Assessing Mobility Policy with AI-Driven Analysis of User-Generated Content

We recommend assessing mobility policies with AI-driven analysis of user-generated content. To achieve this policy recommendation, we propose to take into account the following: (1) Using large language models to analyse user-generated content is a reliable methodology for gathering and analysing typically overlooked relevant information regarding citizens' perceptions in the implementation of sustainable mobility policies; (2) The substantial processing capacity of these models, coupled with their ability to gather a great amount of information, enables decision-makers to supplement and enhance the often-limited traditional data collection methods. As exposed in the following case study, this methodology can provide historical perceptual information on transport modes, mobility policies, and infrastructure, among others; and (3) The ease of applying this methodology through AI open-source recent developments such as ChatGPT allows decision-makers and their teams to rapidly generate and assess a great amount of relevant data. This can facilitate policymakers' effectiveness and efficiency in the decision-making processes in urban mobility planning. However, policymakers should be aware of the characteristics of their selected population and use this as a complementary and evaluative method.