Mobility Transition through Participation? Policy impact of discursive, consultative public participation on urban transport projects for sustainability

Dissertation Projekt, Laura Mark

In my dissertation project at the Faculty of Architecture at RWTH Aachen University, I am using two case studies to investigate the substantive impact of consultative public participation on political decisions and the implications for sustainable development. My object of investigation is planning for the sustainable mobility transition, since on the one hand it is important and urgent for sustainable development and on the other hand it directly affects people’s everyday lives and thus often leads to resistance.

Abstract

A socio-ecological shift in transport requires profound changes in public space that affect the daily lives of users. This redistribution of road space and change in conditions of use is primarily carried out through spatial planning on the part of the public sector, in which the public is also increasingly involved. This is usually associated (implicitly or explicitly) with the public having an influence on the content of the planning; however, the actual effect has hardly been researched.

I am investigating the mechanisms through which the substantive impact of public participation comes about or is prevented, and which factors influence these mechanisms. I am interested in the conditions under which these substantive effects contribute to integrated transport planning, measured both in terms of democratic theory and substantive criteria.

Two municipal transport transition projects in Hamburg serve as case studies, in which the public can participate or has participated through consultation offers and other forms of participation: the redesign of the Elbchaussee in Hamburg and the low-car design of the Ottensen neighbourhood in Hamburg. The processes differ, among other things, in their framework conditions, spatial scale, tasks and participation offerings. For the detailed reconstruction and analysis of these processes, I mainly rely on data from qualitative interviews, document and media analyses, supplemented by results of quantitative population and participant surveys.

Expected Results

Expected results are theses on public participation in the context of the mobility transition. These deal with the mechanisms and factors that influence policy impact and come about through a detailed analysis of the individual case studies, a targeted comparison of the two case studies with each other and the embedding of the empirical results in the state of research as well as other results from the project. These theses are intended to contribute to the discussion on the role of the public in the context of a socio-ecological transformation.

Supporting the Manual Evaluation Process of Citizen’s Contributions Through Natural Language Processing

Doctoral thesis of Julia Romberg

Engaging citizens in decision-making processes is a widely implemented instrument in democracies. On the one hand, such public participation processes serve the goal of achieving a more informed process and thus to potentially improve the process outcome, i.e. resulting policies, through the ideas and suggestions of the citizens. On the other hand, involving the citizenry is an attempt to increase the public acceptance of decisions made.

As public officials try to evaluate the often large quantities of citizen input, they regularly face challenges due to restricted resources (e.g. lack of personnel,time limitations). When it comes to textual contributions, natural language processing (NLP) offers the opportunity to provide automated support for the evaluation, which to date is still carried out mainly manually. Although some research has already been conducted in this area, important questions have so far been insufficiently addressed or have remained completely unanswered. In my thesis, I have focuses on the sub-tasks of thematic structuring and argument analysis of public participation data.

For the thematic structuring of the contributions, I have chosen a supervised learning approach based on classification algorithms and active learning. On the one hand, I have investigated how much manual effort can be reduced by such strategies, using three case studies from German municipalities as examples (for more insights, please refer to this article). On the other hand, I have developed evaluation metrics that reflect the public analysts’ needs when designing topic classification methods with active learning.

In argument mining, on the one hand, I looked at how robustly argument identification and classification methods perform across different participation processes (for more insights, please refer to this article). On the other hand, I focused on the concreteness of arguments. In addition to predicting a three-level concreteness label, I developed a methodology to take into account the subjectivity of concreteness ratings and their impact on the prediction result (for more insights, please refer to this article).