Stochastic Modelling of Train Delay Time Series in Skåne, Sweden
(2018) STAH11 20172Department of Statistics
- Abstract
- The purpose of this paper is to provide a foundation for modelling train delays as multivariate time series. A pertinent issue with this kind of analysis is that the individual time series do not follow the Gaussian normal distribution. Since the normal distribution constitutes an assumption of classical time series methods, these cannot be applied blindly to the data. The solution explored by the paper is to identify the distributions of the time series and subsequently transforming the data into normal distributions.
To this end, a dataset from the Swedish Transport Administration was used containing delays for every train that either departed from the region Skåne as a first departure, or arrived in Skåne as a final destination. The... (More) - The purpose of this paper is to provide a foundation for modelling train delays as multivariate time series. A pertinent issue with this kind of analysis is that the individual time series do not follow the Gaussian normal distribution. Since the normal distribution constitutes an assumption of classical time series methods, these cannot be applied blindly to the data. The solution explored by the paper is to identify the distributions of the time series and subsequently transforming the data into normal distributions.
To this end, a dataset from the Swedish Transport Administration was used containing delays for every train that either departed from the region Skåne as a first departure, or arrived in Skåne as a final destination. The dataset spans from January 1st, 2014 to December 31st, 2016. Time series were constructed for every station with a significant amount of data points by computing the mean daily delays for these stations. Using software for distribution fitting it was found that the asymmetric Laplace distribution best described the distribution of the train delays. The data was successfully transformed into a normal distribution using the empirical cumulative distribution function of the asymmetric Laplace distribution. Comparing the cross-correlation functions before and after the transformations showed mild increases in the time dependence between stations. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8932612
- author
- Vestin, Filip LU and Zulj, Valentin LU
- supervisor
- organization
- course
- STAH11 20172
- year
- 2018
- type
- M2 - Bachelor Degree
- subject
- keywords
- Stochastic models, train delays, time series
- language
- English
- id
- 8932612
- date added to LUP
- 2018-02-02 13:41:57
- date last changed
- 2018-02-02 13:41:57
@misc{8932612, abstract = {{The purpose of this paper is to provide a foundation for modelling train delays as multivariate time series. A pertinent issue with this kind of analysis is that the individual time series do not follow the Gaussian normal distribution. Since the normal distribution constitutes an assumption of classical time series methods, these cannot be applied blindly to the data. The solution explored by the paper is to identify the distributions of the time series and subsequently transforming the data into normal distributions. To this end, a dataset from the Swedish Transport Administration was used containing delays for every train that either departed from the region Skåne as a first departure, or arrived in Skåne as a final destination. The dataset spans from January 1st, 2014 to December 31st, 2016. Time series were constructed for every station with a significant amount of data points by computing the mean daily delays for these stations. Using software for distribution fitting it was found that the asymmetric Laplace distribution best described the distribution of the train delays. The data was successfully transformed into a normal distribution using the empirical cumulative distribution function of the asymmetric Laplace distribution. Comparing the cross-correlation functions before and after the transformations showed mild increases in the time dependence between stations.}}, author = {{Vestin, Filip and Zulj, Valentin}}, language = {{eng}}, note = {{Student Paper}}, title = {{Stochastic Modelling of Train Delay Time Series in Skåne, Sweden}}, year = {{2018}}, }