Detection of incidents and events in urban networks

Thomas, Tom; Berkum, Eric C. van

doi:10.1049/iet-its:20080045

Cited by 21 publications

(20 citation statements)

References 6 publications

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“…Second, low data quality may lead to signalling an NRC event when there is none in reality. Previous researchers have highlighted that data quality in an urban road network is a serious issue, and should be considered in traffic modelling (Hasan, Ben-Akiva, and Emmonds 2011;Thomas and van Berkum 2009). Existing NRC detection methods fail to capture this heterogeneity, as they do not consider the statistical characteristics of the traffic data (Anbaroglu, Heydecker, and Cheng 2014).…”

Section: Introductionmentioning

confidence: 99%

Non-recurrent traffic congestion detection on heterogeneous urban road networks

Anbaroğlu

Cheng

Heydecker

2015

Transportmetrica A: Transport Science

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This paper proposes two novel methods for non-recurrent congestion (NRC) event detection on heterogeneous urban road networks based on link journey time (LJT) estimates. Heterogeneity exists on urban road networks in two main aspects: variation in link lengths and data quality. The proposed NRC detection methods are referred to as percentile-based NRC detection and space-time scan statistics (STSS) based NRC detection. Both of these methods capture the heterogeneity of an urban road network by modelling the LJTs with a lognormal distribution. Empirical analyses are conducted on London's urban road network consisting of 424 links for the 20 weekdays of October 2010. Various parameter settings are tested for both of the methods, and the results favour STSS-based NRC detection method over the percentile-based NRC detection method. Link-based analyses demonstrate the effectiveness of the proposed methods in capturing the heterogeneity of the analysed road network.

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Section: Introductionmentioning

confidence: 99%

Non-recurrent traffic congestion detection on heterogeneous urban road networks

Anbaroğlu

Cheng

Heydecker

2015

Transportmetrica A: Transport Science

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“…( Thomas and van Berkum, 2009) proposed a prediction scheme for recurring traffic events based on data collected at urban intersections. They argue that it is necessary the management of events on demand in case of possible incidents, but they do not validate the results of the analysis with real data of incident detection, and they do not define how the information is collected, shown or stored.…”

Section: Related Workmentioning

confidence: 99%

A methodology for structured ontology construction applied to intelligent transportation systems

Gregor

Toral

Ariza

et al. 2016

Computer Standards & Interfaces

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The number of computers installed in urban and transport networks has grown tremendously in recent years, also the local processing capabilities and digital networking currently available. However, the heterogeneity of existing equipment in the field of ITS (Intelligent Transportation Systems) and the large volume of information they handle, greatly hinder the interoperability of the equipment and the design of cooperative applications between devices currently installed in urban networks. While the dynamic discovery of information, composition and invocation of services through intelligent agents are a potential solution to these problems, all these technologies require intelligent management of information flows. In particular, it is necessary to wean these information flows of the technologies used, enabling universal interoperability between computers, regardless of the context in which they are located. The main objective of this paper is to propose a systematic methodology to create ontologies, using methods such as a semantic clustering algorithms for retrieval and representation of information. Using the proposed methodology, an ontology will be developed in the ITS domain. This ontology will serve as the basis of semantic information to a SS (Semantic Service) that allows the connection of new equipment to an urban

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“…Accident detection techniques can be separated into two categories [9]. The first category provides "recognition" of accidents if the monitored traffic situation is similar to previous accidents situation.…”

Section: Introductionmentioning

confidence: 99%

“…For example, [9,14] compare monitored flow rate with its threshold. In [15] researchers use speed to do comparison and detection, and [16÷18] use density (occupancy).…”

Section: Introductionmentioning

confidence: 99%

Correcting and complementing freeway traffic accident data using Mahalanobis distance based outlier detection

et al. 2017

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Subject reviewA huge amount of traffic data is archived which can be used in data mining especially supervised learning. However, it is not being fully used due to lack of accurate accident information (labels). In this study, we improve a Mahalanobis distance based algorithm to be able to handle differential data to estimate flow fluctuations and detect accidents and use it to support correcting and complementing accident information. The outlier detection algorithm provides accurate suggestions for accident occurring time, duration and direction. We also develop a system with interactive user interface to realize this procedure. There are three contributions for data handling. Firstly, we propose to use multi-metric traffic data instead of single metric for traffic outlier detection. Secondly, we present a practical method to organise traffic data and to evaluate the organisation for Mahalanobis distance. Thirdly, we describe a general method to modify Mahalanobis distance algorithms to be updatable. Keywords: accident data; data labelling; differential distance; Mahalanobis distance; outlier detection; traffic data; updatable algorithm Ispravljanje i nadopunjavanje podataja o prometnim nesrećama na autocesti putem Mahalanobis udaljenosti na temelju otkrivanja netipičnih vrijednostiPregledni članak Arhivirana je ogromna količina podataka o prometu koji bi se mogli koristiti za dobivanje specifičnih podataka. Međutim, oni se u potpunosti ne koriste zbog nepostojanja točnih podataka o prometu (oznaka). U ovom radu poboljšavamo algoritam zasnovan na Mahalanobis udaljenosti za procjenu promjena toka prometa i otkrivanje nesreća i primjenjujemo ga kod ispravljanja i dopunjavanja informacija o nesreći. Algoritam za otkrivanje outliera (netipičnih vrijednosti) pruža točne podatke o vremenu događanja nesreće, trajanju i smjeru. Razvijamo i sustav s interaktivnim sučeljem korisnika u svrhu ostvarenja ovog postupka. Predlažu se tri načina za manipulaciju podacima. Najprije, za otkrivanje outliera u prometu predlažemo uporabu multi-metričkih podataka o prometu umjesto jedno metričkih. Nadalje, predlažemo praktičnu metodu za organizaciju prometnih podataka i evaluaciju organizacije Mahalanobis udaljenosti. Kao treće, dajemo opis opće metode za modifikaciju algoritama Mahalanobis udaljenosti kako bi se mogli ažurirati.

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Detection of incidents and events in urban networks

Abstract: More information is available via www.transumo.nl.

Cited by 21 publications

References 6 publications

Non-recurrent traffic congestion detection on heterogeneous urban road networks

Non-recurrent traffic congestion detection on heterogeneous urban road networks

A methodology for structured ontology construction applied to intelligent transportation systems

Correcting and complementing freeway traffic accident data using Mahalanobis distance based outlier detection

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