Predicting Off-Block Delays: A Case Study at Paris - Charles de Gaulle

International Airport

Thibault Falque

1,2 a

, Bertrand Mazure

2 b

and Karim Tabia

2 c

Exakis Nelite, Paris, France

CRIL, Universit

e d’Artois and CNRS, Lens, France

Keywords:

Machine Learning, Application, Real Time Delay.

Abstract:

Punctuality is a sensitive issue in large airports and hubs for passenger experience and for controlling opera-

tional costs. This paper presents a real and challenging problem of predicting and explaining ﬂight off-block

delays. We study the case of the international airport Paris Charles de Gaulle (Paris-CDG) starting from the

speciﬁcities of this problem at Paris-CDG until the proposal of modelings then solutions and the analysis of

the results on real data covering an entire year of activity. The proof of concept provided in this paper allows us

to believe that the proposed approach could help improving the management of delays and reduce the impact

of the resulting consequences.

1 INTRODUCTION

In the context of an airport, there are several typi-

cal problems of artiﬁcial intelligence, such as plan-

ning, optimization, simulation, and prediction. In-

deed, many air transport problems exist where deci-

sion support systems are used while integrating artiﬁ-

cial intelligence components.

Before the COVID-19 health crisis, the Inter-

national Air Transport Association (IATA) forecasts

showed that passengers would double by 2036, reach-

ing 7.8 billion. The COVID-19 pandemic has slowed

air trafﬁc considerably, especially in 2020 and early

2021, but competitive pressure is always present, even

in reduced activity. In recent months, air trafﬁc has

picked up in several world regions. In France, Paris-

Charles de Gaule airport (Paris-CDG) Air France’s

main hub will see its passenger numbers increase by

35 to 40 million to reach 100 million by 2036. This

will require around 400 additional aircraft movements

(take-offs and landings) per day. The development

project for Paris-CDG airport does not include any

extension or new runway. It is, therefore, essential

to improve the following:

• passenger ﬂows by offering a simpliﬁed, ﬂuid,

and personalized route;

https://orcid.org/0000-0003-2803-1530

https://orcid.org/0000-0002-3508-123X

https://orcid.org/0000-0002-8632-3980

• the punctuality of ﬂights by anticipating delays as

far as possible;

• aircraft movements with optimized and adaptive

planning of resources such as aircraft parking lots,

check-in counters, baggage chutes, etc.

The problem of ﬂight delays, for example (landing

and take-off delays), does not only have immediate

ﬁnancial consequences. These delays can also cause

a chain effect, other delays, and problems that affect

the delays and rankings of airlines and airports.

In (Xu et al., 2008), the authors point out that

about 84% of delays are generated by the airports.

The problem of delay prediction is studied in a few

works, but it mainly concerns landing or taxiing de-

lays. The delay at departure from the parking lot (the

delay between the scheduled time and the actual time

at which an aircraft leaves its parking position or gate)

depends on several factors. Many of these are speciﬁc

to each airport (such as the amount of trafﬁc and re-

sources, passenger processes, weather conditions, air

trafﬁc control, Etc.). At Paris-CDG, there is a take-

off almost every minute, and the slightest delay can

have a cascading effect on the takeoff schedule, which

can take several hours to be resolved and return to a

normal situation. Predicting and forecasting delays in

real-time will allow us to anticipate management by

providing delay management plans and adjustments

to established schedules such as gateway, check-in,

and baggage chutes.

180

Falque, T., Mazure, B. and Tabia, K.

Predicting Off-Block Delays: A Case Study at Paris - Charles de Gaulle International Airport.

DOI: 10.5220/0011668200003393

In Proceedings of the 15th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2023) - Volume 2, pages 180-189

ISBN: 978-989-758-623-1; ISSN: 2184-433X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

One of the speciﬁcities of our work is that we fo-

cused on very ﬂuctuating data due to the COVID-19

pandemic. The main contributions of the paper are

the study of the existing situation at Paris-CDG, the

needs, and then the identiﬁcation of three main tasks

real-time prediction of parking delays, forecasting,

and explicability of predictions. Concerning the mod-

eling, we have identiﬁed ﬁve categories of data for our

problem: ﬂight data, data on the progress of passen-

ger processes (security, boarding, Etc.), weather data,

current delay status data, Etc. Finally, we have con-

ducted an empirical study to perform the data, feature,

and model selection, and we provide an overview of

the results obtained.

2 FLIGHT DELAYS: STATE OF

THE ART

The problem of aircraft delays is a well-known prob-

lem. Different models among random forests, sup-

port vector machines and logistic regression have

been studied (Natarajan et al., 2018) to predict

whether a ﬂight will be delayed. A logistic regres-

sion model to predict a class of departing ﬂights

is proposed in (Nigam and Govinda, 2017). In

(Venkatesh et al., 2017), the authors study arrival

delays and propose different approaches to predict

whether a speciﬁc ﬂight will be delayed. In (Ibrahem

et al., 2021) the authors compared different machine-

learning approaches (random forest, logistic regres-

sion, Bayesian naive classiﬁer, and decision trees)

for delay prediction on arrival. In (Tang, 2021), a

comparison of seven binary models is performed. In

(Yi et al., 2021), the authors have proposed several

stacked approaches for the Boston Logan Interna-

tional Airport ﬂight dataset from January to Decem-

ber 2019.

We also ﬁnd different studies on the impact of

different factors on ﬂight delays. For example, the

authors of (Wang et al., 2003) studied the impact

of ﬂight connections on delay. In (Markovic et al.,

2008), a statistical study on the impact of weather

at Frankfurt airport is proposed. In (Yogita Borse

et al., 2020), the authors focused on weather data

as the main feature to predict the delay class. In

(Esmaeilzadeh and Mokhtarimousavi, 2020), a sup-

port vector machine (SVM) model is used. Based on

20 days, this latter study examines some causes of

air trafﬁc delays at the three major airports in New

York City. In the study (Cai et al., 2021), a deep

learning approach for ﬂight delay prediction consid-

ering a multi-airport scenario is proposed. Regarding

regression-based approaches, the authors (Rebollo

and Balakrishnan, 2014) have proposed approaches

based on classiﬁcation and regression with random

forests for US airports.

To our knowledge, there is only one study that at-

tempts to predict takeoff delay, but it only attempts

to predict one hour before the estimated takeoff time,

and it is intended for the Maastricht (Dalmau-Codina

et al., 2019).

3 Paris-CDG AIRPORT

In this section, we provide factual information about

Paris-CDG airport. Paris-CDG airport is the most

important airport in France. It was opened in 1974

to cope with the saturation of Paris Orly airport (the

main Parisian airport before the opening of Paris-

CDG). It is located north of Paris and is the hub of

Air France. This company represents 50% of the traf-

ﬁc at Paris-CDG. Three main terminals numbered 1

to 3 makeup Paris-CDG.

At Paris-CDG, there is more than one ﬂight de-

parture per minute. There are about 720, 000 ﬂights

per year, or about 2, 000 ﬂights per day. On aver-

age, there are 145 passengers per ﬂight. At Paris-

CDG, resources are currently planned using solutions

resources are the parking lots (or ”stands”) assigned

to the ﬂights. When a parking lot is released late,

this can lead to complex scheduling changes and cas-

cading delays. It is, therefore, essential to anticipate

and predict these delays as accurately as possible, ex-

plain them, and propose actions to limit them. Before

presenting the problem, we will ﬁrst introduce some

terms. We call rotation the set composed of an ar-

rival ﬂight and a departure ﬂight. This set generally

consists of two ﬂights, but there may be only one, in

which case the ﬂight begins a new rotation. The ro-

tation period is the time between the arrival of an air-

craft (landing) and its departure (takeoff). A ﬂight has

a scheduled departure time, called SOBT (Scheduled

Off-Block Time), at which it is supposed to leave its

parking area. The moment when the ﬂight leaves its

parking position is called AOBT. The delay is then the

period between AOBT and SOBT.

3.1 Milestones Before Off-Block

We present here the main milestones preceding the

pushback of an aircraft. These milestone stands are

presented in Figure. 1. Before the ﬂight arrives at

the airport, the ﬂight estimates its arrival time at the

block (EIBT: Estimated In-Block Time). The AIBT

(Actual In Block Time) is the right time when the

Predicting Off-Block Delays: A Case Study at Paris - Charles de Gaulle International Airport

181

EIBT Period

TOBT Period

0 5 10 15 20 25 30 35 40 45 50 55 60

-2h30

SIBT

Arrival

delay

AIBT

SOBT

Departure delay

TSAT AOBT

CTOT

+2h30

Figure 1: Milestones of a ﬂight at Paris-CDG.

ﬂight arrives at its stand. We call arrival delay the

difference between AIBT and SIBT. The boarding

starts between 1 hour and 30 minutes before SOBT.

During the turn-around period (or rotation), the air-

line sends an estimated time that the aircraft is ready

(TOBT: Target Off Block Time) to the management

system. For heavy trafﬁc in the sky or congestion

on the runway, air trafﬁc control can ”slot” a ﬂight,

i.e., force its takeoff between a calculated time called

CTOT (Calculated TakeOff Time) and CTOT plus 15

minutes. If the aircraft fails to takeoff during this pe-

riod, it can be slotted again. The last milestone is the

TSAT (Target Start-Up Approval Time) is the time

provided by air control taking into account TOBT,

CTOT and/or the trafﬁc situation that an aircraft can

expect startup/pushback approval.

3.2 Off-Block Delays at CDG

Punctuality is a sensitive issue in large airports and

hubs for passenger experience and controlling costs

at the airport level. Paris-CDG is ranked

in 2018 in

10th place in terms of punctuality. Around half of the

ﬂights arrive on time, but only 20% take off on time.

A study of delays at Paris-CDG has highlighted

different causes (eg. extreme weather conditions,

congestion, breakdowns, incidents at the airport, pas-

senger processes, etc.) of these delays at different

phases (parking/pushback, taxi-ing, etc.). Figure. 2

gives an overview of off-block delays over the year

considered in our study (March 2021 - March 2022).

It should be noted that during this period, some termi-

nals were closed and are still closed, while some other

terminals have reopened. Over this period, the pro-

portion of ﬂights with off-block delays is 77%. Fig-

ure. 2b shows the number of delayed ﬂights per ter-

minal. We can observe that the two terminals corre-

sponding to Air France (2E and 2F) have the most de-

layed ﬂights. Figure. 2c shows the number of ﬂights

and the mean of delay for each terminal. Terminal 2E

has an average delay 37% above the airport average.

Figure. 2e shows the cumulated delays (in minutes)

each day of the considering period. We can observe

an increase during summer 2022, corresponding to a

resumption of trafﬁc and some terminals’ reopening.

According to OAG Flightview

Figure 2f depicts the sum and the mean of the delays

for each time band of the day, ranging from p1 (6am

- 8am) to p6 (8pm - 11pm). We can observe that the

morning periods, particularly p2 (9am - 11am) and p3

(12pm - 2pm), accumulate most of the delay. Since

the majority of delays occur in the morning and there

is a cascading delay effect, it is crucial to predict and

manage these delays in these time slots accurately.

4 PROBLEM STATEMENT AND

OBJECTIVES

This section presents the problem and the objectives

of our off-block delay prediction approach at Paris-

CDG. Recall that we call off-block delay the time sep-

arating the moment that the aircraft leaves the board-

ing gate (this operation is called pushback) from the

scheduled off-block time (between Actual Off-Block

Time and Scheduled Off Block Time).

Let Y be the target variable to predict for an input

sample describing the ﬂight under study. We distin-

guish two regression tasks:

4.1 Real-Time Off-Block Delay

Prediction

The problem considered here is the one of predict-

ing at any time t, the off-block delay Y (expressed in

minutes) that this ﬂight will indeed have. These pre-

dictions are updated every 5 minutes until the ﬂight

leaves its stand. Indeed, for every slot (each slot lasts

5 minutes), we can acquire new relevant data from

the Paris-CDG operational information system. These

data can be used to update the predictions (this is valid

for dynamic variables such as weather conditions, the

progress of passenger processes, etc.). Real-time off-

block delay predictions are intended mainly to:

• Draw managers’ attention in real-time to ﬂights

likely to have signiﬁcant delays and which may

have cascading consequences.

• Explain and identify actionable causes if neces-

sary to resolve the situation.

ICAART 2023 - 15th International Conference on Agents and Artiﬁcial Intelligence

182

(a) number of on-time ﬂights vs. number of late ﬂights.

(b) proportion of off-block delayed ﬂights per terminal.

nal.

(d) Number of ﬂights by wake category and mean of the

delay by wake category.

(e) Cummulative delay for each day of the considering

period.

(f) Cummulative and mean delay for each period of a

day.

Figure 2: Overview of off-block delays at Paris-CDG from August 2021 to September 2022.

4.2 Off-Block Delay Forecast

We call forecasts the prediction of delays before the

opening of the ﬂights. This may be a few hours or

several days before the ﬂight. In our case, the forecast

cannot rely on dynamic information on the progress

of passenger processes, weather conditions, Etc. A

such forecast may serve to :

• Establish several plans and mitigation measures

according to the expected delays. Then managers

can activate the plan provided for each situation

when it occurs.

• Identify the causes and anticipate the chaining ef-

fect and the consequences.

• Use forecasts to make plausible simulations on

Predicting Off-Block Delays: A Case Study at Paris - Charles de Gaulle International Airport

183

Table 1: Basic ﬂight features (BFF).

Feature Description Type Example

Airline airline unique company code Categorical AF

AircraftType aircraft type code Categorical 77W

Destination IATA code of the destination

airport

Categorical JFK

Terminal CDG Terminal Categorical C2E

Customs Customs Criteria Categorical Schengen

Season The IATA Season Categorical W

Week A week index calculated

from a reference date.

Numeric 1000

Day Day number in the week (1-

Numeric 1

Bus True if the ﬂight has bus ac-

cess.

Boolean True

Parking True if the arrival ﬂight and

the departure ﬂight have the

same parking

Boolean False

SOBT The Scheduled off-block

time in minutes since mid-

night

Timestamp 360

Rotation duration between landing

and takeoff in minutes

Numeric 300

Pax Count Number of passengers on the

ﬂight (estimated)

Numeric 140

Total Pif Passenger Number of passengers that

must pass through the secu-

rity point (estimated)

Numeric 75

Service Type Transport category Categorical J

congestion and queues according to delay fore-

casts, then consider solutions and management

plans.

5 MODEL DEFINITION

In this section, we present and motivate the essential

information currently available in the Paris-CDG op-

erational information system and which is likely to

be relevant for predicting off-block delays. The pro-

posed features come from the analysis of a recent re-

port on takeoff delays at Paris-CDG and the analysis

of an entire year of real data.

5.1 Basic Flight Features (BFF)

These are the basic characteristics of a ﬂight, and they

do not change over time (these features are listed in

Table 1). For example, the name of the airline operat-

ing the ﬂight, the type of the aircraft, the IATA code

of the destination of the ﬂight, the terminal, the type

of customs (national, Schengen, or international), and

the IATA season (Summer or Winter).

5.2 Off-Block Milestone Features

(OMF)

These are milestones corresponding to each off-block

and its management by the various stakeholders, such

as air trafﬁc control, the airline, and the airport.

For this study, we focused on milestones around the

SOBT (-2h30 to +2h30, see Figure. 1). This period is

split into 60 slots of 5 minutes. Each new milestone

(EIBT, TOBT, CTOT, TSAT) has a timestamp. The

OMF features are listed in Table. 2.

5.3 Previous and Current Flights Delay

Features (PCFDF)

Relevant information on the probability of a delay for

a given ﬂight is the proportion of ﬂights scheduled just

before the ﬂight under study and which are late. This

could, for example, be due to congestion, a break-

down at the airport, or extreme weather conditions.

We compute the proportion of late ﬂights and the

average duration of these delays for each ﬂight slot.

At a time t, these features are calculated during a time

window w, ranging from a few minutes to a few hours.

As we will show empirically, the optimal window du-

ration is a few minutes. For example, if w =10 min-

utes and the current slot of the ﬂight is 25, we com-

pute the different values from the ﬂights whose AOBT

is between slot 23 and slot 25.

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Table 2: Off-block milestone features (OMF).

Feature Description Type

Arrival Delay Time in minutes between the last

EIBT and the SIBT.

Numeric

TOBT

di f f

time in minutes between the last

TOBT and the SOBT.

Numeric

TOBT

count

The number of TOBTs Numeric

CT OT

di f f

time in minutes between the last

CTOT and the SOBT.

Numeric

T SAT

di f f

time in minutes between the last

TSAT and the SOBT.

Numeric

Table 3: Previous and current ﬂights delay features (PCFDF).

Feature Description Type

Delay Airport Mean off-block delay from all

the airport (regardless of the ter-

minal)

Numeric

Delay Terminal Mean off-block delay from the

same terminal

Numeric

Delay airline Mean off-block delay from the

same airline

Numeric

Percent delayed ﬂights Airport Proportion of off-block delayed

ﬂights over all the airport (re-

gardless of the terminal)

Numeric

Percent delayed ﬂights Terminal Proportion of off-block delayed

ﬂights from the same terminal

Numeric

Percent delayed ﬂights Airline Proportion of off-block delayed

ﬂights from the same airline

Numeric

5.4 Weather Condition Features (WCF)

Certain weather conditions, such as low visibility and

strong winds, are known to be factors that can cause

takeoff delays and therefore delay the departure of

the ﬂight from its stand. Table 4 shows examples of

weather-related faults.

5.5 Passenger Flow Features (PFF)

These features provide information at any time on the

progress of speciﬁc passenger processes, which may

cause an off-block delay. In particular, the relevant

information is the percentage at slot t of passengers

who have already passed boarding or passed security

checkpoints. These features are used only to predict

and update the off-block delay prediction in real-time

(each ﬂight slot).

6 DATA AND FEATURE

EXTRACTION,

PREPROCESSING AND

SELECTION

In this section, we present our main ﬁndings concern-

ing the selection of variables and the selection of data

(in particular, the choice of the best parameters for

the time window duration, the training data amount,

Etc.).

6.1 Data Extraction and Preprocessing

Paris-CDG’s operational information system (called

AOP for Airport Operation Plan) collects much in-

formation about each ﬂight and its progress. For our

prediction tasks, a new ﬂight entry is created with the

associated time stamp and updated data at each time

slot. It is, therefore, possible to trace the status of

a ﬂight back to its departure. Therefore, for static

Predicting Off-Block Delays: A Case Study at Paris - Charles de Gaulle International Airport

185

Table 4: Weather condition features (WCF).

Feature Description Type

Low Visibility Procedures These procedures are applied at

an airport to ensure safe opera-

tions when there is low visibility.

Boolean

Humidity rate (in percent) Humidity rate Numeric

Wind speed (in meter/sec) Wind speed Numeric

Air pressure (in hectoPascal) Air pressure Numeric

Temperature (in degrees Celsius) Temperature Numeric

characteristics, we extract them only once. For dy-

namic characteristics, such as delays of other ﬂights,

these are calculated variables that we perform with

queries on past ﬂights. For example, to compute

the proportion of ﬂights that have been delayed in

the last w minutes, we need to review all ﬂights in-

volved in the w time window. The delay characteris-

tics of previous and current ﬂights (PCFDF) are com-

puted after extraction with different w windows. In

our study, we considered one year of data (August

2021-September 2022) and constructed a dataset with

10, 633, 920 rows and 31 columns (each ﬂight is re-

peated 60 times with dynamic values for each slot).

6.2 Feature Selection

Once our data set was extracted and preprocessed we

proceeded to the selection of variables in order to

conﬁrm our intuitions and to eliminate attributes that

would prove irrelevant to our prediction tasks. We

ﬁrst performed a simple correlation analysis between

each characteristic and the target variable (delay to

parking departure). The Figures. 3 show the results

of the Pearson correlation coefﬁcient.

It can be seen in Figure. 3 that the most relevant

variables at the slot 0 are :

• the difference between the SOBT and the TSAT

with a score of 0.4,

• the difference between the TOBT between TOBT

and SOBT with a Pearson score of 0.33.

In contrast, the variables representing the rotation

time (Rotation) and the arrival delay of a ﬂight (Ar-

rival delay) seem to have little relevance. These vari-

ables have a negative Pearson score of −0.66 and

−0.29, respectively. For the slot 30, the order of the

essential variables is conﬁrmed. The durations be-

tween SOBT and TSAT or TOBT are the essential

variables with a Pearson score of 0.74 and 0.68.

The importance of the TSAT variable is explained

by the fact that it is one of the last pieces of infor-

mation obtained for a ﬂight before it leaves its park-

ing lot and that departure is most often at the time

indicated by the TSAT. Nevertheless, air trafﬁc con-

trol may send this information very early before the

departure of the aircraft. The variable Percent Flight

Airport, representing the proportion of delayed ﬂights

airport (for the calculation of the values, we used the

10-minute time window), becomes more important

with a progression of its score from 0.16 to 0.23. Fi-

nally, we can note that the dynamic variables (in par-

ticular, the variable concerning the progression of the

boarding) have a score in progression. This progres-

sion shows the importance of using dynamic ﬂight

data for real-time predictions.

In order to validate the ﬁndings on the correla-

tion scores obtained, we performed another empirical

analysis by varying the set of variables used for de-

lay prediction. We also varied the window w used

to calculate the dynamic variables from the nearby

history and the amount of history to use (twelve

months, nine months, six months, or three months).

We used an ensemble model of boosted regression

trees regression trees called LightGbm. In the fol-

lowing, we denote by D

the dataset with m the

number of months used (m ∈ {3, 6, 9, 12}) and w the

duration of the window used for the computation

of the dynamic variables (w ∈ {10, 30, 60}). V de-

note the set of variables used for the dataset (V ∈

{{BFF}, {BFF, WCF, OM F, PFF},

{BFF, PCFDF, WCF, OMF, PFF}}).

For our study, we tested our conﬁguration on 40

days (from August 12 to September 21, 2022). For

each day, we trained the model until the day be-

fore the test day and evaluated it on the test day.

Table 5 shows the optimal conﬁgurations. The er-

rors and R2 score presented in this table are for the

40 days tested. The hyperparameters are noted as

#Tree/#Leaves/LearningRate.

Table 5 presents the results for the different con-

ﬁgurations. The use of dynamic variables dynamic

variables bring a real gain, reducing the error by half

and signiﬁcantly improving the R2 score. Thus the

best conﬁguration uses all of the history (12 months),

with a time window of 60 minutes.

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Figure 3: Results of statistical correlation measure at slots 0 (left side ﬁgure) and at slot 30 (right side ﬁgure).

Table 5: Results of feature selection and historical data.

Dataset Features (V ) Hyperparameters MAE RMSE R2

BFF, PCFDF, WCF, OMF, PFF 75/256/0.05 9.645 13.858 0.731

BFF,WCF, OMF, PFF 75/256/0.05 9.709 14.009 0.725

BFF, PCFDF, WCF, OMF, PFF 32/64/0.05 10.376 14.500 0.705

BFF, PCFDF, WCF, OMF, PFF 32/128/0.05 10.473 14.584 0.702

BFF, PCFDF, WCF, OMF, PFF 32/128/0.05 11.637 15.414 0.667

BFF,WCF, OMF, PFF 32/64/0.05 13.922 20.812 0.393

BFF 75/256/0.05 17.443 26.576 0.0108

7 REAL TIME PREDICTIONS

Table 6 presents the mean absolute error (MAE) for

a subset of the days (for space reasons) tested with

the best model selected in the previous section. We

notice that the proposed model is more accurate and

stable in terms of error than the reference model (e.g.,

a model predicting the average delay at CDG all the

time). However, we can also notice that September

16 signiﬁcantly increased errors.

. The delays could

go up to 4h30 (the average delay over the day was

1h20). Addition, as the period covered for each ﬂight

is between -2h30 and +2h30, we only collect some of

the data on these delays.

Figure 4 gives an overview of the ﬁve most impor-

tant features for our model

. The features are sorted

by the sum of the SHAP values over all samples at slot

0 and slot 30. The color represents the value of the

feature (red corresponds to high, blue to low). This

reveals, for example, that when the slot is 30, a high

On Thursday, September 16, the delays are due to an

air trafﬁc controllers’ strike

Full ﬁgures are available in appendices.

value for TSAT increases the predicted delay. Finally,

the SHAP values largely conﬁrm the coefﬁcients of

Pearson from the 6.2 section and the importance of

the impact of the TSAT and TOBT variables.

8 CONCLUSIONS

Punctuality is a sensitive issue in major airports and

hubs for the passenger experience. In this article,

we have addressed the problem of predicting delays

at the departure of parking lots at Paris-CDG airport,

one of the largest airports in the world and the hub

of the airline Air France. Our study started with ana-

lyzing the problem (its magnitude, form, causes, Etc.)

and the needs (real-time forecasting and prediction) at

Paris-CDG. Based on this analysis, we proposed two

types of feature categories that could be useful for de-

lay prediction: static features used for the forecasting

task and dynamic features (which can be updated in

real-time) used for real-time delay prediction. The

next step was to build a pipeline to extract the raw

data we needed from the operational information sys-

Predicting Off-Block Delays: A Case Study at Paris - Charles de Gaulle International Airport

187

Table 6: MAE for off-block delay forecast.

Date MAE Baseline model

2022-08-13 8.196 18.814

2022-08-14 8.490 18.176

2022-08-15 8.026 16.877

2022-08-16 9.911 20.869

2022-08-17 8.288 18.728

2022-09-05 9.340 19.727

2022-09-10 7.429709 16.339208

2022-09-11 8.311289 19.677116

2022-09-12 7.088036 16.854864

2022-09-13 7.252672 15.125720

2022-09-14 8.613775 17.317014

2022-09-15 7.553985 17.969956

2022-09-16 31.250517 65.622123

Figure 4: SHAP values for 16th September 2022 at slot 0 (top ﬁgure) and slot 30 (bottom ﬁgure).

tem of Paris-CDG. This allowed us to build a dataset

representing one year of authentic activity. We con-

ducted an empirical study to select the characteristics

and the data and then to select the models. One of

the speciﬁcities of our work is that we worked with

very ﬂuctuating data due to the COVID-19 pandemic

pandemic and its consequences in terms of air travel

restrictions on several occasions, as well as other air

trafﬁc hazards. The results show that some delays

can be predicted much better than the baseline model.

This result can be signiﬁcantly improved by system-

atically exploring other models and their best hyper-

parameters. In addition to the accuracy improvement,

one of the crucial elements for our application will be

the explicability of the predictions, in particular, the

identiﬁcation of explanations that can help in delay

management.

REFERENCES

Cai, K., Li, Y., Fang, Y.-P., and Zhu, Y. (2021). A

Deep Learning Approach for Flight Delay Prediction

Through Time-Evolving Graphs. IEEE Trans. Intell.

Transport. Syst., pages 1–11.

Dalmau-Codina, R., Ballerini, F., Naessens, H., Belkoura,

S., and Wangnick, S. (2019). Improving the pre-

dictability of take-off times with machine learning a

case study for the maastricht upper area control centre

area of responsibility.

Esmaeilzadeh, E. and Mokhtarimousavi, S. (2020). Ma-

chine Learning Approach for Flight Departure Delay

Prediction and Analysis.

Ibrahem, A., Elbeh, H., and Mousa, H. M. (2021). A Com-

parative Analysis of Models for Predicting Airline Ar-

rival Delays. page 5.

Markovic, D., Hauf, T., R

ohner, P., and Spehr, U. (2008).

A statistical study of the weather impact on punctual-

ICAART 2023 - 15th International Conference on Agents and Artiﬁcial Intelligence

188

ity at Frankfurt Airport. Meteorological Applications,

15(2):293–303.

Natarajan, V., Meenakshisundaram, S., Balasubramanian,

G., and Sinha, S. (2018). A Novel Approach: Airline

Delay Prediction Using Machine Learning. In 2018

International Conference on Computational Science

and Computational Intelligence (CSCI), pages 1081–

1086.

Nigam, R. and Govinda, K. (2017). Cloud based ﬂight delay

prediction using logistic regression. In 2017 Interna-

tional Conference on Intelligent Sustainable Systems

(ICISS), pages 662–667.

Rebollo, J. J. and Balakrishnan, H. (2014). Characterization

and prediction of air trafﬁc delays. Transportation Re-

search Part C: Emerging Technologies, 44:231–241.

Tang, Y. (2021). Airline Flight Delay Prediction Using Ma-

chine Learning Models. In 2021 5th International

Conference on E-Business and Internet, pages 151–

154, Singapore Singapore. ACM.

Venkatesh, V., Arya, A., Agarwal, P., Lakshmi, S., and

Balana, S. (2017). Iterative machine and deep learn-

ing approach for aviation delay prediction. 2017 4th

IEEE Uttar Pradesh Section International Conference

on Electrical, Computer and Electronics (UPCON).

Wang, Schaefer, and Wojcik (2003). Flight connections

and their impacts on delay propagation. In 22nd Digi-

tal Avionics Systems Conference Proceedings (Cat No

03CH37449) DASC-03, pages 5.B.4–5.1, Indianapo-

lis, IN, USA. IEEE.

Xu, N., Sherry, L., and Laskey, K. B. (2008). Multifactor

Model for Predicting Delays at U.S. Airports. Trans-

portation Research Record, 2052(1):62–71.

Yi, J., Zhang, H., Liu, H., Zhong, G., and Li, G. (2021).

Flight Delay Classiﬁcation Prediction Based on Stack-

ing Algorithm. Journal of Advanced Transportation,

2021:1–10.

Yogita Borse, Dhruvin Jain, Shreyash Sharma, Viral Vora,

Aakash Zaveri, and K J Somaiya College of Engineer-

ing (2020). Flight Delay Prediction System. IJERT,

V9(03):IJERTV9IS030148.

Predicting Off-Block Delays: A Case Study at Paris - Charles de Gaulle International Airport

189