Estimating long-term delay risk with Generalized Linear Models

Marie Milliet de Faverges Giorgio Russolillo 1 Christophe Picouleau 1 Boubekeur Merabet 2 Bertrand Houzel
1 CEDRIC - Centre d'études et de recherche en informatique et communications
2 SNCF Réseau [La Plaine st Denis]
Abstract : This paper presents an original methodology to estimate delay risk a few days before operations with generalized linear models. These models represent a given variable with any distribution from the exponential family, allowing to compute for any subject its own probability distribution according to its features. This methodology is applied on small delays (less than 20 minutes) of high-speed trains arriving at a major french station. Several distributions are tested to fit delay data and three scenarios are evaluated: a single GLM with a negative binomial distribution and two two-part models using both a logistic regression as first part to compute the probability of arriving on time, and a second part using a negative binomial or a lognormal distribution to obtain the probabilities associated with positive delay values. This paper also proposes a validation methodology to assess the quality of these probabilistic predictions based on two aspects: calibration and discrimination.
Keywords : Railway punctuality Generalized Linear Models Delay modeling Zero-inflated data
Document type :
Conference papers
Complete list of metadatas

Cited literature [22 references]  Display  Hide  Download
https://hal-cnam.archives-ouvertes.fr/hal-02473719
Contributor : Marie Milliet de Faverges <>
Submitted on : Monday, February 10, 2020 - 8:50:53 PM
Last modification on : Saturday, February 15, 2020 - 1:36:58 AM

File

MILLIET_Marie_ITSC2.pdf
Files produced by the author(s)

Identifiers

Collections

CNAM | INSMI | CEDRIC-CNAM | UNIV-HESAM

Citation

Marie Milliet de Faverges, Giorgio Russolillo, Christophe Picouleau, Boubekeur Merabet, Bertrand Houzel. Estimating long-term delay risk with Generalized Linear Models. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Nov 2018, Maui, France. pp.2911-2916, ⟨10.1109/ITSC.2018.8569507⟩. ⟨hal-02473719⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

7

Files downloads

3

Contact

Informations

CCSD