Speakers
Description
Global climate change has led to an increase in extreme heat events, affecting urban mobility, particularly bike-sharing systems, which are crucial for sustainable urban development. Although the built environment has a considerable influence on bike-sharing usage, there is limited research on its impact on urban mobility resilience (UMR) of bike-sharing. This study investigates the effect of extreme heat on UMR in Shanghai's urban center, utilizing machine learning techniques, specifically the LightGBM model, SHAP explainability model, and Bayesian parameter optimization. The research explores how the built environment and other factors shape UMR under extreme heat conditions.
A novel quantitative method was developed to measure UMR, using the ratio of bike usage during extreme heat to normal conditions in a spatial unit. The model demonstrated significant explanatory power, with R² values of 73.5% on weekdays and 63.7% on weekends, reliably explaining UMR variations in bike-sharing systems. The results indicate that extreme heat has a more severe negative effect on UMR during weekends, as weekend trips are more leisure-oriented and thus more vulnerable to heat. Additionally, UMR in peripheral urban areas is lower compared to the city center, with high UMR areas in the city center showing a clustered distribution.
In terms of mechanisms, the study found followings: (1) Development intensity and land use diversity have a positive impact on UMR, especially on weekdays. For example, increasing building density (from 0 to 0.3) significantly boosts UMR by nearly 0.1. Similarly, increasing floor area ratio and building height on weekdays also leads to substantial improvements in UMR; (2) The importance of a well-developed public transportation network and infrastructure is emphasized. Proximity to metro stations (within 1,000-1,500 meters) enhances UMR, and bus stop density plays a crucial role in increasing resilience; (3) The reasonable concentration of population and economic activity is vital for enhancing UMR, particularly on weekends. Population density positively affects resilience up to a threshold of 20,000 people per km2, beyond which the impact diminishes. Economic agglomeration on weekends contributes more than 0.2 to resilience, while a reasonable population concentration increases resilience by over 0.05.
The study challenges some conventional views. Increasing road density does not always enhance resilience. In fact, higher road density can reduce UMR by up to 0.1 on weekdays. Similarly, while greening is often considered beneficial, it can lower UMR by up to 0.15 on weekends, indicating the need for more nuanced urban planning approaches.
From a policy perspective, the study offers several key recommendations. High-density development areas, such as commercial and residential zones, tend to show higher UMR under extreme heat, suggesting that promoting moderate high-density development in urban centers is a viable strategy. However, it must be balanced with careful attention to green space optimization and microclimate design. Transportation planners should focus not only on increasing road density but also on ensuring the accessibility of public transportation systems, particularly integrating high-density development areas within a reasonable distance of transportation hubs. Special cycling paths and slow-moving transportation networks should be developed to encourage bike-sharing, reduce barriers to cycling, and improve overall urban mobility resilience. Additionally, urban planning should prioritize functional diversity through mixing land use and the population and economy should be moderately agglomerated.
In conclusion, this research provides a comprehensive framework for assessing and enhancing UMR under extreme heat conditions. The study offers valuable insights for urban planners and policymakers, providing a robust tool for improving UMR in the context of climate change.
References
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| Keywords | Urban mobility resilience; Climate change; Extreme heat; Built environment; Bike-Sharing |
|---|---|
| Best Congress Paper Award | Yes |
