SEMI-PARAMETRIC ESTIMATION OF A SPATIO-TEMPORAL HAWKES PROCESS FOR MODELLING CAR ACCIDENTS
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We propose a semi-parametric spatio-temporal Hawkes process to model the occurrence
of car accidents over general domains.
The overall intensity is split into the sum of a background component capturing the
spatio-temporally varying intensity and an excitation component accounting for the
possible triggering effect between events.
The spatial background is estimated and evaluated on an approximation to the road
network, allowing the derivation of accurate risk maps of road accidents. We ensure that
the spatio-temporal excitation preserves an isotropic behavior and we generalize it to
account for the effect of covariates.
The estimation is pursued maximizing the expected complete data log-likelihood using a
tailored version of the stochastic reconstruction algorithm that adopts ad-hoc boundary
correction strategies.
Two applications analyse car accidents occurred on the London M25 Orbital in 2018 and
on the Rome road network in years 2019, 2020, and 2021.
Results highlight that car accidents of different types might exhibit varying degrees of
excitation, ranging from no triggering to an 8% chance of triggering one further event.
Blended ROOM 34 BUILDING CU002, FLOOR 04 - Participate with zoom ID riunione: 836 2500 4899 Passcode: 123456
Relatore:
Pierfrancesco Alaimo Di Loro
Data:
22/11/2024 - 12:00
Luogo:
[Blended ROOM 34 BUILDING CU002, FLOOR 04 - Participate with zoom ID riunione: 836 2500 4899 Passcode: 123456]
Allegati:
