GOAT 3.0

Need for innovation

The planning of sustainable mobility faces enormous challenges. Complex transport measures must be evaluated in terms of their spatial effects in a goal-oriented manner. The focus of GOAT 3.0 is primarily on planning neighborhood mobility (15-minute city), green space planning and public transport planning. GOAT 3.0 should make it possible, for example, to assess the accessibility of high-quality green spaces using suitable indicators and to identify gaps in supply. The special problem arises from the demand for fine-grained analyses with simultaneous practice-oriented, easily communicable preparation of results.

Project goals

The goal is to develop a digital planning tool (GOAT 3.0) by operationalizing the accessibility approach. By integrating analyses and scenarios for multimodal mobility via an easy-to-use web interface, diverse spatial levels and multifaceted goals (e.g., education, recreation) will be integrated. This is accompanied by the development of new approaches to data preparation and refinement (e.g., AI, fusion algorithms) to create data-driven planning baselines on job and population distributions.

An iterative and co-creative software development process involving application partners from four pilot regions (Rhine-Neckar, Munich, Görlitz and Bonn) is central to this project. Thus, requirements and tests for real planning issues are determined in workshops, among others. For the development of indicators, findings for empirically validated accessibility indicators are generated by means of participatory approaches.

Expected results

Upon completion, GOAT 3.0 will be a fully comprehensive and interactive accessibility tool. Through a simple user interface, the mobility and spaces of tomorrow can be efficiently and effectively planned, designed, and evaluated. This creates significant added value for evidence-based planning and opportunities for economic exploitation. At the same time, a basis for future innovations is created through validation, refinement and opening of data.