Sustainable, Equitable, and Resilient Mobility System
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 to Diseases 🚌🚌🚌🚲🚲🚲
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.
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.
Link-level Road Traffic Resilience to Extreme Weather ❄️❄️❄️🚧🚧🚧
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.
Crowdsourced data offer new opportunities to monitor and investigate changes in road traffic flow during extreme weather. We utilize diverse crowdsourced data from mobile devices and the community-driven navigation app, Waze, to examine the impact of three weather events – floods, winter storms, and fog – on road traffic. We find overall winter storms have the greatest impact on road traffic, while at a link level, lower-class roads with lower average speeds and volumes experience milder impact.
