![]() ![]() However, what sets Waze’s data apart from ordinary taxi data is that they provide real-time route information and allow social interaction via crowdsourced information and alert functionalities. Like other popular vehicle-based traffic data such as taxi data, these applications transform their users’ vehicles into moving city sensors. Popular GPS navigation applications, such as Waze have revolutionized the way humans relate to traffic and mobility, engaging hundreds of millions of users worldwide. Therefore, understanding the cause of many traffic patterns is difficult or impossible. Also, this type of data lacks semantics (i.e., we do not know what type of events are associated with the recorded data). While this approach is widely used, these data often cover a small fraction of the roads and are not uniform among cities. Due to the importance of this problem, many cities Footnote 1 \(^\) Footnote 2 acquire and publish traffic data from different sources such as road sensors. For this reason, it is essential for traffic planning and urban mobility experts to monitor and understand the leading causes of traffic congestions to plan policies and, therefore, identify solutions. This problem is significant in major urban centers where traffic jams cause billions of dollars in losses every year. Traffic jams are a severe problem in urban cities that cause pollution and waste fuel, money, and time and, therefore, severely impact the population’s quality of living. JamVis is validated through three usage scenarios analyzing different events in Rio de Janeiro. Furthermore, to provide an overview of this algorithm’s spatio-temporal results, we introduce a novel visualization called ST-Heatmap. A new algorithm called TST-clustering is introduced to perform event detection, which is an adaptation of the DB-Scan algorithm that allows clustering alerts by space, time, and type. The second component allows identifying events through alerts reported by Waze users about different problems (e.g., potholes, floods, or heavy traffic). The first one supports the exploration of Waze’s traffic jam information through multiple linked views. JamVis comprises two main components designed based on requirements elicited from domain experts. This paper proposes JamVis, a web-based visual analytics framework that leverages Waze’s multi-modal spatio-temporal data to this end. However, exploring these large complex data presents a challenge to domain experts. Therefore, there is an urgent need to build tools that enable authorities to monitor and understand traffic dynamics and their causes. Traffic jams are a significant problem in urban cities that cause pollution and waste fuel, money, and time.
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