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Improving the built environment to support walking is a popular strategy to increase urban sustainability and walkability. In the past decade alone, many US cities have implemented crosswalk visibility enhancement programs as part of road safety improvements and active transportation plans. However, there are no systematic ways of measuring and monitoring the presence of key built environment attributes that influence the safety and walkability of an area, such as marked crosswalks. Furthermore, little is known about how these attributes change over time at a national scale. In this paper, we introduce an innovative approach using a deep learning-based computer vision model on Street View images to identify changes in intersection-level marked crosswalks around more than 4,000 US transit stations over a 14-year period. We found an increase in the overall number of marked crosswalks at intersections. Furthermore, high-visibility crosswalks became more common, as they replaced existing parallel-line crosswalks. We further examine crosswalks around transit stations in New York City and San Francisco to illustrate geographic variations and compare associations with other characteristics of the built environment as reported in the Smart Location Database. Areas with increases in high-visibility crosswalks focused on high density residential areas and areas with a higher percent of zero-vehicle households. However, geographic variations exist. For example, in San Francisco, transit station areas outside downtown or major corridors (South and Southwest of the city) had the lower prevalence of marked crosswalks. This analysis confirms important gaps in crosswalk visibility that call for safety enhancements and opens the door for additional research involving these data. We conclude by discussing the limitations and future research opportunities using computer vision to automatically detect large-scale transportation infrastructure changes at a relatively low cost.

This paper examines empirical relationships among commuters’ mode choice, metropolitan urban form, and socioeconomic attributes in the 100 largest urban areas in the United States and Mexico. Fitting multinomial logit models to data for more than 5 million commuters and their home urban area, we find several consistent relationships and several important differences in relationships among urban form and travel behavior. In both countries, urban residents living in housing types associated with more centrally located housing in more densely populated urban areas with less roadway are less likely to commute by private vehicle than similar residents in other housing types and other urban areas. In addition to some differences in the strength, significance, and signs of several predictor variables, we find large differences in elasticity estimates across contexts. In particular, the US’s high rates of driving and generally car-friendly urban form mean that even dramatic shifts in urban form or income result in only small predicted changes in the probability of commuting by private vehicle. We conclude that land use and transportation policies likely have a more substantial role in shaping commute patterns in countries like Mexico than in countries like the US.

As the claim of many ‘ghost towns’ emerged in China since 2000s, it has aroused increasing social-economic concerns as well as governmental policy responses to ‘destock’ and refrain from the development of new towns and new districts at local level. This dissertation contains an exploratory study that aims to clarify the concept of ghost town by making typological generalization from historical cases. It then specifies the scope of China’s emerging ‘ghost towns’ as those originated from unsuccessful new town development projects featured by highly vacant properties. With a focus on cities in Yangtze River Delta region, it examines the general context of YRD in terms of population growth, urban and economic development. Illustrated by the empirical analysis of two representative cases of Wujin District in Changzhou and Tianducheng in Hangzhou, it argues that inappropriate development strategies as well as underlying institutional arrangement towards neoliberal capitalism fosters such ‘ghost town’ phenomenon. While the situation can possibly be rectified, lessons and implications are twofold: under decentralization, new town development strategy should be differentiated based on city positioning; and under privatization, mega city development is not going to work successfully by solely relying on the private sector.

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