Traffic Incident Detection Using Probabilistic Topic Model Akira Kinoshita Atsuhiro Takasu, Jun Adachi The University of Tokyo National Institute of Informatics 2-1-2 Hitotsubashi, Chiyoda, 2-1-2 Hitotsubashi, Chiyoda, Tokyo, Japan Tokyo, Japan
[email protected] {takasu,adachi}@nii.ac.jp ABSTRACT economic loss to society. A technology that can detect traf- Traffic congestion is quite common in urban settings, and is fic incidents in real time and alert people accordingly would not always caused by traffic incidents. In this paper, we pro- therefore be a desirable way of reducing these ill e↵ects. pose a simple method for detecting traffic incidents by using Against this background, there have been many studies probe-car data to compare usual and current traffic states, on AID, e.g., [2, 13]. Most of the approaches exploit data thereby distinguishing incidents from spontaneous conges- sent from stationary sensors and cameras installed on roads. tion. First, we introduce a traffic state model based on a However, the installation and maintenance of such sensors probabilistic topic model to describe traffic states for a vari- is expensive, with only the main routes likely to have them ety of roads, deriving formulas for estimating the model pa- [17]. On the other hand, probe-car data (PCD), on which rameters from observed data using an expectation–maximi- we focus in this paper, are becoming increasingly important, zation algorithm. Next, we propose an incident detection as the number of probe cars and the size of the associated method based on our model, which issues an alert when a data archives increase.