A traffic breakdown contains phases of trigger&formation (A), propagation (B) and dissipation (C). Our goal is to discover the potential traffic breakdown precursors from region X, which is the downstream area antecedent to the breakdown trigger. Traffic breakdowns, characterized by sudden congestion and reduced vehicle speeds, can lead to severe accidents and increased travel times. Identifying the contributing factors enables the development of predictive models to mitigate these events. Several methods have been developed to identify and predict traffic breakdowns. Statistical estimators and probabilistic models analyze transitional events, with one approach using statistical estimators to assess breakdown probability by classifying these occurrences [1]. Machine learning techniques, such as artificial neural networks, have also shown promise for modeling abrupt traffic transitions [2]. However, a key limitation of current methods is their inability to systematically link environmental and driver behavior factors with the spatiotemporal dynamics of traffic breakdowns. For instance, while studies highlight precursors such as road geometry or the braking of a lead vehicle in a platoon [3, 4], input data is often simplified into tabular formats.

The height of individual vs. collective irrationality has to be automobile traffic. We build roadways around the assumption that we as individual human actors will behave in ways that appear to reward those behaviors at the level of individuals but wind up harming the collective's goal of moving many cars through a limited amount of space as quickly as possible. Witness how a single greedy merge, for example, can send out a cascade of brake lights leading to a further wave of merges, some of which will themselves be greedy (careless). There really is no truly individual behavior in traffic and yet people are people. Fixing this is among the promises of driverless cars.