Volume XLI-B2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 159-164, 2016
https://doi.org/10.5194/isprs-archives-XLI-B2-159-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 159-164, 2016
https://doi.org/10.5194/isprs-archives-XLI-B2-159-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  07 Jun 2016

07 Jun 2016

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

B. Anbaroglu1, B. Heydecker2, and T. Cheng3 B. Anbaroglu et al.
  • 1Hacettepe University, Dept. of Geomatics Engineering, 06800, Beytepe, Ankara, Turkey
  • 2Centre for Transport Studies, Civil, Environmental and Geomatic Engineering, University College London, Gower Street, London, WC1E 6BT UK
  • 3SpaceTimeLab, Civil, Environmental and Geomatic Engineering, University College London, Gower Street, London, WC1E 6BT UK

Keywords: Non-recurrent congestion, space-time clustering, space-time scan statistics, urban road network, travel demand

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.