Durations of Dockless E-Scooter Trips Before and During the COVID-19 Pandemic in Austin, TX: An Analysis Using Hazard-Based Duration Models.

The pandemic arising from the 2019 coronavirus disease has significantly affected all facets of human life across the world, including economies and transportation systems, thereby changing people's travel behaviors. This research was aimed at exploring the relationship between socio-economic factors and e-scooter trip durations before and during the pandemic. We developed a hazard-based duration approach and estimated multiple spatial and non-spatial models on the basis of 2019 and 2020 dockless e-scooter data collected from the City of Austin's Open Data Portal. The results indicated an overall increase in e-scooter trip durations after the pandemic. Moreover, analysis of variables revealed potential changes in users' behavior before and during the pandemic. In particular, whereas e-scooter trip durations were found to be positively associated with aggregate travel time to work before the pandemic, this trend was reversed during the pandemic. In addition, during the pandemic, e-scooter travel time was positively correlated with the ratio of individuals with bachelor's degrees or greater to those with associate degrees or lower. However, no specific pattern was observed before the pandemic. Lastly, the results showed the presence of disparities within the study area; therefore, it is vital to extend e-scooter service areas to cover underserved communities.

Identifying Hospital Deserts in Texas Before and During the COVID-19 Outbreak.

In this study, we proposed a GIS-based approach to analyzing hospital visitors from January to June 2019 and January to June 2020 with the goal of revealing significant changes in the visitor demographics. The target dates were chosen to observe the effect of the first wave of COVID-19 on the visitor count in hospitals. The results indicated that American Indian and Pacific Islander groups were the only ones that sometimes showed no shift in visitor levels between the studied years. For 19 of the 28 hospitals in Austin, TX, the average distance traveled to those hospitals from home increased in 2020 compared with 2019. A hospital desert index was devised to identify the areas in which the demand for hospitals is greater than the current hospital supply. The hospital desert index considers the travel time, location, bed supply, and population. The cities located along the outskirts of metropolitan regions and rural towns showed more hospital deserts than dense city centers.

Disparities in the Impacts of the COVID-19 Pandemic on Public Transit Ridership in Austin, Texas, U.S.A.

The COVID-19 pandemic has disrupted day-to-day lives and infrastructure across the United States, including public transit systems, which saw precipitous declines in ridership beginning in March 2020. This study aimed to explore the disparities in ridership decline across census tracts in Austin, TX and whether demographic and spatial characteristics exist that are related to these declines. Transit ridership data from the Capital Metropolitan Transportation Authority were used in conjunction with American Community Survey data to understand the spatial distribution of ridership changes caused by the pandemic. Using a multivariate clustering analysis as well as geographically weighted regression models, the analysis indicated that areas of the city with older populations as well as higher percentages of Black and Hispanic populations were associated with less severe declines in ridership, whereas areas with higher unemployment saw steeper declines. The percentage of Hispanic residents appeared to affect ridership most clearly in the center of Austin. These findings support and expand on previous research that found that the impacts of the pandemic on transit ridership have emphasized the disparities in transit usage and dependence across the United States and within cities.

Impacts of COVID-19 on bike-sharing usages in Seoul, South Korea.

The COVID-19 pandemic and social distancing restrictions have had a significant impact on urban mobility. As micro mobility offers less contact with other people, docked or dockless e-scooters and bike-sharing have emerged as alternative urban mobility solutions. However, little empirical research has been conducted to investigate how COVID-19 might affect micro mobility usage, especially in a major Asian city. This research aims to study how COVID-19 and other related factors have affected bike-sharing ridership in Seoul, South Korea. Using detailed urban telecommunication data, this study explored the spatial-temporal patterns of a docked bike-sharing system in Seoul. Stepwise negative binomial panel regressions were conducted to find out how COVID-19 and various built environments might affect bike-sharing ridership in the city. Our results showed that open space areas and green infrastructure had statistically significant positive impacts on bike-sharing usage. Compared to registered population factors, real-time telecommunication floating population had a significant positive relationship with both bike trip count and trip duration. The model showed that telecommunication floating population has a significant positive impact on bike-sharing trip counts and trip duration. These findings could offer useful guidelines for emerging shared mobility planning during and after the COVID-19 pandemic.

Cities reshaped by Airbnb: A case study in New York City, Chicago, and Los Angeles.

In the last 10 years, Airbnb has rapidly grown from a simple, online bed and breakfast operation to a leading global hospitality service provider. Scholars have been using different spatial analysis tools to study its potential impacts on cities. To better understand Airbnb's impact this featured graphic applied a cartogram processing tool to reshape census tracts based on Airbnb listing intensity in three major US cities (New York City, Chicago, and Los Angeles). Results showed that different cities have different patterns of Airbnb listings. Census tracts in New York City became completely unrecognizable after the analysis, which indicted a highly skewed Airbnb distribution in the city. Compared with New York City, we saw less and least deformation in Chicago and Los Angeles, respectively, where Airbnb was more evenly distributed. The results showed that Airbnb listings were very evenly distributed in the large US cities. Airbnb would impose completely different impacts on different neighborhoods based on their locations.

An empirical analysis of Airbnb listings in forty American cities.

In the past ten years, Airbnb has rapidly grown from a small, online bed and breakfast product to a leading peer-to-peer hospitality magnate which operates in eighty thousand cities globally. It now offers rooms-for-rent, entire houses for rent, and even allows people to book 'experiences' through the platform. Consequently, cities, researchers, and the concerned public are focusing more on its impacts and exploring viable ways to regulate and facilitate the business while minimizing its potentially negative effects. To better understand Airbnb's operation in US cities, this paper explored how demographics, socioeconomics, and transportation might affect Airbnb listings in forty US cities. The results showed that Airbnb rentals were more likely to locate in neighborhoods with good transit service, short distances to the city center, and high median house value and household income. This study indicated the possible social inequality risk in the shared economy.

Crowdsourcing Bike Share Station Locations: Evaluating Participation and Placement.

Problem research strategy and findings: Planners increasingly involve stakeholders in co-producing vital planning information by crowdsourcing data using online map-based commenting platforms. Few studies, however, investigate the role and impact of such online platforms on planning outcomes. We evaluate the impact of participant input via a public participation geographic information system (PPGIS), a platform to suggest the placement of new bike share stations in New York City (NY) and Chicago (IL). We conducted 2 analyses to evaluate how close planners built new bike share stations to those suggested on PPGIS platforms. According to our proximity analysis, only a small percentage of built stations were within 100 feet (30m) of suggested stations, but our geospatial analysis showed a substantial clustering of suggested and built stations in both cities that was not likely due to random distribution. We found that the PPGIS platforms have great promise for creating genuine co-production of planning knowledge and insights and that system planners did take account of the suggestions offered online. We did not, however, interview planners in either system, and both cities may be atypical, as is bike share planning; moreover, multiple factors influence where bike stations can be located, so not all suggested stations could be built. Takeaway for practice: Planners can use PPGIS and similar platforms to help stakeholders learn by doing and to increase their own local knowledge to improve planning outcomes. Planners should work to develop better online participatory systems and to allow stakeholders to provide more and better data, continuing to evaluate PPGIS efforts to improve the transparency and legitimacy of online public involvement processes.

Deciphering the stroke-built environment nexus in transitional cities: Conceptual framework, empirical evidence, and implications for proactive planning intervention.

Adverse lifestyle-associated health outcomes, and stroke in particular, have been aggravated in transitional countries under high-speed urbanisation. Against this backdrop, deciphering the nexus between built environments (BEs) and lifestyle-associated health outcomes is of importance for crafting proactive interventions. The existing literature on this topic, however, fails to sufficiently capture the multiplicity of health-related BEs and, in turn, the complexity of such a nexus, largely challenging the applicability of established frameworks and the reliability of relevant findings. Looking at the case of stroke in Wuhan, China, this research aims to flesh out the understanding of the nexus between multidimensional BEs and lifestyle-associated health outcomes in transitional cities, with regards to conceptual framework and empirical evidence. To this end, we clarified stroke-related BE elements and integrated them into one conceptual framework. We then visualised stroke risk and examined its BE determinants using the Bayesian conditional autoregressive model. The visualisation results showed that stroke risks exhibited significant clustering in the high-density urban core. The statistical analysis found that, after the data were controlled for sociodemographic characteristics, net population density and building density were positively associated with stroke risk. In contrast, an abundance of public parks and institutional land use and access to medical care facilities have presented negative correlations with stroke risk, regardless of urban density. Our research reveals that compact urban developments might not be a silver bullet for health promotion in transitional cities, calling for an urgent need to scrutinise their applicability. To offset the adverse effects of increasingly dense urban environments, more efforts should also be made to provide better access to the identified salubrious resources. Furthermore, we argue that the establishment of comprehensive conceptual frameworks that connect BEs and lifestyle-associated health outcomes deserves to be highlighted in further research, planning intervention schemes, and health impact assessment projects.

How does 'park and ride' perform? An evaluation using longitudinal data.

The efficiency of park and ride (PnR) lots has not been investigated in serious depth in prior literature. This study examines the effect of various factors on the utilization rate of PnR lots with panel Tobit models. The examined factors consist of land use features, roadway design features, transit ridership, sociodemographic attributes, travel characteristics, policy tools, gasoline prices, and weather conditions. The data is drawn from PnR lots in King County, Washington. Results show that: (1) degree of mixed land use, road density, employment density, percentages of people aged between 18 and 34 and people over 65, the percentage of white people, the percentage of poor people, and transit ridership are positively associated with the utilization rate of PnR lots; (2) the percentage of drive lanes in total roadway miles, the percentage of males, and the mode share percentage of driving are negatively correlated with the utilization rate of PnR lots; (3) various policy interventions, including countermeasures for preserving transit after the economic recession, congestion reduction charge, and bus-rail integration, are all positively correlated with the utilization rate of PnR lots. Contextualized to US cities, PnR is a practical way to attract bus riders, especially young adults, senior citizens, and low-income people to public transit. Dense urban development is encouraged for the full utilization of PnR lots. Additionally, the integration between bus and rail appears to be an effective policy tool to promote PnR utilization.

The Geography and Equity of Crowdsourced Public Participation for Active Transportation Planning.

Transportation planners increasingly use new forms of online public participation alongside traditional in-person approaches, including crowdsourcing tools capable of encouraging geographically specific input. Digital involvement may be particularly valuable in exploring methods to plan at a megaregional scale. Research is beginning to address digital inequalities, recognizing that broadband and smartphone access may restrict opportunities for disadvantaged groups. However, the geography and equity of participation remain pragmatic issues for practice and research. This paper reviews the geography and equity of the participation methods in Austin, Texas for active transportation (bicycling and pedestrian) through three approaches to co-produce informed plans: in-person meetings, public participation geographic information system (PPGIS), and an emerging smartphone platform that logs trips and encourages input on route quality. In addition to spatial analysis with standard deviational ellipses, we include qualitative case analysis to contextualize the geographic and equity implications of different participation approaches. Results show that both online techniques resulted in a larger geography for participation than in-person meetings, with the regional PPGIS covering the most area. However, review of the income levels in each area shows that use of the smartphone-based crowdsourcing platform was aligned with lowest-income areas. This study shows that online participation methods are not homogeneous regarding geography or equity. In some contexts, smartphone applications can help reach lower-income communities, even when compared with in-person meetings. Crowdsourcing tools can be valuable approaches to increase geography and equity of public participation in transportation planning.

Impact of Neighborhood Built Environments on Shopping Travel Modes in Shanghai, China.

Walking is one of the most widely used means of transport. Neighborhood built environments have a direct influence on individuals' daily commuting, recreational travel patterns, and shopping travel behavior. In Chinese cities, shopping activities are among the most frequent reasons for daily travel. Yet, research on the impact of neighborhood built environments on people's shopping travel activities in high-density cities is limited. To fill this research gap, this study investigates how neighborhood built environments might affect pedestrians' shopping travel activities in Shanghai, China. The data, which includes shopping travel patterns, perceived environmental characteristics, and individual socioeconomic status, were collected from a survey of 21 randomly selected neighborhoods in Shanghai in 2011. In total, data from 2,838 samples (participants) were collected. Multinomial logistic regression was used to investigate how neighborhood built environments affect residents' choice of travel mode for shopping, that is, the likelihood of taking transit, driving, or biking vs. walking. Results showed that nearly half of people surveyed (43.3%) used walking as their primary shopping mode. Road network density, presence of primary schools, and average sidewalk width were positively correlated with the likelihood of using walking as the primary shopping mode. Gender, age, and car ownership were also significant in the model.

Promoting public bike-sharing: A lesson from the unsuccessful Pronto system.

In 2014, Seattle implemented its own bike-sharing system, Pronto. However, the system ultimately ceased operation three years later on March 17th, 2017. To learn from this failure, this paper seeks to understand factors that encourage, or discourage, bike-sharing trip generation and attraction at the station level. This paper investigates the effects of land use, roadway design, elevation, bus trips, weather, and temporal factors on three-hour long bike pickups and returns at each docking station. To address temporal autocorrelations and the nonlinear seasonality, the paper implements a generalized additive mixed model (GAMM) that incorporates the joint effects of a time metric and time-varying variables. The paper estimates models on total counts of pickups and returns, as well as pickups categorized by user types and by location. The results clarify that effects of hilly terrain and the rainy weather, two commonly perceived contributors to the failure. Additionally, results suggest that users in the University District, presumably mostly university students, tend to use shared bikes in neighborhoods with a higher household density and a higher percentage of residential land use, and make bike-sharing trips regardless workdays or non-workdays. The paper also contributes to the discussion on the relationship between public transportation service and bike-sharing. In general, users tend to use bike-sharing more at stations that have more scheduled bus trips nearby. However, some bike-sharing users may shift to bus services during peak hours and rainy weather. Several strategies are proposed accordingly to increase bike ridership in the future.

Promoting active student travel: A longitudinal study.

This study investigates the effects of sidewalk modification and bike lane accommodation on students' active travel to schools. The modeling framework assumes that a student's choice for the mode of travel to school is impacted by numerous factors such as neighborhood crime rates, traffic safety, built environment amenities, and socio-demographic factors. A generalized linear model is employed to capture longitudinal changes in the mode share of students who walk or bike to school based on data collected from 53 schools in the city of Seattle, Washington. The modeling results indicate that (1) enhanced sidewalk modifications and bike lane accommodations encourage students walking and biking to school; (2) the implementation of Seattle's student assignment plan helps promote students walking to school possibly due to the change from school choice to neighborhood-based school assignment; (3) the size of the school attendance area is not significantly correlated with students' active travel activities, while the size of school enrollment is negatively associated with walking; (4) in school areas with high employment density, biking to school may be a more attractive option for students; (5) greater crosswalk density may encourage more students to walk to school; (6) the density of bike crashes is negatively associated with students biking to school. In terms of policy implications, transport planners should continually promote walking and biking supportive environments and implement policies to encourage active student travel.

Evaluating the relationship between rail transit and industrial agglomeration: An observation from the Dallas-fort worth region, TX.

Recent studies have suggested that rail transit not only facilitates urban growth but also promotes urban agglomeration. Yet research that links industrial agglomeration with rail transit is scant-what types of industries are likely to cluster near rail stations? To what extent can rail transit access be seen as having an influence on industrial agglomeration? And how do these interactions vary as rail transit proximity increases? To answer these and related questions, we investigate the relationship between industrial agglomeration and rail transit in the Dallas-Fort Worth metropolitan area using the Longitudinal Employer Dynamics (LEHD) employment data from 2014 at the census block level. First, we use the Local Indicator of Spatial Association statistics (LISA) tests to identify industrial agglomeration patterns within the study area. We then use logistics models to reveal the relationship between rail transit proximity and industrial agglomeration. Our study finds that the impacts of rail transit on industrial agglomeration, in terms of magnitudes and signs, are mixed across industries. The varying results suggest that the benefits of rail transit access exhibit considerable demand from certain industry sectors including the manufacturing, knowledge, and services industries, while exerting weaker forces in pulling agglomeration in its immediate environs among other industries (including the retail trade sector). In practice, these results are useful for justifying evidence-based rail transit planning.

Revisiting Image of the City in Cyberspace: Analysis of Spatial Twitter Messages During a Special Event.

This research investigated people's communication of urban space as reflected in Twitter messages (tweets) during the 2012 Super Bowl. The authors archived over 600,000 tweets related to the Super Bowl from January 23 through early February 6. The authors identified 78 Indianapolis-area places or routes named in the tweets. Based on occurrence of these terms, the authors retained 9,103 city-specific messages for analysis. The frequency of such tweets changed over the two-week period and peaked two days before game day. Instances of all of Lynch's (1960) The Image of the City elements (node, district, landmark, path, and edge) were found in the tweets. While node-referencing terms were most common among the 78 spatial identifiers, district and landmark references were most common in the tweet sample. Edge references were almost non-existent and only occurred as named waterways. This research has implications for city-oriented social media monitoring efforts for future special events.

Access to supermarkets and fruit and vegetable consumption.

OBJECTIVES: We examined whether supermarket choice, conceptualized as a proxy for underlying personal factors, would better predict access to supermarkets and fruit and vegetable consumption than mere physical proximity. METHODS: The Seattle Obesity Study geocoded respondents' home addresses and locations of their primary supermarkets. Primary supermarkets were stratified into low, medium, and high cost according to the market basket cost of 100 foods. Data on fruit and vegetable consumption were obtained during telephone surveys. Linear regressions examined associations between physical proximity to primary supermarkets, supermarket choice, and fruit and vegetable consumption. Descriptive analyses examined whether supermarket choice outweighed physical proximity among lower-income and vulnerable groups. RESULTS: Only one third of the respondents shopped at their nearest supermarket for their primary food supply. Those who shopped at low-cost supermarkets were more likely to travel beyond their nearest supermarket. Fruit and vegetable consumption was not associated with physical distance but, with supermarket choice, after adjusting for covariates. CONCLUSIONS: Mere physical distance may not be the most salient variable to reflect access to supermarkets, particularly among those who shop by car. Studies on food environments need to focus beyond neighborhood geographic boundaries to capture actual food shopping behaviors.

Developing a transit desert interactive dashboard: Supervised modeling for forecasting transit deserts.

Transit deserts refer to regions with a gap in transit services, with the demand for transit exceeding the supply. This study goes beyond merely identifying transit deserts to suggest actionable solutions. Using a multi-class supervised machine learning framework, we analyzed factors leading to transit deserts, distinguishing demand by gender. Our focus was on peak-time periods. After assessing the Support Vector Machine, Decision Tree, Random Forest, and K-nearest Neighbor, we settled on the Random Forest method, supported by Diverse Counterfactual Explanation and SHapley Additive Explanation in our analysis. The ranking of feature importance in the trained Random Forest model revealed that factors such as density, design, distance to transit, diversity in the built environment, and sociodemographic characteristics significantly contribute to the classification of transit deserts. Diverse Counterfactual Explanation suggested that a reduction in population density and an increase in the proportion of green open spaces would likely facilitate the transformation of transit deserts into transit oases. SHapley Additive Explanation highlighted the differential impact of various features on each identified transit desert. Our analysis results indicate that identifying transit deserts can vary depending on whether the data is aggregated or separated by demographics. We found areas that have unique transit needs based on gender. The disparity in transit services was particularly pronounced for women. Our model pinpointed the core elements that define a transit desert. Broadly, to address transit deserts, strategies should prioritize the needs of disadvantaged groups and enhance the design and accessibility of transit in the built environment. Our research extends existing analyses of transit deserts by leveraging machine learning to develop a predictive model. We developed a machine learning-powered interactive dashboard. Integrating participatory planning approaches with the development of an interactive interface could enhance ongoing community engagement. Planning practices can evolve with AI in the loop.

Testing the Capability of AI Art Tools for Urban Design.

This study aimed to evaluate the performance of three artificial intelligence (AI) image synthesis models, Dall-E 2, Stable Diffusion, and Midjourney, in generating urban design imagery based on scene descriptions. A total of 240 images were generated and evaluated by two independent professional evaluators using an adapted sensibleness and specificity average metric. The results showed significant differences between the three AI models, as well as differing scores across urban scenes, suggesting that some projects and design elements may be more challenging for AI art generators to represent visually. Analysis of individual design elements showed high accuracy in common features like skyscrapers and lawns, but less frequency in depicting unique elements such as sculptures and transit stops. AI-generated urban designs have potential applications in the early stages of exploration when rapid ideation and visual brainstorming are key. Future research could broaden the style range and include more diverse evaluative metrics. The study aims to guide the development of AI models for more nuanced and inclusive urban design applications, enhancing tools for architects and urban planners.

Hourly Associations between Heat Index and Heat-Related Emergency Medical Service (EMS) Calls in Austin-Travis County, Texas.

This paper aims to investigate the following research questions: (1) what are the hourly patterns of heat index and heat-related emergency medical service (EMS) incidents during summertime?; and (2) how do the lagged effects of heat intensity and hourly excess heat (HEH) vary by heat-related symptoms? Using the hourly weather and heat-related EMS call data in Austin-Travis County, Texas, this paper reveals the relationship between heat index patterns on an hourly basis and heat-related health issues and evaluates the immediate health effects of extreme heat events by utilizing a distributed lag non-linear model (DLNM). Delving into the heat index intensity and HEH, our findings suggest that higher heat intensity has immediate, short-term lagged effects on all causes of heat-related EMS incidents, including in cardiovascular, respiratory, neurological, and non-severe cases, while its relative risk (RR) varies by time. HEH also shows a short-term cumulative lagged effect within 5 h in all-cause, cardiovascular, and non-severe symptoms, while there are no statistically significant RRs found for respiratory and neurological cases in the short term. Our findings could be a reference for policymakers when devoting resources, developing extreme heat warning standards, and optimizing local EMS services, providing data-driven evidence for the effective deployment of ambulances.

Bike-Sharing Station Usage and the Surrounding Built Environments in Major Texas Cities.

This study analyzes the effect of different built environments on bike-share usage in nascent dock-based systems in three Texas cities. Past research offers little insight as to whether elements associated with higher bicycle usage in major cities affect ridership in secondary, developing bike-share markets within lower density American cities. In Austin and Houston, a positive relationship emerges between bike-share usage and proximity to high-comfort bicycle facilities. All three cities demonstrated surprisingly minimal relationship between bike-share usage and other proven drivers of bicycling activity in urban areas, which may result from system design for leisure- and recreation-based trips.