1 real-time inter-modal substitution (rtims) as an airport congestion management strategy mark...
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1
REAL-TIME INTER-MODAL SUBSTITUTION (RTIMS) AS AN AIRPORT
CONGESTION MANAGEMENT STRATEGY
Mark HansenYu Zhang
University of California Berkeley
2
Outline
• Diagnosis– Concentration of Delays– Capacity Profiles
• Cure– CDM– Inter-modal substitution– Ground-transport-enabled diversion– Public policy role
3
Concentration of Delays
• Spatially
• Causally
• Temporally
• Passenger Impact
4
OPSNET Delay at SFO OAK SJC
0
20000
40000
60000
80000
100000
120000
140000
160000
Nov-01 May-02 Dec-02 J un-03 J an-04 Aug-04 Feb-05 Sep-05 Mar-06 Oct-06
Fl i ght Mi nutes
OAKSFOSJ C
0
1
2
3
4
5
6
7
8
Nov-01 May-02 Dec-02 Jun-03 Jan-04 Aug-04 Feb-05 Sep-05 Mar-06 Oct-06
Percentage ofOperations Delayed
OAK
SFO
SJC
5
Delays at SFO
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Jul-98 Dec-99 Apr-01 Sep-02 Jan-04 May-05 Oct-06 Feb-08
Month
% D
ela
ys
Du
e t
o W
ea
the
r
0%
2%
4%
6%
8%
10%
12%
% O
ps
De
lay
ed
Pct Due to Weather
Pct Ops Delayed
6
Concentration of Delays in Time, 2007 to Date
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Pct of Days
Pct
of
Del
ays
>80% of delays on worst 20% of days
7
Delay Projections
0
20
40
60
80
100
120
140
160
180
Jan-
01
Jan-
15
Jan-
29
Feb-1
2
Feb-2
6
Mar
-11
Mar
-25
Apr-0
8
Apr-2
2
May
-06
May
-20
Jun-
03
Jun-
17
Jul-0
1
Jul-1
5
Jul-2
9
Aug-1
2
Aug-2
6
Sep-0
9
Sep-2
3
Oct
-07
Oct
-21
Nov-0
4
Nov-1
8
Avg
. To
tal D
elay
(m
in)
2004 Actual
2014 Projection
2025 Projection
Delays projected using JPDO feasible schedulesAssumes weather in 2014 and 2025 the same as 2004
8
Statistics—Yearly Delayed Passenger
No. Airport Delayed Passenger
1 LaGuardia 61,120
2 Chicago O’Hare 57,545
3 Newark 37,132
4 Atlanta 28,229
5 San Francisco 24,478
6 Boston 24,120
7 Philadelphia 21,521
8 Dallas Fort-Worth 20,638
9 Los Angeles 17,141
10 Phoenix 14,024
From Evans and Clarke 2002 pp82
9
Introduction — Delay in NAS
• Disrupted passengers are only three percent of the total passengers, they suffered 39 percent of the total passenger delay.
From Barnhart et al. 2004
Passenger Average Delay % Passengers% Total
passenger Delay
Disrupted 303 minutes 3.20% 39%
Non-disrupted
16 minutes 96.80% 61%
10
Historical Capacity Scenarios at SFO
0
2
4
6
8
10
12
14
16
Time of Day
Quarter-Hourly AAR
61681635925
11
Historical Capacity Scenarios at OAK
0
2
4
6
8
10
12
14
AAR7
1
AAR7
3
AAR8
1
AAR8
3
AAR9
1
AAR9
3
AAR1
01
AAR1
03
AAR1
11
AAR1
13
AAR1
21
AAR1
23
AAR1
31
AAR1
33
AAR1
41
AAR1
43
AAR1
51
AAR1
53
AAR1
61
AAR1
63
AAR1
71
AAR1
73
AAR1
81
AAR1
83
AAR1
91
AAR1
93
AAR2
01
AAR2
03
AAR2
11
AAR2
13
AAR2
21
AAR2
23
AAR2
31
AAR2
33
Ti me of Day
Quarter-Hourl y AAR
59410179732
12
Clustering of Joint Clusters at SFO and OAK
0
5
10
15
20
25
30
AAR7
1
AAR7
3
AAR8
1
AAR8
3
AAR9
1
AAR9
3
AAR1
01
AAR1
03
AAR1
11
AAR1
13
AAR1
21
AAR1
23
AAR1
31
AAR1
33
AAR1
41
AAR1
43
AAR1
51
AAR1
53
AAR1
61
AAR1
63
AAR1
71
AAR1
73
AAR1
81
AAR1
83
AAR1
91
AAR1
93
AAR2
01
AAR2
03
AAR2
11
AAR2
13
AAR2
21
AAR2
23
AAR2
31
AAR2
33
Ti me of Day
Quarter- Hourl y AAR
45. 70%1. 40%31. 30%21. 60%
13
Air Traffic Flow Management (ATM)
• Ground Delay Programs (GDP)– Ration-by-Schedule (RBS) – Compression
• Collaborative Decision Making (CDM)– Flight Schedule Monitor (FSM) – Ration-by-Schedule (RBS)– Compression– Slot Credit Substitution (SCS)
14
Procedure of Collaborative Decision Making (CDM)
ATCSCC, Facilities and AOCs Evaluate Demand vs. Ca
pacity
Proposed GDP Advisory
Is the GDP still required?
End
AOC Response(Cancellations)
No
YesIssue GDP
(RBS)
AOC Response(Re-ordering and
Cancellations)Compression
Exit loop whenprogram expiresor is cancelled
AOC SmartCancellation Problem
Slot Credit Substitution
Based on Metron 2004
15
Ideas
Substitute short-haul flights with surface transport when capacity temporarily drops at hub airports
Divert flights to alternate airports and provide surface transport between alternate and original airports.
16
Hub-and-Spoke Network
Bottleneck: hub airport with capacity constraints
17
Inter-modal Substitution of Short-haul Flights
Fast link
Slow link
bt
wta
bt Transport time on slow link
wta Transport time on fast link and delay due to node capacity
18
Inter-model Substitution plus Diversion
Fast link
Slow link
bt
wta
bt Transport time on slow link
wta Transport time on fast link and delay due to node capacity
19
Why Focus on Short-haul Flights?
• Provide airlines more flexibility to make smart tactical cancellations– More capacity for large-jet operations – allow cancellation decisions to be made with more
reliable capacity information.
• Reduce miss connections and passenger delay– Canceling short-haul flights inconveniences fewer
customers– Passengers save time if the extra transportation
time with surface modes is less than the flight delay they would encounter.
20
Numerical Example ― Input
Airport: San Francisco International Airport (SFO)
Schedule: a typical Thursday in 2004
Source: the Official Airline Guide (OAG)
Airline: a hypothetical feeder airline (Airline S) serving all segments less than 5
00 miles. Detail:
130 flights from 6:00am to 12:00am (18 hours). Capacity:
severe capacity shortfall starting from 6:00am and ending at 10:00am.
two arrivals per hour in the shortfall period and eight arrivals per hour afterwards.
21
Scheduled and Hypothetical Cumulative Arrivals without Cancellation
0
20
40
60
80
100
120
140
6 8 10 12 14 16 18 20 22 24 26
Time of day
Cumulative numberof arrivals
Scheduled ArrivalHypothetical Arrival
22
Numerical Example ― Programming and Solution
w/ocancel
Cancelw/ IMS
Total cost ($) 1720888 731003
Cancellation 25
Substitution 22
See more results
23
Combined Concept
Inter-modal substitution
Diversion
Operate as usual
24
Public Policy Role
• Individual airlines unlikely to implement these ideas on their own
• Policy interventions– Push airlines to reduce operations in periods or
reduced capacity (pricing)– Assist airlines in developing systems for
surface transport to enable real-time substitution and diversion
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