ASPIRE: A Method for Quantitatively Rating Transportation Methods in U.S. Cities

With the growing reliance on mass transportation in urban areas, the effects of climate change on cities become more pronounced. To avoid pollution and congestion from motorized transportation, investing in walking is crucial for achieving transportation-based climate goals. This paper aims to develop a comprehensive, quantitative model capable of evaluating three transportation modes (walking, cycling, and public transportation) in urban environments. Using a deep learning model and a data collection pipeline, ASPIRE gathers information on a U.S. city’s geography, environment, safety, aesthetics, and population. Early analysis has assessed the model’s accuracy and established a framework to identify underdeveloped mode infrastructure from a climate perspective. This model can quantitatively evaluate the environmental impact of infrastructure priorities in cities and guide urban planning.