This paper investigated the travel patterns of 1.7 million shared E-scooter trips from April 2018 to February 2019 in Austin, TX. There were more than 6000 active E-scooters in operation each month, generating over 150,000 trips and covered approximately 117,000 miles. During this period, the average travel distance and operation time of E-scooter trips were 0.77 miles and 7.55 min, respectively. We further identified two E-scooter usage hotspots in the city (Downtown Austin and the University of Texas campus). The spatial analysis showed that more trips originated from Downtown Austin than were completed, while the opposite was true for the UT campus. We also investigated the relationship between the number of E-scooter trips and the surrounding environments. The results show that areas with higher population density and more residents with higher education were correlated with more E-scooter trips. A shorter distance to the city center, the presence of transit stations, better street connectivity, and more compact land use were also associated with increased E scooter usage in Austin, TX. Surprisingly, the proportion of young residents within a neighborhood was negatively correlated with E-scooter usage.

This study revealed the inequitable societal impacts of E-scooters on disadvantaged populations. The study conducted a population distribution analysis to compare the use opportunities and space intrusion burdens of E-scooter sharing on four vulnerable population groups in Austin, Texas. Nearly all minority population experienced fewer E-scooter use opportunities. Ten percent of the minority population waited for a disproportionately longer time before a disturbance could be resolved. Ten percent of the low-income population were in a disadvantaged position in high opportunities and moderate burdens. Twenty percent of the physically disabled population faced more moderate-level burdens. The result did not show significant inequitable outcomes for the elderly population.

Dockless electric scooter (E-scooters) services have emerged in the United States as an alternative form of micro transit in the past few years. With the increasing popularity of E-scooters, it is important for cities to manage their usage to create and maintain safe urban environments. However, E-scooter safety in U.S. urban environments remains unexplored due to the lack of traffic and crash data related to E-scooters. Our study objective is to better understand E-scooter crashes from a street network perspective. New parcel level street network data are obtained from Zillow and curated in Geographic Information System (GIS). We conducted local Moran’s I and independent Z-test to compare where and how the street network that involves E-scooter crash differs spatially with traffic incidents. The analysis results show that there is a spatial correlation between E-scooter crashes and traffic incidents. Nevertheless, E-scooter crashes do not fully replicate characteristics of traffic incidents. Compared to traffic incidents, E-scooter incidents tend to occur adjacent to traffic signals and on primary roads.

The novel coronavirus (COVID-19) is currently being regarded worldwide as a global pandemic, with New York City as one of the epicenters. There is an emerging demand for precise investigation on this disease, specifically how to slow outbreaks and reopen responsibly. This paper explores and models the spatial factors of COVID-19 in New York City through ordinary least squares regression and geographically weighted regression. Results indicate medical density, green space density, mean distance traveled, male percentage, and commuting (walking, carpooling, and public transit) could correlate to higher rates of COVID-19 positive cases. In contrast, areas with high percentages working from home and white only could correlate to lower rates of COVID-19 positive cases. Additionally, there are distinct associations in various zip code areas or clusters. Overall, this study suggests that public sanitation is critical in disease control in areas with high public transportation demand, and the effect of travel reduction is significant in delaying the outbreak. This study advises policymakers to implement unique policies, preventions measures, and reopening strategies based on localized situations considering COVID-19 outbreaks.

In recent years, many progressive U.S. cities have witnessed the rapid popularization of dockless small vehicles as a car-free travel alternative to meet the short distance travel demand. The research gap exists in revealing the social outcome of the massive influx of shared small vehicles on public space. To that end, this study analyzed 4,100 parking violation reports in Austin, Texas, crowdsourced from the Austin 311 non-emergency service request system. The results showed that sidewalk and other public space intrusions were the two most frequently reported violations. Additionally, it found that improperly parked vehicles in parks required the longest time to be cleaned. Among the three reporting methods included in this study, 91% were submitted through smartphone applications, compared to 5% by phone calls and 2% through the web interface. The response time of smartphone reports was significantly greater than that of phone call reports (17.4 hours vs. 2.5 hours). Finally, the GIS hotspot analysis showed that university campus and downtown were both violation clusters, yet campus violations were solved more quickly. This study proposed a shared responsibility framework of key players in shared micro-mobility management and suggested using crowdsourcing 311 system data to facilitate communications between stakeholders.