Predicting Trains Delays using a Two-level Machine Learning
Hassiba Laifa
, Raoudha Khcherif and Henda Ben Ghezala
RIADI Laboratory, ENSI, University of Manouba, Manouba, Tunisia
Keywords: Train Delay, Prediction, Machine Learning, Classification, Regression, LightGBM.
Abstract: Train delay is a critical problem in railway systems. A previous prediction of delays is a critical issue
advantageous for passengers to re-plan their journeys more reliably. It is also essential for railway operators
to control the feasibility of timetable realization for more efficient train schedules. This paper aims to present
a novel two-level Light Gradient Boosting Machine (LightGBM) approach that combines classification and
regression in a hybrid model. It was proposed to predict passenger train delays on the Tunisian railway.
The first level indicates the class of delay, where the delays are divided into intervals of 5 minutes ([0,5],
[6,10], …, [>60]), 13 classes in total were obtained. The second level then predicts the actual delay in minutes,
considering the expected delay class at the first level. This model was trained and tested based on the historical
data of train operation collected by the Tunisian National Railways Company (SNCFT) and infrastructure
characteristics. Our methodology consists of the following phases: data collection, data cleaning, complete
data analysis, feature engineering, modeling and evaluation. The obtained results indicate that the two-level
approach based on the LightGBM model outperforms the one-level method. It also outperformed the
benchmark models.
Rail transport in Tunisia is regarded as a significant
mode of transportation for both goods and people.
The Tunisian rail network comprises 23 lines, 2165
km, 267 stations and stops. It also has 4 road-rail links
to promote bimodal passenger transport by
combining rail and road transport. This network
ensures daily 316 train runs including 246 passenger
Punctuality is considered the primary measure of
the performance of a railway system and is an
essential factor for efficiency in the railway sector.
On the Tunisian railway, trains that had a delay more
significant than 15 minutes were considered delayed.
On the other hand, they were considered within the
given time frame if they had a delay of fewer than 15
minutes. Thus, the punctuality rate of trains
formulated by the Tunisian Ministry of transport =
(Number of trains-Number of trains> 15) / Number of
trains. For example, the punctuality of Tunisian
passenger trains in 2019 was only about 23%, which
is deemed deficient.
For the records registered by the SNCFT, these
delays have different causes, such as disruptions in
the operation flow, infrastructure problems
(construction work, repair work, accidents), and
weather conditions.
A late train is likely to propagate its delay with
other trains. Thus, managing these delays
(rescheduling) allows traffic managers to change the
direction of trains to use the rail network
appropriately. In this context, delay prediction is one
of the most significant challenges to improving traffic
management and dispatch. This prediction will
minimize delays and prevent problems in the railway
plan. It will also be of great help for passengers to
plan their itinerary according to their work, also for
traffic managers to reschedule the other trains.
Thus, this work aims to predict passenger train delays
in Tunisia. Therefore, this work presents a hybrid
classification–regression approach. A new method
Laifa, H., Khcherif, R. and Ben Ghezala, H.
Predicting Trains Delays using a Two-level Machine Learning Approach.
DOI: 10.5220/0010898300003116
In Proceedings of the 14th International Conference on Agents and Artificial Intelligence (ICAART 2022) - Volume 3, pages 737-744
ISBN: 978-989-758-547-0; ISSN: 2184-433X
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reser ved
called ‘‘two-level lightGBM’’ is proposed. At the
first level, a lightGBM classifier is applied to predict
the interval of delay ([0,5], [6,10] …). This level is
used to construct a new feature. The newly created
features should help improve the overall model
performance. A lightGBM regressor is used at the
second level to predict a delay in minutes considering
the predicted delay class at the first level. This model
was trained and tested using the historical data of train
operation collected by the SNCFT and infrastructure
characteristics information.
To validate this approach, it has been compared
with several benchmark approaches, including
random forest, support vector machine, artificial
neural network and xgboost. Furthermore, the two-
level approach was also applied to the benchmark
models to obtain a fair, balanced comparison. The
validation results indicate that the proposed two-level
lightGBM method outperforms these benchmark
approaches in prediction accuracy for both one-level
and two-level modeling. In addition, a 7%
improvement in the accuracy of the lightGBM model
after two-level modeling was observed. Additionally,
an amelioration in all benchmark models' accuracy
was observed after the two-level application.
This paper is organized as follows: We introduce
the predictive analytics process in the second section.
Section 3 presents previous research on machine
learning for passenger train delay prediction. Section
4 describes our methodology and the different phase
of its application. Then, in section 5, we evaluate the
proposed methodology and compare obtained results.
Finally, we finish this manuscript with concluding
remarks and our future perspectives in section 6.
Predictive analytics includes statistical models,
machine learning algorithms and data mining
techniques that analyze historical and real-time data
to predict future events. Predictive analytics play an
essential role in theory building, testing, and
assessing relevance. It includes two components: (1)
Predictive models, designed for predicting new/future
observations or scenarios. (2) Methods for evaluating
the predictive power (predictive accuracy) of a model
(Shmueli and Koppius, 2011). Predictive models
include (but are not limited to):
- Supervised learning: The input for training is
presented with a pair of examples containing
features (X1, X2, ..., Xn) and their desired target
(Y). The machine deals with labeled data,
meaning the target is predefined. The goal is to
learn a rule that maps inputs to outputs. It
involves two general methods, differs in the type
of target.
Classification: the target has a
categorical data type (two or more
classes); for example, predict if the train
will be delayed or not.
Regression: the targets are continuous,
for example, predict how many minutes
the train will be delayed.
- Unsupervised learning: the model work on its
own to discover and identify clusters or groups
of similar records (i.e., clustering methods such
as K-means, K-medoids, Fuzzy c-means). The
machine deals with unlabeled data, meaning the
target is not predefined.
This work aims to build a hybrid classification-
regression approach for trains delay prediction in the
Tunisian railway system.
The railway transportation systems show significant
interest in machine learning and artificial intelligence
technologies to collect, process, and analyze large
amounts of data to extract useful information. To this
end, several works have tried to establish a
relationship between the delays of the trains and the
various characteristics of the railway system and
develop different methods to construct prediction
models. Our work focused on recent research that has
developed machine-learning models to address
passenger train delay prediction.
The artificial Neural Network (ANN) model has
been intensively used in the literature to address trains
delays prediction. The authors in (Yaghini et al.,
2013) used it to predict the delay of passenger trains
in Iranian Railways. Besides, (Bosscha, 2016) aims to
expect secondary delays in a railway network using a
recurrent neural network. ANN was the most accurate
method when applied and evaluated using the
decision tree model with and without adaboost, as
demonstrated in (Nilsson and Henning, 2018).
Predictive algorithms based on artificial neural
networks (Back-Propagation Neural Network
(BPNN), Wavelet Neural Network (WNN) and
genetic algorithms (BPNN optimized by Genetic
Algorithm (GA-BPNN) and WNN optimized by
Genetic Algorithm (GA-WNN)) were applied in (Liu
et al., 2017) for train arrival time prediction, the
results showed that the GA-BPNN is the more
ICAART 2022 - 14th International Conference on Agents and Artificial Intelligence
efficient model. Support Vector Regression (SVR)
model is used for the first time in (Marković et al.,
2015) to analyze train arrival delays on the Serbian
railways where the SVR model outperformed the
ANN algorithm. The Linear Regression (LR) model
was used in (Li, Daamen and Goverde, 2016) to
predict the peak hour dwell times, while the k-nearest
neighbor (K-nn) model was employed to estimate the
off-peak-hour dwell times using data from Dutch
railway stations. Moreover, Random Forest (RF)
method was widely used in literature to predict trains
delays and has shown promising results. Three
different algorithms (Extreme Learning Machines
(ELM), Kernel Regularized Least Squares (KRLS)
and Random Forests (RF)) were applied in (Oneto et
al, 2016) to address train delay prediction problem
relying on data provided by the Italian railway system
and weather information. The performance
comparison indicates that RF consistently performed
the other algorithms. Besides, the random forest
outperformed the other evaluated methods in (Jiang
et al., 2019 ; Arshad and Ahmed, 2019 ; Li, Wen, Hu.,
Xu, Huang, and Jiang, 2020). Instead, the study
carried out in (Mou et al.,2019) proposed a Short-
Term Long Memory (LSTM) model to predict the
train arrival delay. Comparing its performance with
RF and ANN shows that the proposed model
outperformed the RF and ANN. Work in (Shi et
al.,2019) presents the first application of the Gradient
Boosting Regression Tress (GBRT) model to predict
train delay. The provided results demonstrate that the
proposed model based on GBRT had a higher
prediction precision and outperformed the SVR and
the RF models. Statistical analysis was applied in
(Kecman et al., 2015) to predict the lengths of running
and dwelling times using three global predictive
models, namely Robust Linear Regression (LTS),
Regression Trees (RT) and Random Forests (RF), and
local models applied in a particular train line, station
or block section, based on the LTS with some
refinements. These models were tested using delay
history data from Rotterdam and The Hague in the
Netherlands. The results indicate that the local
models gave better accuracy and computation time
results. A deep learning (DL) approach, namely CLF-
Net, which combines 3-Dimensional Convolutional
Neural Networks (3D CNN), Long Short-Term
Memory (LSTM) recurrent neural network, and
Fully-Connected Neural Network (FCNN)
architectures, was developed in (Huang et al.,2020) to
predict train delay of two high-speed rail (HSR) lines
in China.
Furthermore, some researches combine two or
more models, such as in (Lulli et al., 2018) where a
hybrid approach that combines the Decision Tree
(DT) and Random Forest regression (RF) was
proposed to predict the running time, the dwell, the
train delay, and the penalty costs. The authors in (Nair
et al., 2019) also addressed the problem of forecasting
train delays up to 24 h in advance by applying a data-
driven method that combines a set of simulation and
statistical approaches as an ensemble method. The
proposed method was tested using extensive data
from the train network of Germany, and the obtained
results demonstrate that the process based on
ensemble outperformed the component models.
Furthermore, a coupled classification–regression
model was proposed in (Nabian et al., 2019) where a
bi-level random forest was formed of: i) a random
forest classifier in the first level to predict whether a
train delay will increase, decrease, or remain
unchanged; and ii) a random forest regressor to
estimate delay in minutes given the predicted delay
class at primary level. Further, a two-stage prediction
model is built-in (Gao et al., 2020). The first stage
predicts the total buffer time of delayed trains in
sections and stations, and the second stage predicts
the recovery time of primary delay based on the first
stage results.
Our previous work (Laifa et al., 2021) presents
the first application of the LightGBM algorithm to
predict trains delays using real Tunisian railway
network data records. Our method based on
LightGBM regressor had outperformed the tested
models, namely ANN, XGBoost, RF and SVR.
We can conclude that different machine learning
methods have been widely used in train delay
predictions. However, the outperformed model
differs from one study to another, depending on the
used data case considering the unique features of
different railway networks.
To predict delays in the Tunisian railway system, we
proposed an approach that consists of four main steps
Data collection,
Data preparation including data cleaning,
visualization and feature engineering.
The proposed approach is presented in Figure 1.
Predicting Trains Delays using a Two-level Machine Learning Approach
Figure 1: Proposed approach.
4.1 Data Collection
The used database is collected from the National
Tunisian Railway Company (SNCFT). It consists of
12350 travel samples, from 1.1.2019 to 31/12/2019,
including 55 passenger trains and 4 main destinations
(Tunis-Nabeul, Tunis-Sousse, Tunis-Tozeur, Tunis-
Sfax). The dataset has the following features:
Table 1: Data features summary.
Features Definition
Train Unique code of each
Code_cir Running frequency
(daily, only Saturday
and Sunday, etc.
Line The railroad took by the
Direction Railroad is a single-
track or a double-track.
Destination Departure station to
target station
Distance The traveling distance
Nbr_station Number of stations and
stops between the
departure and the target
Date Date of travel
Weekday Day of travel (for
Monday to Sunday)
Holiday A boolean variable
indicates if the journey
is a holiday or not.
Month Month of travel (from
January to December)
Season Season of travel
(Winter, Spring,
Summer or Autumn)
Motifs The reason behind the
It shows how much time
(in minutes) a train
takes to begin its new
journey after the
scheduled departure
The time when the train
The time at which the
train arrives at a given
Reveals how much time
(in minutes) a train
takes to arrive after the
scheduled arrival time
(Our target variable).
4.2 Data Preparation
The collected data suffer from some problems that
why a cleaning step is inevitable to improve data
quality before model training. Therefore, we have
applied a cleaning operation set to solve the data
shortcomings in this phase. It deals with null values,
handling outliers and transforming incorrect format.
For null values, we dropped records with a null
value in Arrival_delay. The remaining was filled
either with the median in numerical feature (as to
outliers’ existence as observed in Figure 2) or with an
"Unknown" value if the feature was categorical.
Figure 2: Departure delay histogram.
Then we transformed the data type of departure
delay from an object into numeric. Finally, all
cleaning operations were executed using the Python
Data cleanin
Data visualization
Features engineering
Trains operations
records from
New feature construction
ICAART 2022 - 14th International Conference on Agents and Artificial Intelligence
Where the cleaning step finished, we visualized
our data to understand better and find the features that
affect train delay. The data were evaluated in various
ways, including univariate, bivariate, and
multivariate analysis. For example, to discover the
relationship between all dataset features, we
implemented the correlation matrix following in
figure 3. The light color presents a strong correlation
between the variables corresponding to the x-axis and
the y-axis. The lighter the color of the square, the
more the correlation is positive. Conversely, the dark
color has a weak correlation, the darker the color of
the square, the more negative the correlation.
Figure 3: Correlation matrix.
Visualizations processes were applied using the
Python Library 'matplotlib'. Then, the features likely
to affect train delays were selected as the model
features (inputs(X)).
The model can only analyze digital data, for this,
a feature engineering phase is necessary to convert
the categorical columns into numerical values.
Therefore, we applied cyclic encoding to transform
cyclic variables (such as weekdays, months and
season), one-hot-encoding, presented by the
"get_dummies()" function, to convert categorical
variables that have fewer than five values and target-
encoding for the remaining categorical variables
(trains and motifs).
Table 2 summarize our feature engineering phase
Cyclic-enc =
cyclic encoding,
One-h-enc =
Target-enc =
Type-tf =
type transformation, Cat =
Flt = float, Obj =
object, Int = integer.
Table 2: Features engineering summary.
Features Initiate
Trains Cat Target-enc Flt
Motifs Cat Target-enc Flt
Destination Cat One-h-enc Int
Direction Cat One-h-enc Int
Line Cat One-h-enc Int
Weekday Cat Cyclic-enc Int
Month Cat Cyclic-enc Int
Season Cat Cyclic-enc Int
Holiday Cat One-h-enc Int
Obj Type-tf Flt
Obj Type-tf Flt
4.3 Modeling
LightGBM algorithm (Ke et al., 2017) is a gradient
boosting framework that uses a histogram-based
decision tree learning algorithm. It is based on two
novel techniques: Gradient-based One-Side Sampling
(GOSS) and Exclusive Feature Bundling (EFB). With
GOSS, a significant proportion of data instances with
small gradients is excluded to reduce the number of
data instances. Only the rest is used to estimate the
information gain. However, the EFB, which consists
of bundling exclusive features, is employed to
effectively reduce the number of features.
It is considered an efficient model that can handle
large-scale data and achieve better accuracy with
faster training speed and minimum memory usage. It
also supports parallel and distributed learning.
From these advantages, LightGBM is widely used
in many research areas and has shown promising
results in different machine learning tasks.
Additionally, this algorithm was appropriately
applied in our study because most of the features in
our datasets had a categorical data type, and the
number of features was augmented after the features-
engineering step.
We proposed in this paper an approach that mix
lightGBM classifier and lightGBM regressor to
predict train delays. The following figure details the
Predicting Trains Delays using a Two-level Machine Learning Approach
modeling phase, where the hybrid approach is
Figure 4: Two-level modeling.
This phase presents a novel contribution where we
proposed and implemented a classification model as
a first level of learning that classifies the delays in
intervals of 5 minutes.
After the cleaning and features engineering
phases, the obtained data are passed to classification
modeling, where we deployed a classification model
based on a lightGBM classifier algorithm that
classified the delays in intervals of 5 minutes ([0,5],
[6,10],…, [>60]), a total of 13 classes were obtained.
We chose short intervals so that the model is more
precise and that the information is not lost, i.e., a
delay between 0 and 5 and more accurate than a delay
between 0 and 10 or between 0 and 15.
Then, the predicted classes of all rows are added
to the initial data as a new feature.
In the second level of learning, we implemented a
regression learning based on a lightGBM regressor to
predict the delay in minutes. The new data include the
newly created features that should be useful in
improving the overall model performance at this level
of learning.
4.4 Evaluation
4.4.1 Benchmarks Models
To evaluate our proposed approach, we used the
following benchmarks models:
Support Vector Regression (SVR) (Smola
and Schölkopf, 2004).
Random forest (RFR) (Breiman, 2001).
Extreme Gradient Boosting (XGboost)
(Chen and Guestrin, 2016).
Artificial Neural Network (ANN) (Hopfield,
The processed data were separated into 80 % for
training and 20% for testing to apply the models. We
used the official python implementation of the
Lightgbm model
. All the experiments were
conducted on an I7 3.2 GHz 8-core CPU and 16 GB
of memory.
The default hyperparameters were applied in
LightGBM, XGBoost, Random Forest and SVM
We applied testing values for the ANN
hyperparameters to choose the optimal one for each
hyperparameter. Table 3 summarizes all
combinations of hyperparameters values with the
optimal one.
Table 3: ANN hyperparameters summary.
Hyperparameter Tested values Optimal value
Epoch 50, 100, 150 150
Hidden layers 1, 2, 3 1
Input layer neurons 16,32,64, 128 64
Hidden layer neurons 16,32,64, 128 32
Batch Size 32, 50, 100 50
Drop-out 0, 0.1, 0.2 0.1
Activation Function / Relu
/ Adam
4.4.2 Evaluation Metrics
Three evaluation factors were employed in this study
to evaluate the performance of the applied models,
namely R-squared (R²) (1), Mean absolute error
(MSE) (2) and Root Mean Squared Error (RMSE) (3).
These statistical parameters are defined as follows:
 =
 =
where n denotes the Number of target values y = (
, . . .,
) and ŷ is the predicted value of y.
ICAART 2022 - 14th International Conference on Agents and Artificial Intelligence
Table 4 present the performance of the proposed
hybrid approach of the lightGBM algorithm (2L-
LGBM) in terms of R², MAE and RMSE in both
training and test data. As followed in the table, the
accuracy of the proposed approach reached 87% in
test data and as is observed, the training model hasn't
been overfitted in the learning process.
Table 4: Two-level LightGBM performance.
87.83 6.10 11.91
87.17 6.84 13.37
Our previous work (Laifa et al., 2021) presented a
one-level learning approach where we applied the
regression level directly. However, the actual work
differs from that with two levels of learning and the
input data of regression level has an additional
feature, namely “Interval” created by the first level.
Table 5 presents the results of our previous work
where the lightGBM regressor (LGBM) is compared
with Support Vector Machine regressor (SVM),
Random Forest regressor (RF), Extreme Gradient
Boosting regressor (XGB), Artificial Neural Network
regressor (ANN) algorithms using test data.
Table 5: One-level approaches performance.
Models R-squared Running time (s)
LGBM 80.31 0.67
ANN 78.92 45.41
RF 77.11 17.98
XGB 76.44 4.03
SVM 76.03 10.89
It is clear that the lightGBM outperforms the other
tested model in terms of R-squared and is the faster
one with minimum running time.
Table 6 compares the performance results when
applying the two-level modeling to all the tested
models in test data. Two-level LGBM (2L-LGBM),
two-level NN (2L-NN), two-level RF (2L-RF), two-
level XGB (2L-XGB), two-level (2L-SVM).
Observing Tables 5 and 6, we see that the
lightGBM model outperforms the benchmark models
in both one-level and two-level learning approaches.
We can also see by comparing Tables 5 and 6 that the
two-level approach of all models is accurate better
Table 6: Two-level results for all models.
2L-LGBM 87.52 6.84 13.37
2L-ANN 81.26 9.30 16.15
2L-RF 83.77 7.81 15.03
2L-XGB 84.58 7.47 14.65
2L-SVM 80.55 9.35 16.46
than the one-level approach. Specially, around 7%
improvement in LightGBM model performance, 8 %
for XGBoost, 4% for SVM, 6% for Random forest
and 3% for ANN after two-level modeling.
We proposed in this paper a hybrid approach with two
levels of learning, namely a two-level system.
Primary level introduced in classification task that
constructs new feature to improve prediction
accuracy. In this level, the class of delay, categorized
in intervals of 5 minutes, is predicted. The new
feature was added to the initial dataset. The secondary
level presented in regression learning indicates delays
in minutes. The proposed approach was trained and
tested using historical data of train operation collected
by the SNCFT of Tunisia. It consists of four main
steps: data collection, data preparation; includes data
cleaning, visualization and feature engineering; two-
level modeling and evaluation. The statistical results
indicate that the approach based on two levels of
learning performs better than that one-level learning.
We also found that the model based on the lightGBM
algorithm outperforms all tested models in both two-
level and one-level learning. The prediction accuracy
of the proposed approach reached 87 %, which
improves the prediction accuracy effectively.
Our upcoming work will focus on
hyperparameters optimization and features selection
techniques for better performance; furthermore, we
will integrate external data sources that can impact
train delays, such as weather information, to improve
prediction accuracy.
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