Visualizing Routes with AI-Discovered Street-View Patterns

Street-level visual appearances play an important role in studying social systems, such as understanding the built environment, driving routes, and associated social and economic factors. It has not been integrated into a typical geographical visualization interface (e.g., map services) for planning driving routes. In this paper, we study this new visualization task with several new contributions. First, we experiment with a set of AI techniques and propose a solution of using semantic latent vectors for quantifying visual appearance features. Second, we calculate image similarities among a large set of street-view images and then discover spatial imagery patterns. Third, we integrate these discovered patterns into driving route planners with new visualization techniques. Finally, we present VivaRoutes, an interactive visualization prototype, to show how visualizations leveraged with these discovered patterns can help users effectively and interactively explore multiple routes. Furthermore, we conducted a user study to assess the usefulness and utility of VivaRoutes.

Clustering Algorithms to Analyze the Road Traffic Crashes

Selecting an appropriate clustering method as well as an optimal number of clusters in road accident data is at times confusing and difficult. This paper analyzes shortcomings of different existing techniques applied to cluster accident-prone areas and recommends using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS) to overcome them. Comparative performance analysis based on real-life data on the recorded cases of road accidents in North Carolina also show more effectiveness and efficiency achieved by these algorithms.

Data Mining and Visualization to Understand Accident-prone Areas

In this study, we present both data mining and information visualization techniques to identify accident-prone areas, most accident-prone time, day, and month. Also, we surveyed among volunteers to understand which visualization techniques help non-expert users to understand the findings better. Findings of this study suggest that most accidents occur in the dusk (i.e., between 6 to 7 pm), and on Fridays. Results also suggest that most accidents occurred in October, which is a popular month for tourism. These findings are consistent with social information and can help policymakers, residents, tourists, and other law enforcement agencies. This study can be extended to draw broader implications.