a collision risk assessment method for runway threshold

This document is downloaded from DR‑NTU (https://dr.ntu.edu.sg)Nanyang Technological University, Singapore.

A collision risk assessment method for runwaythreshold management : a case study ofSingapore Changi Airport

Ang, Haojie; Cai, Qing; Alam, Sameer

2020

Ang, H., Cai, Q. & Alam, S. (2020). A collision risk assessment method for runway thresholdmanagement : a case study of Singapore Changi Airport. 9th International Conference forResearch in Air Transportation.

https://hdl.handle.net/10356/152381

© 2020 International Conference for Research in Air Transportation (ICRAT). All rightsreserved. This paper was published in proceedings of 9th International Conference forResearch in Air Transportation and is made available with permission of InternationalConference for Research in Air Transportation (ICRAT).

Downloaded on 31 Oct 2021 03:53:02 SGT

9th International Conference for Research in Air Transportation

A Collision Risk Assessment Method for RunwayThreshold Management: A Case Study of Singapore

Changi AirportHaojie Ang, Qing Cai and Sameer Alam

School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore

Abstract—Airports are indispensable infrastructures in an airtransportation system with runways being the most critical com-ponent serving departures and arrivals. With constant increasein demand of air traffic, much effort has been made to managethe runway capacity to improve the throughput of airports.Apart from operational changes, there is a significant investmentin runway infrastructure improvements such as new runwaydevelopment/extension. However, many runways suffer from longrunway thresholds due to safety constraints in face of approachpath obstacles, which leads to reduction in Landing DistanceAvailable (LDA), thereby reducing runway capacity. This paperproposes a method to manage the runway threshold by computingand assessing the collision risk of a given flight approach pathwith an obstacle profile. To do so, we develop the arrival flightprofile along with the altitude distribution the landing aircraftusing ADS-B data. We then factor in the height of obstacleswith reference to the obstacle surface profile. The convolutionof two distribution is then used to assess the collision riskbetween the aircraft on approach path and the obstacle for bettermanagement of runway threshold. The proposed model is appliedat Singapore Changi Airport, which has a long runway thresholddue to the ship movements in the Strait of Singapore, which areconsidered as safety risks to the landing aircraft. Results suggestthat, for CAT I/II approaches, with aircraft having aerodromereference code 3/4, the runway threshold for runway SingaporeChangi Airport 20R can safely be reduced by approximately 100meters, while meeting the safety requirements. This increases theLanding Distance available and may lead to an increase runwaycapacity.

I. INTRODUCTION

Air traffic plays an important role in the growing economyof the world [1, 3]. According to International Air TransportAssociation (IATA), the amount of passengers is predicted tohit 8.2 billion by the year 2037 based on the present trend [4].With the continuous increase in air traffic demand, congestionat airports is inevitable [5] which contributes to the safety risksof air traffic. Therefore, airports have to come out with newplans to mitigate air traffic congestion [9, 11] so as to ensurethe system safety of air traffic. Note that runways is one ofthe most critical components in effecting airport capacity [13].Therefore, in order to accommodate more air traffic, one of themost effective ways is efficient management of runways [14].

Tremendous effort has been made towards efficient runwaymanagement. Existing endeavours can be roughly categorisedinto two classes, viz., runway operational/procedural improve-ments [15, 16] and runway infrastructure improvements. From

the operational/procedural perspective, research such as Run-way Configuration Management (RCM), Arrival DepartureRunway Balancing (ADRB) [17–19], Runway OccupancyTime management [20], System Oriented Runway Manage-ment (SORM) [21], etc., have been developed to improvethe runway management for airports [13, 22, 24]. However,there is limited improvement that these approaches can bringabout due to complex interactions of several sub-systems inair traffic management which limits the full realization of sucheffort. Compared to operational and procedural improvements,runway infrastructure improvements such as extending existingrunways and building new ones could be more straightforwardand effective to cope up with the air traffic demand. However,there are many factors which are required to be consideredbefore proceeding. For example, building a new runway hasto take into account the cost, noise effect, local environmentalconcerns, etc., while extending a runway may subject to lim-ited land availability and safety concerns (converging runways,availability of Stop-Way, Clearway and Runway Safety Area,etc.).

Due to the limited space of cities and also to reduce thenoise effect of the aircraft that can cause disturbance to thecity residents, many airports/runways such as Kansai Inter-national Airport, Hong Kong International Airport, IncheonInternational Airport, Singapore Changi Airport etc., are builton reclaimed land [27, 28]. One of the major safety risksfor those runways is the movements of the ship in the seawhich can interfere in the approach path of arrivals. In order tomaintain the safety standard, a displaced threshold is stipulatedto the runways to avoid collision between the arrival flightsand the ship. However, most of those runway thresholds weredeveloped in a conservative manner leading to a runwaythreshold that takes away a considerable runway length. Forexample, the runway threshold for Macau International Airportis 364m and Singapore Changi International Airport is 740m.If the threshold of a runway can be managed (reduced) theLanding Distance Available (LDA) can be increased, and asa consequence, the runway capacity may be increased due toincreased availability of runway exits (previously inaccessi-ble), wide-body aircraft operations (which may require longerLDA), and runway safety (reducing the chances of runwayexcursions) [29]. Thus, there is a window of opportunity for

several such airports (build on reclaimed land on sea) in theworld, to manage the runway thresholds efficiently, in orderto improve capacity and safety.

This work aims to manage the runway threshold in order toincrease the LDA. To do so, we propose a data-driven methodand a computational model to reduce the runway thresholdby making use of the flight trajectory data and the obstructionsurface profile as per the ICAO standards on Approach SurfaceObstacles [31]. The proposed model allows for flight pathobstruction analysis leading to reduction in runway thresholddistance. To validate the effectiveness of the proposed method,we carry out a case study on Singapore Changi InternationalAirport, hereafter termed as Changi Airport. Changi Airport,being one of the major air transport hub in Southeastern Asia,had handled over 380,000 aircraft movements in 2018 andmay also face with traffic congestion in the future due to theincreasing air traffic demand [32]. Changi Airport is one ofthe airports that is built on a reclaimed land and is situatedat the eastern end of Singapore which is surrounded by theSingapore Strait [33]. Fig. 1 shows the runway 20R, whichis of interest, and the surrounding Singapore Strait. Due tothe ship movements in the Singapore Strait, a permanentlydisplaced threshold is provided for runway 20R.

Ship

Singapore Strait

Fixed runway

threshold (740m)

Runway (4000m by 60m)

Fig. 1. Runway 20R and the surrounding Singapore Strait in which the shipmovements result in a permanently displaced threshold of 740m.

Due to the unique geographical location of Singapore,ship movements in the Singapore Strait are considered as anobstruction to surrounding airspace due to the height of theship. As runway 20R has a heading facing the Singapore Strait,a permanently displaced threshold of 740m is regulated forthat runway [35]. This is determined by imposing a highlyconservative maximum safety limit based on the height ofship, that passes through the Singapore Strait. However, thisconservative set up may result in a loss of runway length(LDA) which leads to lower runway capacity.

With the increased availability of ADS-B data in approachand landing phase, we now have a better insight into aircraftlanding profile. Further the ship movement data in SingaporeStrait along with the tidal waves data which is available fromMeteorological Service Singapore and Windfinder, can allowus to better model the approach obstructions profile such thatthe runway threshold can be better managed.

The remainder of this paper is organized as follows. Sec-tion 2 illustrates the research background that is required tounderstand the research problem intended to address. Section 3

describes the research methodology and the developed methodfor runway threshold management. Section 4 presents theexperimental settings for the application of our proposedmethod to Changi Airport while section 5 demonstrates theexperimental results. Concluding remarks are then providedin Section 6.

II. RESEARCH BACKGROUNDS

A. Obstacle Limitation Surfaces

Obstacles surrounding the aerodrome impact the aircraftlanding and take-off and are the main factors for determiningthe runway threshold configuration. In this section we intro-duce the concept of obstacle limitation surfaces around therunway based upon the ICAO Annex 14 Aerodromes (ObstacleRestriction and Removal), hereafter referred to as Annex-14.

An aerodrome is a defined area on land or sea where flightoperations take place. Annex-14 defines the airspace aroundthe aerodromes to be kept free from obstacles to allow theusual operations of aircraft to operate safely and to eliminatethe chance that aerodromes become unusable due to theconstruction of obstacles around the aerodromes. Therefore,different types of obstacle limitation surfaces (OLS) and theirrespective limits are established to which an object may extendinto the airspace. There are 9 different surfaces namely outer-horizontal surface, conical surface, inner-horizontal surface,approach surface, inner-approach surface, transitional surface,inner-transitional surface, balked-landing surface and take-offclimb surface [31]. Fig. 2 illustrates the different types of OLSaround the runway. These are imaginary surfaces which mustnot be violated by any obstacle.

Conical

Transitional

ApproachApproach

Inner approach Strip

Inner horizontal

Conical

Take-off climb

B

A A

B

Approach Take-off climb

Inner horizontalConical

Conical

Transitional

Section A-A

Section B-B

Approach Transitional

Inner horizontal

Inner approach

See Figure 4-2 for inner transitional and balked landing obstacle limitation surfaces andAttachment B for a three-dimensional view

Fig. 2. An illustration of different types of OLS around the runway [31].

The dimensions of OLS are specified based on differenttypes of approach runways classification and the aircraft aero-drome reference code number. The different classification ofapproach runways is non-instrument, non-precision approachand precision approach. The aircraft reference code number isclassified based on its highest value of the aeroplane referencefield length for which the runway is intended [31]. In thiswork, we are assuming the aircraft is wide-body of codenumber 3/4 performing a landing approach on the precisionapproach CAT I/II runway. Table I shows the dimension

and slopes of the approach surface based on the aerodromereference code of aircraft and the precision approach CAT I/IIrunway.

TABLE IDIMENSION OF APPROACH SURFACE BASED ON PRECISION APPROACH

CATEGORY AND AERODROME REFERENCE CODE NUMBER [31].

Precision Code Element 1 ApproachApproachCategory

CodeNumber

Aeroplane referencefield length (m)

Distance fromthreshold (m) Slope

I 1 (0, 800) 60 2.5%I 2 [800, 1 200) 60 2.5%

I/II/III 3 [1 200, 1 800) 60 2%I/II/III 4 [1 800, +∞) 60 2%

In this paper, we are assuming the runway is under precisionapproach CAT I/II, where the approach surface is an inclinedplane or combination of planes directly after the runwaythreshold. The inner approach surface is a rectangular portionof the approach surfaces directly after the runway threshold.

B. Displaced Runway Threshold and Landing Distance Avail-able

A displaced runway threshold is a threshold located at apoint on the runway other than the designated beginning of therunway. The portion of the runway that is behind the displacethreshold can only be used for taxi/takeoff on both directionand landing on opposite direction [36]. Aircraft that is landingon the runway with a displaced runway threshold can onlyland beyond the displaced threshold marking. The LandingDistance Available (LDA) is defined as the length of runwaydeclared available and is suitable for the landing of aircraft.The LDA may be less than the actual length of the runway orthe length of the runway directly after a displaced thresholdnot including the Stop-Way and Clearway. Fig. 3 illustrates thedisplaced threshold markings and the LDA of a runway. Fig.3(a) illustrates the most common type of runway threshold donot have a displacement and its LDA. Fig. 3(b) illustrates therunway with a displaced threshold and its LDA.

(a) LDA

(b) LDA

20R

02 L

20R

02 L

Fig. 3. Graphical illustrations of runway markings. (a) Runway threshold thatdo not have displacement and its LDA, (b) Displaced threshold and its LDA.

III. PROBLEM DESCRIPTION AND METHODOLOGY

A. Problem Description

This work aims to reduce the length of the displaced thresh-old of a runway after accounting for the obstruction surfaceprofile. Without loss of generality, throughout the paper weconsider airports that are build on the reclaimed land overthe ocean where ship movement is frequent. Consequently,we consider such ship movements as the obstructions to theaerodrome approach segment.

Currently, the runway of such airports has a displacedthreshold to prevent the collision of the aircraft with theships in the strait during landing. However, the displacedthreshold may be a very conservative due to the impositionof the maximum safety limit based on the height of theship. Therefore, if the maximum safety limit can be reduced,the displaced runway threshold can also be reduced. Fig. 4illustrates the idea of reducing the maximum safety limit toreduce the displaced threshold of a runway.

Maximum safety limit

Approach surface boundary

Reduced safety limit

Reduced runway threshold

Original runway threshold Strait

Fig. 4. The idea of reducing runway threshold length by reducing themaximum safety limit.

To determine acceptable safety limit, the collision riskbetween the aircraft and ships must be computed. The crucialdata which are required to determine the collision risk is thevariations in the height of the ship (due to tidal waves) andthe vertical deviations of the aircraft landing trajectory.

B. Methodology

Fig. 5 presents a graphical overview of the proposedmethod. It can be seen from Fig. 5 that the proposed methodinvolves three phases, i.e., the preliminary phase, the develop-ing phase and the experimental phase.

1

Maximum height of the Inner approach surface

Maximum height of the ship

Landing trajectory of aircraft

Fixed Runway Threshold (740m)

Normal Distribution of the height of the

ship

Normal Distribution of the altitude of the

aircraft

Collision risk

Preliminary Phase

Developing Phase

Experimental Phase

Collision risk for each of the 3 shipping lanes

Analysis of the critical lane to predict the length of runway threshold that can be reduced

Fig. 5. Overview of the proposed method for reducing the runway threshold.

In the preliminary phase, the maximum height of theapproach surface, maximum height of the ship and the landingtrajectory of the aircraft are being determined. Next, during thedeveloping phase, the normal distribution of the ship’s heightand the aircraft’s altitude is derived and the collision riskbetween the ship and aircraft is further determined. Finally, inthe experimental phase, the collision risk calculation method isapplied individually to different shipping lanes to obtain their

corresponding collision risk. By analysing the collision risk ofthe critical lane, the length of runway threshold that can bereduced is estimated.

C. Research Assumptions and Reasons

In order to solve the research problem given in the abovesubsection, the following assumptions together with theircorresponding reasons are provided.

1) One of the fundamental assumption in the current workis that there is a maximum safety distance limit (see Fig.4) between the ship and the aircraft. This assumption ismade to determine the collision risk in a safer approach.

2) The mean height of the ship is assumed to be maximumheight of the approach surface and it can be generated toa normal distribution after accounting for the height ofthe tidal waves. This assumption is made due to the factthat the height of the ship cannot be above an approachsurface as state in the Annex-14.

3) The variations of the height of the ship and the altitudeof the aircraft from the landing trajectory are assumed tofollow normal distributions. On one hand, Gaussian dis-tribution has very good mathematical properties. On theother hand, the occurrences of many natural phenomenaare reported to obey Gaussian distributions.

4) The standard deviation of the altitude of the aircraftat a certain latitude is assumed to remain unchangedwhen the runway threshold in reduced. This assumptionis made as the standard deviation data for lower altitudeis not available at the same latitude.

IV. EXPERIMENTAL DESIGN

In this paper, we are using Changi Airport as a case studyto estimate the length of runway threshold that can be reducedby accounting the collision risk between the aircraft and theships in Singapore Strait. Runway 20R is the only runway inSingapore that has a displaced threshold due ship movementsin the Singapore Strait. Therefore, the ships in the Singaporestrait are considered as the obstruction for the approach aircraftand a displaced threshold is required to clear the obstruction.In this section, the detailed experimental design is presented.

A. Runway 20R of Changi Airport and Singapore Strait

The Singapore Strait consists of 3 shipping lanes thatfacilitate ship movements. The runway threshold of runway20R is determined by the maximum height limit of the shipin the Singapore Strait. Fig. 6 shows the runway threshold ofrunway 20R and the lanes in Singapore Strait. The table on thebottom right of Fig. 6 shows the latitude of the starting/middlepoint of each shipping lane together with its distance to thethreshold of the runway.

Lane 2Lane 1

Lane 3

Runway Threshold

Lane 2 starting point

Lane 1 middle point

Lane 3 starting point

2610m

2440m

2220m

LaneDistance from

threshold LatitudeLane 1 middle point 2220m 103.99664�Lane 2 starting point 2330m 103.99707�Lane 3 starting point 2530m 103.99775�

Fig. 6. Distance from the runway threshold and their corresponding latitudeat the starting point of each lane. The picture and data are from Google Map.

B. Normal Distribution of the Height of the Ship

To generate the normal distribution of the height of the ship,the maximum height of the approach surface which is assumedto be the mean height of the ship is determined first. Afterward,the standard deviation is calculated by taking into account thevariation in the height of the ship due to wave and tide. Finally,the normal distribution is generated. The detailed computationsare presented in the following sub-sections.

1) Maximum height of the approach surface: The distancesbetween the runway threshold and lane 1, lane 2 and lane 3are measured to calculate the maximum height of the approachsurface which is assumed as the maximum height of the ship.The distance from each individual lane to the runway thresholdcan be seen in Fig. 6.

After obtaining the distance, the maximum height of theapproach surface is computed using the dimension and slopeof the approach surface in Table I. Fig. 7 demonstrates theway how we calculate the maximum height of the approachsurface.

Lane 1 Lane 2 Lane 3

2% of slope = 1.4576 degree

60m 43.2m47.6m

51m

2220 – 60 = 2160m2440 – 60 = 2380m

2610 – 60 = 2550m

O A B C

D

E

F

Fig. 7. Computation method to find the maximum approach height for Codenumber 3/4 performing a landing approach on the precision approach CATI/II runway.

In Fig. 7 we denote DA, EB, and FC respectively as themaximum height of the approach surface with respect to lane1, lane 2 and lane 3. We therefore have DA

AO = EBBO = FC

CO .From Table I we know that the distance from threshold is 60m.Thus, we have AO = 2220−60 = 2160m, BO = 2440−60 =2380m, and CO = 2610 − 60 = 2550m. From Table I wealso know that the slope is 2% for aircraft with aerodromereference code number 3/4 performing a landing approach onthe precision approach CAT I/II runway. As a consequence,

we have DA = AO×2% = 43.2m, EB = BO×2% = 47.6m,and FC = CO× 2% = 51m.

The maximum height of the approach surface is assumedto be the mean height of the ship in Singapore Strait so as toensure the maximum height limit is used in our research. Themean height of the ship for code number 3/4 in lane 2 willbe used to derive the normal distribution. The mean height ofthe ship in lane 1 and lane 3 will be use for comparison inthe next section.

C. Variation in height of ship

Ships moving along the Singapore Strait encounter tides andwaves which affect the maximum height of the ship. Thus, itis important to account for additional height due to waves andtides. The maximum height of tides for the month of February2019 is 3.4m [37]. The maximum height of tides on each dayin February is shown in Fig. 8.

0

0.5

1

1.5

2

2.5

3

3.5

4

Max

imum

hei

ght o

f tid

es (m

)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 February

Height of tides in February

Fig. 8. Maximum height of tides in February, 2019 [37].

The maximum height of wave is estimated to be 0.4m [38].Therefore, a variation of 3.8m for the height of the ship istaken into consideration during the developing of algorithm togenerate the normal distribution of the ship’s height.

D. Computing standard deviation of ship’s height

The mean of the ship is 47.6m and the maximum heightof ship is equal to 47.6 + 3.8 = 51.4m. To find the standarddeviation to generate a normal distribution, the study of normaldistribution is required. Fig. 10(a) shows the empirical rule forthe normal distribution.

Fig. 10(a), indicates that, 99.7% of the data fall within 3standard deviation from the mean. Therefore, to determine thestandard deviation of the ship, the maximum height of thetides and wave can be used to estimate the standard deviation.Firstly, 3.8m is taken to be equal to 3 standard deviation forwhich the standard deviation is equal to 1.2667. An algorithmis developed to check the maximum height of ship based onthe mean of 47.6 and standard deviation of 1.2667 for 100,000sample followed by plotting of the normal distribution of theship. This algorithm requires the use of the external Pythonlibrary NumPy to generate data to form a normal distributionand the library Matplotlib to display the normal distribution.The maximum height of the ship obtained by the standarddeviation is 53m which has a big difference from the originalmaximum height of ship which is 51.4m. Therefore, instead ofusing 3 standard deviation from the mean, 4 standard deviationfrom the mean is used. The standard deviation used is 3.8divided by 4 which equals 0.95. The maximum height of theship obtained by the standard deviation is 51.5m which is closeto the original maximum height of the ship. Therefore, a mean

of value 47.6 and standard deviation of 0.95 are used in thealgorithm to determine the collision risk between a ship andan aircraft. Fig. 10(b) displays the normal distribution of theship’s height with a mean of 47.6m and standard deviation of0.95 plot by using histogram as indicated in blue colour anda best fit line of the histogram as indicated in red colour.

μ+σμ μ+2σ μ+3σμ – σμ – 2σμ – 3σ

2.1% 2.1%

13.6% 13.6%

34.1% 34.1%

68%

95%

99.7≈100%

(a) (b)Fig. 9. Normal distributions. (a) The empirical rule for the normal distribu-tion [39] and (b) the normal distribution of the ship’s height.

E. Computing Normal Distribution of the Altitude of theAircraft

The aircraft landing trajectory data is obtained from theADS-B data purchased from FlightAware. It consists of 50samples of different altitudes that range from 69.76m to774.7m, and for each altitude it has its latitude, and its threeupper and lower standard deviations from the mean. Afterorganizing the original data, the standard deviation for eachrespective altitude can be obtained. From the 50 samples ofdifferent altitudes, interpolation is used to obtain the requiredaltitude and its standard deviation. The data can be found inthe Appendix. The required altitude is based on the latitude ofwhere the lane 2 in the Singapore Strait start. The latitudeof the lane 2 starting point is obtained from Google Mapand interpolation is used to get the corresponding altitude andstandard deviation. Instead of using the latitude of lane 2 inthe middle, the latitude of lane 2 starting point is used dueto better collision risk approximation in the final algorithm.The start of lane 2 defined as the lane in the middle of lane 1and lane 2. Based on Fig. 6, the start of lane 2 is calculatedas 2330m from the runway threshold and the correspondinglatitude can be obtained.

TABLE IIMEAN ALTITUDE AND ITS STANDARD DEVIATION FOR AIRCRAFT ABOVE

LANE 2 STARTING POINT.

Point 1 Point 2 Lane 2 startingpoint

Latitude (◦) 103.99612245 103.99714286 103.997076Altitude (m) 154.7815 169.0436174 168.1091292StdDevAlt 21.74806848 21.6881532 21.69207901

The latitude of the lane 2 starting point is 103.997076◦

and it is used to interpolate the corresponding altitude andits standard deviation based on the aircraft landing trajectorydata. The two points (Point 1 (T7) and Point 2 (T8)) used forinterpolation can be found in the Appendix and the altitudeand its standard deviation of the lane 2 starting point is foundto be 168.11m and 21.69 rounded off to 2 decimal points

respectively. Table II shows the points used for interpolationand the mean and standard deviation of lane 2 starting point.

Fig. 10a displays the normal distribution of the aircraftaltitude with a mean of 168.11m and standard deviation of21.69 plot by using histogram as indicated in orange colourand a best fit line of the histogram as indicated in red colour.

(a) (b)Fig. 10. Normal distributions of (a) aircraft’s altitude and (b) ship’s heightand aircraft’s altitude to determine the collision risk.

F. Determining the Collision Risk in the Approach Segment

Collision occurs when the normal distributions of ship andaircraft intersect each other. This occurs when the maximumvalue of the ship’s height normal distribution is higher than theminimum value of the aircraft’s altitude normal distribution.Fig. 10b displays the normal distribution of the ship’s heightwith a mean of 47.6m and standard deviation of 0.95 togetherwith the aircraft’s altitude with a mean of 168.11m andstandard deviation of 21.69.

By using the data and information of the normal distributionof the ship’s height and aircraft’s altitude, the collision riskcan be determined. When the differences between the aircraftminimum height of the aircraft landing trajectory normaldistribution and the ship maximum height of the ship’s heightnormal distribution is less than 0, it indicates that collisionoccurs. To find the collision probability, a function is createdto loop the process of determining collision for 100,000 times.The collision risk is then quantified as the number of timescollision occurs divided by 100,000.

G. Determine the Collision Risk as the Runway ThresholdReduce

When the runway threshold is shifted forward, the altitudeof the aircraft will be reduced accordingly as the landing slopeof the aircraft remains the same. Therefore, the further therunway threshold is shifted forward, the lower the altitude ofthe aircraft at the same latitude. The landing slope is firstinitialized to be tan−1 AltL

DistLwhich is calculated to be 4.13◦

for lane 2 where AltL is defined as the altitude of aircraftat a specific lane and DistL is defined as the distance fromthe runway threshold to a specific lane. Using the distancebetween the runway threshold and the starting point of lane2 which is 2330m as obtained from Fig. 6 and altitude ofaircraft at lane 2 which is 168.11m as obtained from TableII, the landing slope is computed as follow: Landingslope =tan−1 168.11

2330 = 4.13◦. Assuming that the landing slope remainthe same when the runway threshold is shifted forward, in thiscase we shifted the runway threshold by 60m, the altitude of

the aircraft is computed as follow: Altitudeofaircraft =(2330− 60)× tan(4.13◦). This is illustrated in Fig. 11.

60m

2330m

168.11m163.78m

Same Landing slope

Fig. 11. Reduction in the altitude of the aircraft based on the length of runwaythreshold shift.

Based on the idea to determine the collision risk betweenthe ship and aircraft in the approach segment as mentionedpreviously and accounting for the collision risk as the runwaythreshold reduce, Algorithm 1 was developed to determine thecollision risk between the aircraft and the ship in relation tothe length of runway threshold shifted.

Algorithm 1 Determine the collision risk between the aircraftand the ship in relation to the shifted runway threshold.

1) Define the landing slope of the aircraft LS =tan−1 AltL

DistL, where AltL is the altitude of aircraft at a

specific lane and DistL is the distance from the runwaythreshold to a specific lane;

2) Set Threshold_shift = [ ], Alt = [ ], and CR = [ ];3) Set Threshold_shift = 0 : 10 : 300;4) ∀d ∈ Threshold_shift, Alt = [Alt, (DistL − d) ∗

tan(LS)];5) For t in Alt, do

a) Initialize x = 0 and y = 0;b) While x < 100000, do

i) ship_height = NormDistr(µs, σs, 10000);ii) ship_highest = max(ship_height);

iii) aircraft_alt = NormDistr(µa, σa, 10000);iv) aircraft_lowest = min(aircraft_altitude);v) Diff = aircraft_lowest− ship_highest;

vi) If Diff ≤ 0, then y = y + 1;vii) x = x+ 1;

c) End Whiled) CR = [CR, y/100000].

6) End

Algorithm 1 will take account up to 300m of the runwaythreshold shift with an interval of 10m. The correspondingaltitude of the aircraft for each of the interval is calculatedusing the fixed initialized landing slope of 4.13◦ and the lengthof threshold shifted. The collision risk at every interval of 10mof runway threshold shift is determined.

V. EXPERIMENTAL RESULTS

A. Collision Risk for Shipping Lane 2

After applying the collision risk calculation method to Lane2 of the Singapore Strait, the results are reported in the formof a scatter plot. Fig. 12 displays scatter plot in which thevariations of the collision risk and the altitude of the aircraft

above Lane 2 are shown with respect to the variation of theshifted runway threshold.

0 50 100 150 200 250 300Threshold shift (m)

−0.01

0.00

0.01

0.02

0.03

0.04

0.05

0.06

Collis

ion Risk

0 50 100 150 200 250 300Threshold shift (m)

140

145

150

155

160

165

170

Altitud

e (m

)Fig. 12. Scatter plot of the collision risk vs length of runway threshold shiftedon the left and the altitude vs length of runway threshold shifted on the rightfor Lane 2.

From Fig. 12, the collision risk varies from 0 to 0.055with respect to the threshold shift and the altitude of theaircraft decreases linearly from 168.11m to 146.46m when thelength of threshold shift increases from 0m to 300m. It can beobserved that the collision risk increases exponentially whenthe length of threshold shifted is more than 150m.

B. Collision Risk for Shipping Lanes 1 and 3

In the above subsection, the collision risk between the shipand aircraft is determined for the shipping lane 2 in theSingapore Strait. In order to ensure that the collision riskobtained for shipping lane 2 is accurate, the collision risk forthe other two shipping lanes in Singapore Strait are required tobe determined. Using Algorithm 1, the collision risk for lanes 1and 3 can be determined but the variables used for computationare different. The latitude of the aircraft is based on theshipping lane and therefore the altitude of the aircraft willchange accordingly based on the fixed initialized landing slopeon each lane which is also different. The mean and standarddeviation of the ship’s height and aircraft’s altitude will changeaccordingly based on the shipping lane. The latitude of lane 1is taken at the middle due to uncertainty of the start of lane 1,while the latitude of lane 3 is taken from the starting point oflane 3. The latitudes of the lanes are obtained from the googlemaps as shown in Fig. 6.

TABLE IIIMEAN ALTITUDES AND THEIR STANDARD DEVIATIONS FOR AIRCRAFT

ABOVE LANE 1 MIDDLE POINT AND LANE 3 STARTING POINT

Lane 1

Point Point 1 Point 2 Lane 1 middlepoint


Lane 3

Point Point 1 Point 2 Lane 3 startingpoint


The latitudes of the lane 1 middle point and lane 3 startingpoint are 103.996640◦ and 103.997751◦ respectively. Follow-ing which, they are used to interpolate the correspondingaltitude and its standard deviation based on the aircraft landing

trajectory data. The two set of points (Point 1 (T7) and Point 2(T8), Point 1 (T8) and Point 2 (T9)) used for interpolation arereported in the Appendix for lane 1 and 3 respectively, and thealtitudes and standard deviations of lane 1 middle point andlane 3 starting points are interpolated to be 162.02m, 21.72 and177.56m, 21.66, rounded off to 2 decimal point respectively.Tables III shows the points that is used for interpolation andthe acquired mean and standard deviation for lane 1 middlepoint and lane 3 starting point.

After obtaining the altitude of aircraft at lane 1 and 3, thelanding slope can be computed. The landing slopes of lane 1, 2and 3 are calculated to be 4.17◦, 4.12◦ and 4.02◦ respectively.Referring to Fig. 7, the maximum allowable heights of theship for lane 1 and lane 3 are 43.2m and 51m, respectively.Following the previous assumption, the maximum allowableheight is assumed to be the mean of the ship for generatingthe normal distribution. The standard deviations of the ship’sheight normal distribution of lane 1 and lane 3 are the sameas that of lane 2 which is 0.95 as the standard deviation isobtained from the height variation due to tides and waveswhich are the same for all the lanes in Singapore Strait. Afterobtaining the required data, the collision risk can now bedetermined using Algorithm 1.

After applying the collision risk calculation method to lanes1 and 3 of the Singapore Strait, the results of the collisionrisk are obtained in form of scatter plot. Fig. 13 displays thescatter plots in which the variations of the collision risk andthe altitude of the aircraft above lanes 1 and 3 are shown withrespect to the variation of the shifted runway threshold.

(a) Lane 1

(b) Lane 3Fig. 13. Scatter plots of the collision risk vs length of runway thresholdshifted on the left and the altitude vs length of runway threshold shifted onthe right for lanes 1 and 3.

As can be seen from Fig. 13(a), the collision risk varies from0 to 0.08 with respect to the threshold shift. The altitude of theaircraft decreases linearly from 162.02m to 140.13m when thelength of threshold shift increases from 0m to 300m. It can beobserved that the collision risk increases exponentially if thelength of threshold shifted is more than 150m. The similar

phenomena also can be discovered from what are shown inFig. 13(b). From Fig. 13(b) we see that the collision risk doesnot increase exponentially when the length of threshold shiftedis more than 150m. This is because that lane 3 is relativelyfar away from the runway and its impacts on the collision riskare therefore relatively smaller than that of lanes 1 and 2.

VI. CONCLUSION

The constructions of airports may suffer from obstructionconstraints. When an aerodrome is surrounded by obstructions,a displaced threshold is assigned to the runway to maintain thesafety standard for departures and arrivals of the flights. Notethat the displaced thresholds for the runways of some airportswere determined in a very conservative way. Such conservativethresholds decrease the LDA of the runways impacting therunway capacity.

This paper provided a new perspective for dynamic runwaythreshold management by reducing the runway threshold asand when required. To do so, a data-driven collision riskmethod, that makes full use of the flight approach path andobstacle surface profile, was suggested. The developed modelwas applied to Singapore Changi Airport, which suffers fromlong displaced runway threshold due to the ship movementsin Singapore Strait Channel. It was suggested that a maximumlength of runway threshold that can be reduced is 100m.

Although the case study is only carried out for SingaporeChangi Airport, the generalization of the framework of the pro-posed method to other airports that are subject to obstructionconstraints is apparent. For future planning of airports, thatare to be built on reclaimed land or nearby the straits, wherethere are possible ship movement obstructing the surroundingairspace, this model can be used to investigate the possibilitiesof various displaced threshold distance for a given runway.

ACKNOWLEDGMENT

This work is supported by NTU-CAAS Research GrantM4062429.052 by Air Traffic Management Research Institute,School of MAE, Nanyang Technological University, Singa-pore.

REFERENCES

[1] M. C. Gelhausen, P. Berster, and D. Wilken, “Do airport capacityconstraints have a serious impact on the future development of airtraffic?” Journal of Air Transport Management, vol. 28, pp. 3–13, 2013.

[2] S. Wilke, A. Majumdar, and W. Y. Ochieng, “Airport surface operations:A holistic framework for operations modeling and risk management,”Safety Science, vol. 63, pp. 18–33, 2014.

[3] IATA, “IATA forecast predicts 8.2 billion air travelers in 2037,https://www.iata.org/pressroom/pr/pages/2018-10-24-02.aspx,” October2018.

[4] C. Barnhart, D. Fearing, A. Odoni, and V. Vaze, “Demand and capacitymanagement in air transportation,” EURO Journal on Transportationand Logistics, vol. 1, no. 1, pp. 135–155, 2012.

[5] K. G. Zografos, M. A. Madas, and K. N. Androutsopoulos, “Increasingairport capacity utilisation through optimum slot scheduling: review ofcurrent developments and identification of future needs,” Journal ofScheduling, vol. 20, no. 1, pp. 3–24, 2017.

[6] B. Vaaben and J. Larsen, “Mitigation of airspace congestion impact onairline networks,” Journal of Air Transport Management, vol. 47, pp.54–65, 2015.

[7] A. Lieder and R. Stolletz, “Scheduling aircraft take-offs and landingson interdependent and heterogeneous runways,” Transportation ResearchPart E: Logistics and Transportation Review, vol. 88, pp. 167–188, 2016.

[8] J. A. Bennell, M. Mesgarpour, and C. N. Potts, “Airport runwayscheduling,” 4OR, vol. 9, no. 2, p. 115, May 2011.

[9] A. Kanafani, “Operational procedures to increase runway capacity,”Journal of Transportation Engineering, vol. 109, no. 3, pp. 414–424,1983.

[10] M. Janic, “Modeling effects of different air traffic control operationalprocedures, separation rules, and service disciplines on runway landingcapacity,” Journal of Advanced Transportation, vol. 48, no. 6, pp. 556–574, 2014.

[11] D. M. Lohr, Gary W.; Williams, “Current practices in runway con-figuration management (RCM) and arrival/departure runway balancing(ADRB),” NASA Langley Research Center, Hampton, VA, UnitedStates, Tech. Rep. NASA/TM-2008-215557, L-19505, LF99-7407, De-cember 2008.

[12] M. D. Christopher Weld and R. Kincaid, “A runway configurationmanagement model with marginally decreasing transition capacities,”Advances in Operations Research, vol. 2010, no. 1, pp. 1–22, 2010.

[13] D. Bertsimas, M. Frankovich, and A. Odoni, “Optimal selection ofairport runway configurations,” Operations Research, vol. 59, no. 6, pp.1407–1419, 2011.

[14] T. Kolos-Lakatos, “The influence of runway occupancy time and wakevortex separation requirements on runway throughput,” Ph.D. disserta-tion, Massachusetts Institute of Technology, 2013.

[15] G. Lohr, S. Brown, H. Stough, S. Atkins, S. Eisenhawer, and D. Long,“System oriented runway management: A research update,” in NinthUSA/EUROPE Air Traffic Management Research and DevelopmentSeminar, Berlin; Germany, June 2011.

[16] F. Farhadi, A. Ghoniem, and M. Al-Salem, “Runway capacitymanagement–an empirical study with application to doha internationalairport,” Transportation Research Part E: Logistics and TransportationReview, vol. 68, pp. 53–63, 2014.

[17] S. J. Heblij and R. A. A. Wijnen, “Development of a runway alloca-tion optimisation model for airport strategic planning,” TransportationPlanning and Technology, vol. 31, no. 2, pp. 201–214, 2008.

[18] S. S. A. Aiyar, “The case for offshore airports,https://www.cato.org/publications/commentary/case-offshore-airports,”January 2003.

[19] H.-K. Yan, N. Wang, L. Wei, and Q. Fu, “Comparing aircraft noisepollution and cost-risk effects of inland and offshore airports: The caseof dalian international airport, dalian, china,” Transportation ResearchPart D: Transport and Environment, vol. 24, pp. 37–43, 2013.

[20] I. Kirkland, R. Caves, I. Humphreys, and D. Pitfield, “An improvedmethodology for assessing risk in aircraft operations at airports, appliedto runway overruns,” Safety Science, vol. 42, no. 10, pp. 891–905, 2004.

[21] ICAO, “Volume 1 Aerodrome Design and Operation, 4th Edition,” 2004.[22] Department of Statistic, “Civil aircraft arrivals, de-

partures, passengers and mail, changi airport, annual,https://www.tablebuilder.singstat.gov.sg/publicfacing/displaychart.action,”(Accessed Nov 06, 2019).

[23] Singapore Infopedia, “Changi Airport,http://eresources.nlb.gov.sg/infopedia/articles/sip_574_2004-12-23.html,” (Accessed Nov 06, 2019).

[24] CAAS, “AIP Singapore, https://www.caas.gov.sg/docs/default-source/pdf/aip-10oct19.pdf,” (Accessed Nov 06, 2019).

[25] FAA, “Section 3. Airport Marking Aids and Signs,https://www.faa.gov/air_traffic/publications/atpubs/aim_html/chap2_section_3.html,” (Accessed Nov 06, 2019).

[26] Meteorological Service Singapore, “Astronomical data and tides,http://www.weather.gov.sg/weather-astronomical-and-tidal-information-monthly-data,” (Accessed Nov 06, 2019).

[27] “Windfinder, Wind, waves & weather forecast (Singapore ChangiAirport), https://www.windfinder.com/forecast/singapore_changi,” (Ac-cessed Nov 06, 2019).

[28] D. Kernler, “A visual representation of the Empirical(68-95-99.7) Rule based on the normal distribution,https://commons.wikimedia.org/wiki/File:Empirical_Rule.PNG#/media/File:Empirical_rule_histogram.svg,” 2014.

a collision risk assessment method for runway threshold

Documents