potentially help exposure assessment and inform environmental and health policies, particularly for places (e.g., developing countries) where regulatory monitoring network is limited. In Chapter 5, I developed national LUR models to predict annual average concentrations of 6 criteria pollutants (NO2, PM2.5, O3, CO, SO2 and PM10) in the US to compare models using new data (Google POI, Google Street View [GSV] and Local Climate Zone [LCZ]) vs. traditional geographic variables (e.g., road lengths, area of built land) based on different modeling approaches (partial least square [PLS], stepwise regression and machine learning [ML] with and without Kriging effect). Model performance was similar for both variable scenarios (e.g., random 10-fold CV R2 of ML-kriging models for NO2, new vs. traditional: 0.89 vs. 0.91); whereas adding the new variables to the traditional LUR models didn't necessarily improve model performance. Models with kriging effect outperformed those without (e.g., CV R2 for PM2.5 using the new variables, ML-kriging vs. ML: 0.83 vs. 0.67). The importance of the new variables to LUR models highlights the potential of substituting traditional variables, thus enabling LUR models for areas with limited or no data (e.g., developing countries) and across cities. The dissertation presents the integration of new/open data from non-government sponsored platform and crowd-sourced low-cost sensor networks in LUR models based on different modeling approaches for predicting ambient air pollution. The analyses provide evidence that using new data sources of both air quality and predictor variables could serve as promising strategies to improve LUR models for tracking exposures more accurately. The results could inform environment scientists, health policy makers, as well as urban planners interested in promoting healthy communities.
- Smart City and Related Implementation Challenges - Case Study: Kakinada and KanpurGupta, Khushboo (Virginia Tech, 2020-02-13)With advancement in information and communication technologies (ICT), Smart Cities are becoming a popular urban development strategy amongst policymakers and city managers to respond to various threats posed by rapid urbanization such as environmental degradation and increasing inequality (Hartemink, 2016). Therefore, globally, regions ranging from small towns to megacities are proposing and investing in smart city (SC) initiatives. Unfortunately, the prolific use of this term by city managers and technology vendors is clouding the view on what it really takes to become a SC (Van den Bergh and Viaene, 2015). Consequently, cities are experiencing multiple implementation risks when trying to turn a smart city ambition into reality. These implementation risks reflect the gaps or missing pieces in the current organizational structure and policies designed for implementing SC projects at the city level. They can be understood better if the process of SC transformation is explored using diverse cases of cities undergoing such a transformation. However, the current studies on SC initiatives at the local, regional, national, and international level have focused on: 1) strengthening the SC concept rather than understanding the practical implementation of the concept – i.e., discussing SC characteristics and outcomes rather than focusing on the challenges faced in implementing SC projects; 2) cases that have already been developed as a SC or are soon to become a SC, leaving out the opportunity to study cities undergoing SC transformation and the identification of implementation risks; and 3) cases from more advanced economies. Taken together, these observations reveal the need for research that focuses on SC initiatives in a developing nation context. More specifically, there is a need for researchers, city managers, and policymakers in these regions to focus on the process of SC transformation to identify implementation risks early on in the process. Understanding these risks may help the development of better risk mitigation strategies and result in more successful SC projects. This research explores SC implementation risks in two cities currently undergoing a SC transformation in India – Kakinada and Kanpur. While examining the risks landscape in these two cities, the research also explores what city officials are focused on when implementing SC projects. This research finds that: 1) implementation risks such as Institutional, Resource and Partnership, and Social are crucial for implementing SC projects; 2) in the cities of Kakinada and Kanpur, Institutional risks that relate to gaps and deficiencies in local urban governance such as overlapping functions of multiple local urban development agencies, have causal linkages with other risks such as Resource and Partnership risks and Financial risks, which further delay project implementation; and 3) city officials and industry professionals implementing SC projects in Kakinada and Kanpur have a slightly different perspective on smartness, however both the groups focus on External smartness of the city – i.e., projects related to physical infrastructure such as mobility and sanitation – rather than Internal smartness of the city – i.e., strengthening local urban governance, increasing citizen engagement, etc. Overall, this research proposes that there is a need to frame the concept of a SC around both Internal and External Smartness of the city. This research will be of special interest to: 1) cities (in both developed and developing nations) currently implementing SC projects by providing a framework to systematically examine the risk landscape for successful project implementation; and 2) communities/institutions (especially in developing nations) proposing SC initiatives by helping them focus on components, goals, and enablers of a SC.
- A systematic review of scientific literature on accessibility measurements and the treatment of automated vehiclesMo, Fan (Virginia Tech, 2020-02-05)Accessibility plays an important role in a number of scientific fields, and significant advances in measuring accessibility have been made over the past two decades. However, since the comprehensive review of accessibility measures conducted by Geurs and van Wee in 2004, no attempt has been made to update their study. In addition, the emergence of Automated Vehicles (AVs) is expected to dramatically impact accessibility. Therefore, based on the relevant assessment criteria proposed by Geurs and van Wee (2004) (i.e., theoretical basis, interpretability, operationalization, and usability), this research reviews: (1) progress made over the past two decades on measuring accessibility; and (2) how accessibility measures have incorporated the impacts of AVs. A total of 495 papers and books were identified through a search of Scopus, Web of Science, and EBSCOhost in May 2019. The results found that the existing accessibility measures have been further refined, and new measures have been created by leveraging more advanced behavior theories and/or models. In addition, the operationalization of almost all of the measures has become easier due to more readily available data and more advanced implementation tools. As a result of these changes, accessibility measures are becoming more usable and can more accurately assess social, economic, and environmental impacts. However, the interpretation of these measures is becoming more difficult due to the incorporation of more complicated theories and models. Interestingly, very few papers discussed AVs in the context of accessibility measures. Finally, as a result of this study, future research opportunities are identified.
- Using Mobile Monitoring and Vehicle Emissions to Develop and Validate Machine Learning Empirical Models of Particulate Air PollutionAlazmi, Asmaa Salem (Virginia Tech, 2021-08-18)Increasing levels of air pollution are prompting researchers to develop more reliable air pollution modeling approaches in order to protect the public and the environment from toxic contaminants and airborne pathogens. Although land use regression has long been used to assess exposure to air pollution, researchers are increasingly using machine learning algorithms to quantify the concentration of harmful pollutants—for this study black carbon (BC) and particle number (PN). Additionally, researchers are moving away from using fixed-site data in favor of using mobile monitoring data in a variety of locations to develop hourly empirical models of particulate air pollution. This study uses secondary data describing BC and PN pollutant levels, which are obtained from roads that bikers share in the more rural location of Blacksburg (VA). Machine learning (ML) algorithms are then built to develop accurate and reliable short-term empirical prediction models. Different pre-processing methods for the mobile monitoring data and various input variables are tested to assess how ML can be used effectively in this process. Three types of time-average models are developed (daytime, hourly average, and one second models). Various combinations of spatial and temporal input variables are used in the short-term models. The impact of adding more spatiotemporal variables (e.g., emissions) to machine learning models to improve model performance is assessed in the short-term models. Incorporating spatial and temporal autocorrelation is intended to develop more sophisticated validation approaches for identifying ML performance patterns—the goal of which is to predict concentration levels more accurately in comparison to using raw data without data reprocessing. The results show that the model developed using refined disaggregated data is able to detect the spatial distribution of the pollutant concentration at equivalent levels as the smoothed data models, although the latter display fewer errors. The performance of the short-term model including all variables is equivalent to the model omitting emissions. The ML results are compared to earlier stepwise regression model results, suggesting that ML has the ability to improve both long-term and short-term model accuracy. Our findings indicate that ML demonstrates higher predictive capacity in comparison to stepwise regression. The results from this study may be useful in enhancing the performance of ML through the incorporation of different data preprocessing tasks, as well as showing how different input variables contribute to the ML modeling process. The findings from this study could be used toward the development of environmental/eco-friendly routes that would decrease the risk for exposure to harmful vehicle-related emissions.