Transport policy research has been developed systematically since the second half of the 20th century, when urban planning and transportation systems became critical components of economic development and urbanization. The field has evolved through a wealth of diverse publications and scholarly contributions. Our comprehensive guide synthesizes the state of the art in 20 major categories of transport policy research, identified based on a data-driven categorization. Each category is represented by a leading expert in the field, providing a rigorous assessment of the state of the art on an area together with an identification of challenges to inform future research and policymaking. Aggregated analysis reveals six major trends in policy research, namely: Adaptability, Network Integration, Sustainability, Wireless Connectivity, Equity, and Resilience — combined to form the word ANSWER, with each component being crucial to understanding and addressing the field's multifaceted challenges. Our guide aims to provide answers to pressing challenges and serve as an essential, accessible reference regarding transport policy research for academics, policymakers, and industry stakeholders alike.

Today's geo-location estimation approaches are able to infer the location of a target image using its visual content alone. These approaches typically exploit visual matching techniques, applied to a large collection of background images with known geo-locations. Users who are unaware that visual analysis and retrieval approaches can compromise their geo-privacy, unwittingly open themselves to risks of crime or other unintended consequences. This paper lays the groundwork for a new approach to geo-privacy of social images: Instead of requiring a change of user behavior, we start by investigating users' existing photo-sharing practices. We carry out a series of experiments using a large collection of social images (8.5M) to systematically analyze how photo editing practices impact the performance of geo-location estimation. We find that standard image enhancements, including filters and cropping, already serve as natural geo-privacy protectors. In our experiments, up to 19% of images whose location would otherwise be automatically predictable were unlocalizeable after enhancement. We conclude that it would be wrong to assume that geo-visual privacy is a lost cause in today's world of rapidly maturing machine learning. Instead, protecting users against the unwanted effects of pixel-based inference is a viable research field. A starting point is understanding the geo-privacy bonus of already established user behavior.