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A non-parametric Hawkes process model of primary and secondary accidents on a UK smart motorway
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Kalair, Kieran, Connaughton, Colm and Alaimo Di Loro, Pierfrancesco (2021) A non-parametric Hawkes process model of primary and secondary accidents on a UK smart motorway. Journal of the Royal Statistical Society Series B: Statistical Methodology, 70 (1). pp. 80-97. doi:10.1111/rssc.12450 ISSN 1369-7412.
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Official URL: https://doi.org/10.1111/rssc.12450
Abstract
A self-exciting spatio-temporal point process is fitted to incident data from the UK National Traffic Information Service to model the rates of primary and secondary ac- cidents on the M25 motorway in a 12-month period during 2017-18. This process uses a background component to represent primary accidents, and a self-exciting component to represent secondary accidents. The background consists of periodic daily and weekly components, a spatial component and a long-term trend. The self-exciting components are decaying, unidirectional functions of space and time. These components are de- termined via kernel smoothing and likelihood estimation. Temporally, the background is stable across seasons with a daily double peak structure reflecting commuting patterns. Spatially, there are two peaks in intensity, one of which becomes more pronounced dur- ing the study period. Self-excitation accounts for 6-7% of the data with associated time and length scales around 100 minutes and 1 kilometre respectively. In-sample and out- of-sample validation are performed to assess the model fit. When we restrict the data to incidents that resulted in large speed drops on the network, the results remain coherent.
| Item Type: | Journal Article | ||||||||
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| Divisions: | Faculty of Science, Engineering and Medicine > Research Centres > Centre for Complexity Science Faculty of Science, Engineering and Medicine > Science > Mathematics |
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| Journal or Publication Title: | Journal of the Royal Statistical Society Series B: Statistical Methodology | ||||||||
| Publisher: | Wiley-Blackwell Publishing, Inc | ||||||||
| ISSN: | 1369-7412 | ||||||||
| Official Date: | January 2021 | ||||||||
| Dates: |
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| Volume: | 70 | ||||||||
| Number: | 1 | ||||||||
| Page Range: | pp. 80-97 | ||||||||
| DOI: | 10.1111/rssc.12450 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | . | ||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||
| Date of first compliant deposit: | 3 September 2020 | ||||||||
| Date of first compliant Open Access: | 16 February 2021 | ||||||||
| RIOXX Funder/Project Grant: |
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