Resilient Mobility and Community

Unexpected events, such as natural disasters, diseases, and extreme weather events, lead to substantial impacts on human travel. My work focuses on quantitatively examining the time-varying patterns of human travel before, during, and after these abnormal interventions. My particular attention to accessibility inequities, policy barriers, and resilient disparities in underserved communities under unusual interventions provides valuable suggestions for building a more sustainable, equitable, and resilient urban mobility system.

Multi-modal Travel Demand Resilience 🚌🚌🚌🚲🚲🚲

[TR Part C] Using location data of over 150 million monthly active mobile devices in the US, we successfully measure how human mobility changed during the COVID-19 pandemic using three metrics: average trips per person, average person-miles traveled, and percentage of residents staying home. Data are updated daily and made publicly available via an online platform.

[TR Part D] The COVID-19 pandemic has led to a globally unprecedented decline in transit ridership. We leverage the 20-year daily transit ridership data in Chicago to infer the impact of COVID-19 on ridership using the Bayesian structural time series model, controlling confounding effects of socioeconomic disparities. Results show that ridership declined more in regions with more commercial lands and higher percentages of white, educated, and high-income individuals.

[JTG] Leveraging two-year bikesharing trips in Chicago, we examine the spatiotemporal evolution of bikesharing usage across the pandemic and compare it with other travel modes. We find that bikesharing is more resilient compared with transit, driving, and walking. Deep socio-economic inequities also exist: stations located in high-income areas go from more increase before the pandemic to more decrease during the pandemic.

[J. R. Soc. Interface] By analyzing the mobile device location data during the pandemic, we find a spontaneous mobility reduction that occurred regardless of government actions and a ‘floor’ phenomenon, where human mobility reached a lower bound and stopped decreasing soon after each state announced the stay-at-home order. We also document that the states' stay-at-home policies have only led to about a 5% reduction in average human mobility.

Road Network Traffic Resilience ❄️❄️❄️🚧🚧🚧

[IEEE ITSC] Heavy rain can induce road flooding, especially in the urban areas due to poor drainage systems. Effectively identifying the flooding road segment can help people plan their travel reasonably and reduce losses. Combining social media posts, precipitation, and traffic flow information, we develop an automatic road flooding detection algorithm and deploy it to Shenzhen City, China. Result shows that our algorithm performs satisfactorily with a 68%–90% detection rate and a 1.5%–2% false alarm rate.

[IJDRR] Crowdsourced data offer new opportunities to monitor and investigate changes in road traffic flow during extreme weather. We use two types of crowdsourced data: passively collected location data (PCLD) from mobile devices and actively collected user reports (ACUR) from the WAZE app, to examine the impact of floods, winter storms, and fog on road traffic. We find that ACUR may not accurately reflect the actual impact during extreme weather events, as few users are able to travel outside to actively act as “sensors”.