Detection of anomalies in urban traffic from open data

Jorma Kilpi; Sami Koskinen; Johan Scholliers

Detection of anomalies in urban traffic from open data

Research output: Contribution to conference › Conference article › Scientific › peer-review

Abstract

In order to keep transport flowing in the city, Urban Traffic Management Centres (TMCs) need to have timely knowledge of any traffic disturbances. A wide range of data from vehicles and infrastructure is available, such as floating car data, traffic cameras and loop detectors at traffic lights. The Integrated Urban Mobility pilot from the TransformingTransport EU project demonstrates the potential of automated processing of these big data sources to improve situational awareness and to provide information to travellers. Through artificial intelligence and big data technologies, anomalies can be detected from data, which is often available as open data.

Original language	English
Pages	TP1916
Number of pages	9
Publication status	Published - 3 Jun 2019
MoE publication type	Not Eligible
Event	13th ITS European Congress: Fulfilling ITS promises - Brainport Eindhoven, Netherlands Duration: 3 Jun 2019 → 6 Jun 2019 https://2019.itsineurope.com/

Conference

Conference	13th ITS European Congress
Country/Territory	Netherlands
City	Brainport Eindhoven
Period	3/06/19 → 6/06/19
Internet address	https://2019.itsineurope.com/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Cite this

@conference{aed50a4c5b9641c591d6e1d55683e6ff,

title = "Detection of anomalies in urban traffic from open data",

abstract = "In order to keep transport flowing in the city, Urban Traffic Management Centres (TMCs) need to have timely knowledge of any traffic disturbances. A wide range of data from vehicles and infrastructure is available, such as floating car data, traffic cameras and loop detectors at traffic lights. The Integrated Urban Mobility pilot from the TransformingTransport EU project demonstrates the potential of automated processing of these big data sources to improve situational awareness and to provide information to travellers. Through artificial intelligence and big data technologies, anomalies can be detected from data, which is often available as open data.",

author = "Jorma Kilpi and Sami Koskinen and Johan Scholliers",

note = "no proceedings info/issn/isbm; 13th ITS European Congress : Fulfilling ITS promises ; Conference date: 03-06-2019 Through 06-06-2019",

year = "2019",

month = jun,

day = "3",

language = "English",

pages = "TP1916",

url = "https://2019.itsineurope.com/",

}

TY - CONF

T1 - Detection of anomalies in urban traffic from open data

AU - Kilpi, Jorma

AU - Koskinen, Sami

AU - Scholliers, Johan

N1 - no proceedings info/issn/isbm

PY - 2019/6/3

Y1 - 2019/6/3

N2 - In order to keep transport flowing in the city, Urban Traffic Management Centres (TMCs) need to have timely knowledge of any traffic disturbances. A wide range of data from vehicles and infrastructure is available, such as floating car data, traffic cameras and loop detectors at traffic lights. The Integrated Urban Mobility pilot from the TransformingTransport EU project demonstrates the potential of automated processing of these big data sources to improve situational awareness and to provide information to travellers. Through artificial intelligence and big data technologies, anomalies can be detected from data, which is often available as open data.

AB - In order to keep transport flowing in the city, Urban Traffic Management Centres (TMCs) need to have timely knowledge of any traffic disturbances. A wide range of data from vehicles and infrastructure is available, such as floating car data, traffic cameras and loop detectors at traffic lights. The Integrated Urban Mobility pilot from the TransformingTransport EU project demonstrates the potential of automated processing of these big data sources to improve situational awareness and to provide information to travellers. Through artificial intelligence and big data technologies, anomalies can be detected from data, which is often available as open data.

UR - https://transformingtransport.eu/index.php/downloads/presentations

M3 - Conference article

SP - TP1916

T2 - 13th ITS European Congress

Y2 - 3 June 2019 through 6 June 2019

ER -

Detection of anomalies in urban traffic from open data

Abstract

Conference

UN SDGs

Other files and links

Fingerprint

Cite this