Many studies focus on the mobility patterns of elderly people. However, few studies have explored the mobility of elderly women in rural and small urban areas. Compared with their peers in large urban areas, these women tend to face greater challenges because of limited services and mobility options. Using data from 1,021 respondents to a survey conducted in North Dakota, this study examines the determinants of the travel behavior of elderly women. In particular, a multilevel conceptual ecological model was applied to identify how individual-level social environment and physical environment factors influence their travel patterns. Ordered probit models were estimated for the frequencies of nine types of trips. It was found that individual factors (e.g., self-efficacy and physical limitations), social environment factors (e.g., clubs and family), and physical environment factors (e.g., rural versus small urban areas) significantly affect the mobility of elderly women in North Dakota. The implications of these results for policies that aim to enhance the mobility of elderly women in rural and small urban locations are discussed.
Dockless bike sharing is an emerging paradigm. Like many other technologies, it brings advantages and disadvantages to communities. Further investigation into public opinion will shed light on the impact of this technology on communities and provide input to city authorities for transportation planning. Transportation planning processes can be enhanced by engaging the community through social media technologies. Social media like Twitter, Facebook, and other microblogging media have been used for planning, but have not been extensively evaluated for that purpose. This study examined approximately 32,000 posts on Twitter to assess public opinion on dockless bike-sharing systems. Using a mix of text mining and statistical techniques, we examined relevant posts to determine the sentiment polarity of tweets, the underlying topics in the tweets, and the extent of engagement and impact on the decision-making process. Results given by two different sentiment algorithms show that there is more positive than negative polarity across the algorithms. Also, the findings show that the underlying topics in tweets include electric scooters, private e-hailing companies, and blockage of sidewalks, among others. The results indicate that the dockless shared mobility models are potentially useful in generating participation, but faced substantial technical, analytical, and communication barriers to influencing decision-making.
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