HOW TRAVEL DEMAND AFFECTS DETECTION OF NON-RECURRENT TRAFFIC CONGESTION ON URBAN ROAD NETWORKS


== Abstract ==

Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revise their existing plans to mitigate its effects. Space-time clusters of high link journey time estimates correspond to non-recurrent congestion events. Existing research, however, has not considered the effect of travel demand on the effectiveness of non-recurrent congestion detection methods. Therefore, this paper investigates how travel demand affects detection of non-recurrent traffic congestion detection on urban road networks. Travel demand has been classified into three categories as low, normal and high. The experiments are carried out on London’s urban road network, and the results demonstrate the necessity to adjust the relative importance of the component evaluation criteria depending on the travel demand level.


== Original document ==

The different versions of the original document can be found in:

* [https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/159/2016 https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/159/2016] under the license https://creativecommons.org/licenses/by

* [https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/159/2016/isprs-archives-XLI-B2-159-2016.pdf https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/159/2016/isprs-archives-XLI-B2-159-2016.pdf]

* [http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/159/2016/isprs-archives-XLI-B2-159-2016.pdf http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/159/2016/isprs-archives-XLI-B2-159-2016.pdf],
: [http://dx.doi.org/10.5194/isprs-archives-xli-b2-159-2016 http://dx.doi.org/10.5194/isprs-archives-xli-b2-159-2016] under the license cc-by

* [https://discovery.ucl.ac.uk/id/eprint/1514407 https://discovery.ucl.ac.uk/id/eprint/1514407],
: [https://discovery.ucl.ac.uk/id/eprint/1514407/1/Anbaroglu_How_travel_demand.pdf https://discovery.ucl.ac.uk/id/eprint/1514407/1/Anbaroglu_How_travel_demand.pdf]

* [https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/159/2016/isprs-archives-XLI-B2-159-2016.pdf https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/159/2016/isprs-archives-XLI-B2-159-2016.pdf],
: [http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/159/2016 http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/159/2016],
: [https://ui.adsabs.harvard.edu/abs/2016ISPAr49B2..159A/abstract https://ui.adsabs.harvard.edu/abs/2016ISPAr49B2..159A/abstract],
: [https://discovery.ucl.ac.uk/id/eprint/1514407 https://discovery.ucl.ac.uk/id/eprint/1514407],
: [https://noa.gwlb.de/receive/cop_mods_00012834 https://noa.gwlb.de/receive/cop_mods_00012834],
: [https://academic.microsoft.com/#/detail/2409630806 https://academic.microsoft.com/#/detail/2409630806]

* [https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/159/2016/isprs-archives-XLI-B2-159-2016.pdf https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/159/2016/isprs-archives-XLI-B2-159-2016.pdf],
: [https://doaj.org/toc/1682-1750 https://doaj.org/toc/1682-1750],
: [https://doaj.org/toc/2194-9034 https://doaj.org/toc/2194-9034]