dc.contributor.author | GHOSH, BIDISHA | en |
dc.date.accessioned | 2015-03-04T13:36:30Z | |
dc.date.available | 2015-03-04T13:36:30Z | |
dc.date.issued | 2014 | en |
dc.date.submitted | 2014 | en |
dc.identifier.citation | Ghosh, B., & Smith, D. P., Customization of automatic incident detection algorithms for signalized urban arterials, Journal of Intelligent Transportation Systems, 18, 4, 2014, 426 - 441 | en |
dc.identifier.other | Y | en |
dc.description | PUBLISHED | en |
dc.description.abstract | Non
-
recurrent congestion or
incidents are detrimental to the operability and efficiency of busy
urban t
ransport networks. There exist
s
multiple Automatic Incident Detection Algorithms
(AIDA) to remotely detect the occurrence of an incident in highway or freeway scenarios,
however very little research has been performed to automatically detect incidents in signalised
urban
arterials
. This
limited research attention has mostly been
focussed
on developing new
urban arterial specific algorithms rather than identifying alternative methods to synthesize
existing freeway based algorithms to urban conditions. The main hindrance to such synthesis
is
that the traffic patterns on the signalised urban
arterials
are significantly different from the
same on highways/freeways due to the presence of traffic intersections. This paper introduces
a new strategy of
customis
ing
the existing AIDAs (freeway base
d or otherwise) to significantly
improve their adaptability to signalised urban arterial transport networks. The new strategy
focuses on preprocessing the tr
affic information before being used as input
to a
freeway/highway based AIDA to lessen the effect o
f traffic signals and to imitate the input
patterns in highway/freeway based incident conditions. The effectiveness of this new strategy
has been established with the help of four existing AIDAs. The proposed strategy is a simple
solution to implement exis
ting algorithms to signalised urban networks without any further
instrumentation or operational cost. | en |
dc.format.extent | 426 | en |
dc.format.extent | 441 | en |
dc.language.iso | en | en |
dc.relation.ispartofseries | Journal of Intelligent Transportation Systems | en |
dc.relation.ispartofseries | 18 | en |
dc.relation.ispartofseries | 4 | en |
dc.rights | Y | en |
dc.subject | Artificial Neural Networks | en |
dc.subject | Urban Arterial | en |
dc.subject | Signalized Traffic Intersections | en |
dc.subject | Incident Detection | en |
dc.title | Customization of automatic incident detection algorithms for signalized urban arterials | en |
dc.type | Journal Article | en |
dc.type.supercollection | scholarly_publications | en |
dc.type.supercollection | refereed_publications | en |
dc.identifier.peoplefinderurl | http://people.tcd.ie/bghosh | en |
dc.identifier.rssinternalid | 101712 | en |
dc.identifier.doi | http://www.tandfonline.com/doi/abs/10.1080/15472450.2013.806843#.VPb-Z_msWM8 | en |
dc.rights.ecaccessrights | openAccess | |
dc.identifier.uri | http://hdl.handle.net/2262/73404 | |