Detecting and interpreting non‐recurrent congestion from traffic and social media data

Abstract A non‐recurring incident often negatively affects traffic, which is represented as non‐recurrent congestion. However, travellers can usually perceive congestion without knowing the underlying reasons. Accordingly, this paper proposes a data‐driven framework for non‐recurrent congestion dete...

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Autores principales:	Sen Luan, Xiaolei Ma, Meng Li, Yuelong Su, Zhenning Dong
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Transportation engineering TA1001-1280 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/fff14d153a944859b6ce861474079dc4
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spelling	oai:doaj.org-article:fff14d153a944859b6ce861474079dc42021-11-11T10:16:45ZDetecting and interpreting non‐recurrent congestion from traffic and social media data1751-95781751-956X10.1049/itr2.12104https://doaj.org/article/fff14d153a944859b6ce861474079dc42021-12-01T00:00:00Zhttps://doi.org/10.1049/itr2.12104https://doaj.org/toc/1751-956Xhttps://doaj.org/toc/1751-9578Abstract A non‐recurring incident often negatively affects traffic, which is represented as non‐recurrent congestion. However, travellers can usually perceive congestion without knowing the underlying reasons. Accordingly, this paper proposes a data‐driven framework for non‐recurrent congestion detection and interpretation analysis. First, a statistical algorithm named generalized extreme studentized deviate is introduced to detect non‐recurrent congestion by comparing the current traffic speed with the speed threshold learned from historical data. The case study in Beijing shows that the proposed generalized extreme studentized deviate outperforms other prevailing algorithms in terms of detection rate, false alarm rate, and mean detection time. Second, data mining and natural language processing technologies are implemented on data collected from Sina Weibo, a Chinese microblog site akin to Twitter, to classify non‐recurring incidents that may be associated with non‐recurrent congestion, including traffic accident, road construction, concert, special sport (marathon), and commercial activity. Results show that overall classification accuracy reaches 95%. Finally, the association relationship between the detected non‐recurrent congestions and incidents is established via spatiotemporal information matching. This information matching provides a bidirectional verification. On the one hand, nearly 58% of non‐recurrent congestion can be explained by incident‐related (IR) microblogs. On the other hand, an average of 62% of IR microblogs can be traced by nearby non‐recurrent congestions. This paper suggests that social media can be used as a secondary source and integrated with traffic data to enhance the understanding of non‐recurrent congestion.Sen LuanXiaolei Ma,Meng LiYuelong SuZhenning DongWileyarticleTransportation engineeringTA1001-1280Electronic computers. Computer scienceQA75.5-76.95ENIET Intelligent Transport Systems, Vol 15, Iss 12, Pp 1461-1477 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Transportation engineering TA1001-1280 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Transportation engineering TA1001-1280 Electronic computers. Computer science QA75.5-76.95 Sen Luan Xiaolei Ma, Meng Li Yuelong Su Zhenning Dong Detecting and interpreting non‐recurrent congestion from traffic and social media data
description	Abstract A non‐recurring incident often negatively affects traffic, which is represented as non‐recurrent congestion. However, travellers can usually perceive congestion without knowing the underlying reasons. Accordingly, this paper proposes a data‐driven framework for non‐recurrent congestion detection and interpretation analysis. First, a statistical algorithm named generalized extreme studentized deviate is introduced to detect non‐recurrent congestion by comparing the current traffic speed with the speed threshold learned from historical data. The case study in Beijing shows that the proposed generalized extreme studentized deviate outperforms other prevailing algorithms in terms of detection rate, false alarm rate, and mean detection time. Second, data mining and natural language processing technologies are implemented on data collected from Sina Weibo, a Chinese microblog site akin to Twitter, to classify non‐recurring incidents that may be associated with non‐recurrent congestion, including traffic accident, road construction, concert, special sport (marathon), and commercial activity. Results show that overall classification accuracy reaches 95%. Finally, the association relationship between the detected non‐recurrent congestions and incidents is established via spatiotemporal information matching. This information matching provides a bidirectional verification. On the one hand, nearly 58% of non‐recurrent congestion can be explained by incident‐related (IR) microblogs. On the other hand, an average of 62% of IR microblogs can be traced by nearby non‐recurrent congestions. This paper suggests that social media can be used as a secondary source and integrated with traffic data to enhance the understanding of non‐recurrent congestion.
format	article
author	Sen Luan Xiaolei Ma, Meng Li Yuelong Su Zhenning Dong
author_facet	Sen Luan Xiaolei Ma, Meng Li Yuelong Su Zhenning Dong
author_sort	Sen Luan
title	Detecting and interpreting non‐recurrent congestion from traffic and social media data
title_short	Detecting and interpreting non‐recurrent congestion from traffic and social media data
title_full	Detecting and interpreting non‐recurrent congestion from traffic and social media data
title_fullStr	Detecting and interpreting non‐recurrent congestion from traffic and social media data
title_full_unstemmed	Detecting and interpreting non‐recurrent congestion from traffic and social media data
title_sort	detecting and interpreting non‐recurrent congestion from traffic and social media data
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/fff14d153a944859b6ce861474079dc4
work_keys_str_mv	AT senluan detectingandinterpretingnonrecurrentcongestionfromtrafficandsocialmediadata AT xiaoleima detectingandinterpretingnonrecurrentcongestionfromtrafficandsocialmediadata AT mengli detectingandinterpretingnonrecurrentcongestionfromtrafficandsocialmediadata AT yuelongsu detectingandinterpretingnonrecurrentcongestionfromtrafficandsocialmediadata AT zhenningdong detectingandinterpretingnonrecurrentcongestionfromtrafficandsocialmediadata
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Detecting and interpreting non‐recurrent congestion from traffic and social media data

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