A New Framework for Traffic Anomaly Detection

Abstract

Trajectory data is becoming more and more popular nowadays and extensive studies have been conducted on trajectory data. One important research direction about trajectory data is the anomaly detection which is to find all anomalies based on trajectory patterns in a road network. In this paper, we introduce a road segment-based anomaly detection problem, which is to detect the abnormal road segments each of which has its "real" traffic deviating from its "expected" traffic and to infer the major causes of anomalies on the road network. First, a deviation-based method is proposed to quantify the anomaly of reach road segment. Second, based on the observation that one anomaly from a road segment can trigger other anomalies from the road segments nearby, a diffusionbased method based on a heat diffusion model is proposed to infer the major causes of anomalies on the whole road network. To validate our methods, we conduct intensive experiments on a large real-world GPS dataset of about 23,000 taxis in Shenzhen, China to demonstrate the performance of our algorithms.

Detecting vehicle traffic patterns in urban environments using taxi trajectory intersection points

“…Bertini (2005) shows with this result that instead of highway traffic congestion measuring urban traffic congestion has many different possibilities. Besides describing movement in geographic space, which may be a two-or three-dimensional Euclidean movement space (Gudmundsson, Laube, and Wolle 2012;Wang et al 2015a), we have the option to inspect vehicle movement in the network space (Ji, Luo, and Geroliminis 2014;Lan et al 2014). Using average information of graphs with arcs and nodes, it is possible to detect congestion propagation and bottleneck identification in a computationally efficient way (Wang et al 2015b).…”

“…Other approaches include the interactive selections based on road segments for deriving traffic congestion durations using traffic jam propagation graphs . We can enrich the network space, which consists of arcs and nodes, with real-time information or with averaged day-wise traffic information for the detection of traffic anomalies (Lan et al 2014). The enriching process of road segments (arcs) with traffic data from mobile tracking devices is in most cases connected with an adapted Map Matching (MM) algorithm.…”

Root Cause Analysis of Traffic Anomalies Using Uneven Diffusion Model

“…The work most related to this study finds the major anomaly causes based on heat diffusion model [2]. Traffic anomalies are assumed to be like heat sources, which propagate energy to surrounding parts in road networks.…”

“…There are different ways of partition in the context of specific applications, for example, partition by road links [2], or by dividing city areas using main road segments [5], or using road network Voronoi diagram [6]. Note the city area partition method and the time bin span are independent of the root cause analysis proposed in this study.…”

Bus Network Analysis and Monitoring

“…Some relevant works address the detection of traffic anomalies with GPS data (LIU, W. et al, 2011;CHAWLA et al, 2012;PANG et al, 2013;WANG et al, 2013), while others use social media data as a source of mobility data to detect anomalies (DALY et al, 2013;PAN et al, 2013;SAKAKI et al, 2013;CHEN et al, 2014;D'ANDREA et al, 2015). According to (KUANG et al, 2015), traffic anomaly detection methods can be classified into four main categories: distancebased (SETHI, 2013), cluster-based (ANBAROGLU et al, 2014), classificationbased (LAN et al, 2014), and statistics-based categories ( Kinoshita et al 2015).…”