Abstract

With the ongoing trend towards digitisation, vast amounts of, often very fine-grained, data are being collected. The ultimate goal is to capture and understand the behaviour of a system, such as the traffic in a city. However, making sense of such data is not straightforward due to its high level of detail and complex dependencies in time and space. Exploring heuristic approaches is essential to arrive at data representations that enable better insights into the underlying system dynamics by zooming out from the detail. In this paper, a novel approach for representing and reasoning about traffic state transition behaviour via a multitude of parameterised Markov chains models, cleverly designed to fit in a cascade, is proposed. The benefits of working with a multitude of individual Markov chains are outlined and subsequently, it is illustrated how to combine them into daily transition graphs such that their graph representation can be exploited to extract insights about daily traffic behaviour. In addition, targeting context-specific studies, an alternative approach is introduced combining in a dynamic fashion a cascade of Markov chains covering longer and overlapping time windows. A recursive algorithm is conceived and validated allowing to exploit this cascade structure for computing state transition probabilities over time. The potential of the proposed approach for mining traffic state transitions is demonstrated on a use case derived from real-world data.

Article ID: 2023L28

Month: June

Year: 2023

Address: Online

Venue: The 36th Canadian Conference on Artificial Intelligence

Publisher: Canadian Artificial Intelligence Association

URL: https://caiac.pubpub.org/pub/jv5jhck0