Dynamic and Context-Aware Models in Traffic, Crime, and Routing

The recent developments in the research area have seen a shift towards more data-driven and flexible methodologies, particularly in the fields of traffic management, crime prediction, and routing algorithms. There is a notable emphasis on leveraging historical data and real-time information to create more adaptive and efficient systems. For instance, innovative approaches in traffic management are focusing on imputing truck information across nationwide networks using iterative algorithms, which can significantly enhance traffic planning and policy-making. In crime prediction, there is a move towards event-centric frameworks that allow for flexible time intervals, addressing the irregularities and complexities inherent in predicting crime hotspots. Additionally, routing algorithms are being reimagined through trajectory-based methods, which bypass traditional graph-based systems by directly utilizing vehicle trajectory data, offering a simpler and more adaptable approach. These advancements highlight a trend towards more dynamic and context-aware models that can better respond to real-world complexities and temporal variations.

Noteworthy papers include one that introduces a novel event-centric framework for predicting crime hotspots with flexible time intervals, and another that proposes a new trajectory-based routing paradigm, bypassing traditional graph-based systems by directly utilizing raw trajectory data.

Sources

An Iterative Algorithm to Impute Truck Information over Nationwide Traffic Networks

An Event-centric Framework for Predicting Crime Hotspots with Flexible Time Intervals

TrajRoute: Rethinking Routing with a Simple Trajectory-Based Approach -- Forget the Maps and Traffic!

A High-Resolution, US-scale Digital Similar of Interacting Livestock, Wild Birds, and Human Ecosystems with Applications to Multi-host Epidemic Spread

Compositional simulation-based inference for time series

The Recurrent Sticky Hierarchical Dirichlet Process Hidden Markov Model

Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems

A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior

Unsupervised Abnormal Stop Detection for Long Distance Coaches with Low-Frequency GPS

Which bits went where? Past and future transfer entropy decomposition with the information bottleneck

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