Scalable Pythagorean Mean based Incident Detection in Smart Transportation Systems (original) (raw)

Anomaly based Incident Detection in Large Scale Smart Transportation Systems

2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)

Modern smart cities are focusing on smart transportation solutions to detect and mitigate the effects of various traffic incidents in the city. To materialize this, roadside units and ambient transportation sensors are being deployed to collect vehicular data that provides real-time traffic monitoring. In this paper, we first propose a real-time data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a Pythagorean Mean-based invariant as an anomaly detection metric that is highly stable under no incidents but shows a deviation in the presence of incidents. We learn the bounds of the invariants in a robust manner such that anomaly detection can generalize to unseen events, even when learning from real noisy data. We perform extensive experimental validation using mobility data collected from the City of Nashville, Tennessee, and prove that the method can detect incidents within each cluster in real-time.

Quantifying Urban Traffic Anomalies

ArXiv, 2016

Detecting and quantifying anomalies in urban traffic is critical for real-time alerting or re-routing in the short run and urban planning in the long run. We describe a two-step framework that achieves these two goals in a robust, fast, online, and unsupervised manner. First, we adapt stable principal component pursuit to detect anomalies for each road segment. This allows us to pinpoint traffic anomalies early and precisely in space. Then we group the road-level anomalies across time and space into meaningful anomaly events using a simple graph expansion procedure. These events can be easily clustered, visualized, and analyzed by urban planners. We demonstrate the effectiveness of our system using 7 weeks of anonymized and aggregated cellular location data in Dallas-Fort Worth. We suggest potential opportunities for urban planners and policy makers to use our methodology to make informed changes. These applications include real-time re-routing of traffic in response to abnormally h...

A Decentralized Approach for Real Time Anomaly Detection in Transportation Networks

2019 IEEE International Conference on Smart Computing (SMARTCOMP)

Internet of Things (IoT), edge/fog computing, and the cloud are fueling rapid development in smart connected cities. Given the increasing rate of urbanization, the advancement of these technologies is a critical component of mitigating demand on already constrained transportation resources. Smart transportation systems are most effectively implemented as a decentralized network, in which traffic sensors send data to small low-powered devices called Roadside Units (RSUs). These RSUs host various computation and networking services. Data driven applications such as optimal routing require precise real-time data, however, data-driven approaches are susceptible to data integrity attacks. Therefore we propose a multi-tiered anomaly detection framework which utilizes spare processing capabilities of the distributed RSU network in combination with the cloud for fast, real-time detection. In this paper we present a novel real time anomaly detection framework. Additionally, we focus on implementation of our framework in smart-city transportation systems by providing a constrained clustering algorithm for RSU placement throughout the network. Extensive experimental validation using traffic data from Nashville, TN demonstrates that the proposed methods significantly reduce computation requirements while maintaining similar performance to current state of the art anomaly detection methods.

On Mining Anomalous Patterns in Road Traffic Streams

2011

Large number of taxicabs in major metropolitan cities are now equipped with a GPS device. Since taxis are on the road nearly twenty four hours a day (with drivers changing shifts), they can now act as reliable sensors to monitor the behavior of traffic. In this paper we use GPS data from taxis to monitor the emergence of unexpected behavior in the Beijing metropolitan area. We adapt likelihood ratio tests (LRT) which have previously been mostly used in epidemiological studies to describe traffic patterns. To the best of our knowledge the use of LRT in traffic domain is not only novel but results in accurate and rapid detection of anomalous behavior.

Detecting Traffic Anomalies in Urban Areas using Taxi GPS Data

：Large-scale GPS data contain hidden information and provide us with the opportunity to discover knowledge that may be useful for transportation systems using advanced data mining techniques. In major metropolitan cities, many taxicabs are equipped with GPS devices. Because taxies operate continuously nearly 24 hours per day, they can be used as reliable sensors for the perceived traffic state. In this article, the entire city is divided into sub-regions by roads, and taxi GPS data are transformed into traffic flow data to build a traffic flow matrix. In addition, we propose a highly efficient anomaly detection algorithm based on wavelet transform and PCA (Principal Component Analysis) for detecting anomalous traffic events in urban regions. A traffic anomaly is considered to occur in a sub-region when the values of the corresponding indicators deviate significantly from the expected values. We evaluated our algorithm using a GPS dataset that was generated by more than 15,000 taxies over a period of half a year in Harbin, China. The results show that our detection algorithm is effective and efficient.

A Survey on Urban Traffic Anomalies Detection Algorithms

IEEE Access

This paper reviews the use of outlier detection approaches in urban traffic analysis. We divide existing solutions into two main categories: flow outlier detection and trajectory outlier detection. The first category groups solutions that detect flow outliers and includes statistical, similarity, and pattern mining approaches. The second category contains solutions where the trajectory outliers are derived, including offline processing for trajectory outliers and online processing for sub-trajectory outliers. Solutions in each of these categories are described, illustrated, and discussed, and open perspectives and research trends are drawn. Compared to state-of-the-art survey papers, the contribution of this paper lies in providing a deep analysis of all the kinds of representations in urban traffic data, including flow values, segment flow values, trajectories, and sub-trajectories. In this context, we can better understand the intuition, limitations, and benefits of the existing outlier urban traffic detection algorithms. As a result, practitioners can receive some guidance for selecting the most suitable methods for their particular case. INDEX TERMS Urban traffic analysis, outlier detection, machine learning, data mining.

On Learning From Inaccurate and Incomplete Traffic Flow Data

IEEE Transactions on Intelligent Transportation Systems

Today, we live in an era where pervasive sensor networks both collect and broadcast rich digital footprints about the human mobility. However, most of this data often comes in an incomplete and/or inaccurate fashion. In this paper, we propose a knowledge discovery framework to handle such issues in the context of automatic incident detection systems fed with traffic flow data. This framework operates in three steps : 1) it clusters sensors with a novel multi-criteria distance metric tailored for this purpose, followed by a heuristic rule that labels the abnormal groups; 2) then, a spatial cross-correlation framework identifies seasonal and individual abnormal readings to perform a more fine-grained filtering; and 3) finally, we propose a novel fundamental diagram that discovers the critical density of a given road section/spot on a data-driven fashion that is resistant to both outliers and noise within the input data. Large-scale experiments were conducted over traffic flow data provided by a major Asian highway operator. The obtained results illustrate well the contributions of this framework: it drastically reduces the noise within the raw data, and it also allows determining reliable definitions of traffic states (congestion/no congestion) on a completely automated way.

Real-Time Road Traffic Anomaly Detection

Many modeling approaches have been proposed to help forecast and detect incidents. Accident has received the most attention from researchers due to its impacts economically. The traffic congestion costs billions of dollars to economy. The main reasons of major percentage of traffic congestion are the incidents. Road accidents continue to increase in digital age. There are many reasons for road accidents. This paper will discuss and introduce new algorithm for road accident detection. Various forecast schemes have been proposed to manage the traffic data. In this paper we will introduce road accident detection scheme based on improved exponential moving average. The proposed traffic incident detection algorithm is based on the automatic exponential moving average scheme. The detection algorithm is based on analyzing the collected traffic flow parameters. The detection algorithm is based on analyzing the collected traffic flow parameters. In addition a real-time accident forecast model was developed based on short-term variation of traffic flow characteristics.

Data-Driven Detection of Anomalies and Cascading Failures in Traffic Networks

Annual Conference of the PHM Society, 2019

Traffic networks are one of the most critical infrastructures for any community. The increasing integration of smart and connected sensors in traffic networks provides researchers with unique opportunities to study the dynamics of this critical community infrastructure. Our focus in this paper is on the failure dynamics of traffic networks. We are specifically interested in analyzing the cascade effects of traffic congestions caused by physical incidents, focusing on developing mechanisms to isolate and identify the source of a congestion. To analyze failure propagation, it is crucial to develop (a) monitors that can identify an anomaly and (b) a model to capture the dynamics of anomaly propagation. In this paper, we use real traffic data from Nashville, TN to demonstrate a novel anomaly detector and a Timed Failure Propagation Graph based diagnostics mechanism. Our novelty lies in the ability to capture the the spatial information and the interconnections of the traffic network as...

Heimdall: an AI-based infrastructure for traffic monitoring and anomalies detection

2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)

Since their appearance, Smart Cities have aimed at improving the daily life of people, helping to make public services smarter and more efficient. Several of these services are often intended to provide better security conditions for citizens and drivers. In this vein, we present HEIMDALL, an AI-based video surveillance system for traffic monitoring and anomalies detection. The proposed system features three main tiers: a ground level, consisting of a set of smart lampposts equipped with cameras and sensors, and an advanced AI unit for detecting accidents and traffic anomalies in real time; a territorial level, which integrates and combines the information collected from the different lampposts, and cross-correlates it with external data sources, in order to coordinate and handle warnings and alerts; a training level, in charge of continuously improving the accuracy of the modules that have to sense the environment. Finally, we propose and discuss an early experimental approach for the detection of anomalies, based on a Faster R-CNN, and adopted in the proposed infrastructure.