Real-time traffic incident detection using a probabilistic topic model

Abstract

a b s t r a c tTraffic congestion occurs frequently in urban settings, and is not always caused by traffic incidents. In this paper, we propose a simple method for detecting traffic incidents from probe-car data by identifying unusual events that distinguish incidents from spontaneous congestion. First, we introduce a traffic state model based on a probabilistic topic model to describe the traffic states for a variety of roads. Formulas for estimating the model parameters are derived, so that the model of usual traffic can be learned using an expectation-maximization algorithm. Next, we propose several divergence functions to evaluate differences between the current and usual traffic states and streaming algorithms that detect high-divergence segments in real time. We conducted an experiment with data collected for the entire Shuto Expressway system in Tokyo during 2010 and 2011. The results showed that our method discriminates successfully between anomalous car trajectories and the more usual, slowly moving traffic patterns.

A Regional Topic Model Using Hybrid Stochastic Variational Gibbs Sampling for Real-Time Video Mining

“…Yu et al [20] proposed a topic model for detecting an anomalous group of individuals in a social network. Kinoshita et al [21] introduced a traffic state model based on a probabilistic topic model to describe the traffic states for a variety of roads, the model can be learned using an expectation-maximization algorithm. Hospedales et al [22] introduced a dynamic topic model named Markov clustering topic model (MCTM), and an approximation to online Bayesian inference was formulated to enable dynamic scene understanding and behavior mining in new video data online in real-time.…”

Automated Incident Detection Using Real-Time Floating Car Data

“…Several comparable taxi FCD systems have also been deployed in Berlin (200 taxis), Vienna (400), and Nuremburg (500) in [25]. A real-time AID system using an underlying state classification for traffic on an hourly basis was reported in [26], although no specific information is given on the actual real-time detection time or FCD coverage. However, most of these systems are limited to cities.…”

A Statistical Fitness Test of Newell's 3-detector Simplification Method for Unexpected Incident Detection in the Expressway Traffic Flow

“…Analysing data was extracted from a horizontal flow loop facilities. Kinoshita et al(2015) applied a traffic state model based on a probabilistic topic model and they proposed several divergence function to evaluate differences between the current and usual traffic states.…”