LONG-TERM DEMAND FORECASTING OF MANAGED LANES Christopher Mwalwanda 13 th TRB Transportation...
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Transcript of LONG-TERM DEMAND FORECASTING OF MANAGED LANES Christopher Mwalwanda 13 th TRB Transportation...
LONG-TERM DEMAND FORECASTING OF MANAGED LANES
Christopher Mwalwanda
13th TRB Transportation Planning Applications ConferenceMay 10, 2011
Challenges in Addressing Key Influential Risk Parameters
• More Complex than Traditional Forecasting– Competition Conditions are immediately apparent
• More Data for Operational Assessments– Public Behavioral Characteristics– Geometrical Consideration/Travel Speed
Deterioration Analysis– Time of Day Profiling
• Eligibility and Pricing Options– Operational Demand Management versus
Revenue Generation
MANAGED LANE FORECASTING 101
SR 167, Seattle, WA•2008
I-680, Alameda, CA•2010
SR 91, Orange, CA•1995
I-15, San Diego, CA•1998
Houston, TX•US 290 QuickRide 1998 •I-10 Katy Freeway Managed Lanes, 2009
I-95, Miami, FL•2008
Minneapolis, MN•I-394 , 2005•I-35W, 2009
I-15, Salt Lake, UT•2006
I-25, Denver, CO•2006
OPERATING MANAGED LANE PROJECTS
•I-580•SR 237•SR 85 & US 101
•IH-635 /LBJ•NTE
I-95 Section 100
Route 495 Lincoln Tunnel
Atlanta (Various)
I-405
US 36
US 290
Existing Managed Lanes Projects
Planned or Under ConstructionStudied
I-595
I-25 North
RECENT HOT/MANAGED LANE PROJECTS
MoPacLoop 1
• New and Innovative Demand Management Techniques– Dynamic Speed Limits/Dynamic Re-striping– Shoulder Lane Utilization– GPS/Dynamic Re-routing Procedures
• How does one develop a forecast?– Point forecasts for financial feasibility– Ranges for procurement assessment
FORECASTING CHALLENGES
MANAGED LANE POLICIES
• HOV’s• HOT’s• ETL’s• TOT’s
Facility Type
PricingTypeFacility Location Comments
Fixed Variable Rates
SR 91 Orange County, CA ETL's Preset Varies by day of week and hour of day
I-25 HOT Lanes Denver, CO ETL's (HOT) Preset HOV's free – reversible/Free Flow for Buses
I-95 Express Lanes Miami, FL ETL's (HOT)
Dynamic Pricing
I-15 Managed Lanes San Diego, CA ETL's (HOT) Dynamic Must keep free flow for HOV
I-394 MNPASS Minneapolis, MN ETL's (HOT) Dynamic Must keep free flow for HOV
SR 167 Seattle, WA ETL's (HOT) Dynamic Must keep free flow for HOV
IH 10 Toll Lanes Houston, TX
I-15 Managed Lanes Salt Lake City, UT ETL's (HOT) Dynamic Must keep free flow for HOVDynamic* Registered HOV
ETL's (HOT) Preset HOV's free during peak periods
VARIABLE PRICING EXAMPLES
Project Name Length
Lanes Daily VolumeAnnual Revenue (million)
Tolling PolicyML GP ML (000) GP (000)
SR 167_WA 9 2 4 2 - 2.3 112 - 115 $0.4 - $0.5 HOV2+ free
I-394_MN* 11 1/2 4 4 - 4.5 150 – 160 $1.4 - $1.6 HOV2+ free
I-25_CO* 7 2 8 4 – 5 220 – 230 $2.0 - $2.5 HOV2+ free
IH 10_TX 12 4 10 25 - 27 220 - 225 $6.0 - $7.0 HOV2+ free peak period
I-95_FL 6 2 8 50 – 55 210 - 250 $13 - $14.0 HOV3+ free, Registered
SR 91_CA 10 4 8 35 - 40 215 - 220 $35 -$40HOV3+ discount in PM, free all other times
* Reversible facilities
EXISTING ML OVERVIEW
Mod
erat
ely
Con
gest
ed
Pea
k P
erio
d C
onge
sted
Hyp
er-C
onge
sted
# of Years
RE
VE
NU
E
• Market Capture– Attracting User
Markets– Peak Period HOV
Discounting– HOV 2+ or 3+ Market
Segmentation– Already Relatively
Mature Corridors
• Maturation of Targeted Demand
― Captures Sufficient Targeted Daily Demand
• Management of Demand
― High Toll Rates
― Discourage excessive usage
EVOLUTION OF MANAGED LANES
• Hockey Stick Revenue Achievable?? It Depends and requires:– Detailed Assessment of the all key variables– Focus on Future Operational Performances (GP & ML)
• Key Risk Associated with Forecasts– Competing Facilities– Escalation of Toll Rates– Maximum Demand Capture Rates– Off-peak/Directionality Considerations– Local Corridor Characteristics– Future Geometrical and Network Connectivity
EVOLUTION IMPLICATIONS
Annual Revenue growth has been very strong: 9.6% AAGR (1998 - 2004) [Inflation ~ 2.9%]
16.9% AAGR (2004 - 2007) [Inflation ~ 4.0%] Recession effect: -4.8% AAGR (2007 - 2010)
Overall nominal growth:7.5% AAGR (1998 - 2010)[Inflation ~ 2.8%]
Real Growth ~ 4.7% AAGR
REVENUE GROWTH IMPLICAITON?
$0
$5,000
$10,000
$15,000
$20,000
$25,000
$30,000
$35,000
$40,000
$45,000
$50,000
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Ann
ual R
evne
ues
(000
's)
SR 91 - CA
0
5,000
10,000
15,000
20,000
25,000
30,000
SR 167_WA I-394 MN (Reversible)
I-25_CO (Reversible)
IH-10_TX I-95_FL SR 91_CA
Ge
ne
ral P
urp
os
e D
aily
AA
AD
T
pe
r la
ne
2010 Estimates from Available Data
GP Daily AADT/Lane
$0
$20,000
$40,000
$60,000
$80,000
$100,000
$120,000
$140,000
$160,000
SR 167_WA I-394 MN (Reversible)
I-25_CO (Reversible)
IH-10_TX I-95_FL SR 91_CA
Mo
nth
ly R
ev
ne
ue
s
2010 Estimates from Available Data
Monthly Revenue/Mile/Direction
$0.00
$0.50
$1.00
$1.50
$2.00
$2.50
$3.00
$3.50
SR 167_WA I-394 MN (Reversible)
I-25_CO (Reversible)
IH-10_TX I-95_FL SR 91_CA
Re
ve
nu
e p
er
To
lled
Ve
hic
le
2010 Estimates from Available Data
Average Revenue/Vehicle
REVENUE – POLICY IMPLICATIONS
• Corridor Demand (Peaking/ Directionality) • Market/ OD pattern (Diversification)• Weekend Traffic Profile
• Traffic Conditions/Operations • GP Lane Congestion, Queuing/Metering, Time Saving
• Traveler’s Characteristics • Willingness-to-pay, Value of Reliability, Safety
• Toll Rate Pricing Structures, ML Access etc.
MANAGED LANE TRAFFIC – KEY FACTORS
• Economic Growth– Long-term Cyclical Trends/ Diversification of Growth
• Traffic Growth Profiles– Seasonality/Weekly/Daily/Hourly Distributions
• Values of Time– Income Growth and Distributions
A good forecaster is not smarter than everyone else, they merely have their ignorance better organized Anonymous
LONG-TERM CONSIDERATIONS
• Mode Trends/Market Shifts– HOV/Commercial Vehicle Market Trends – Aging Population/Migration Patterns
• Inflationary Trends– Toll Rate Escalation and Disposable Income
• Additional Influential Factors– Incident Rates/ Fuel Prices– Geometric/Operational Impedances on Speeds
LONG-TERM CONSIDERATIONS
• Risk Ranges (Tend to be Situational)– Location Dependent (Mature vs Undeveloped/Corridor
vs Regional)– Economic Diversity – Dependency on Single Markets/Industries
• There are many ways to get to the same place– Concave versus Convex Growth
ECONOMIC GROWTH
The past does not repeat itself, but it rhymes. Mark Twain
Brazoria Co.
Galveston Co.
Harris Co.
Fort Bend Co.
“Forecasters tend to use historical data for support rather than illumination” Montgomery County
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
1960 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
Census
1972 Projection
1986 Projection
1992 Projection
2005 Projection
Harris County
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
1960 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
Census
1972 Projection
1986 Projection
1992 Projection
2005 Projection
Fort Bend County
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
1960 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
Census
1972 Projection
1986 Projection
1992 Projection
2005 Projection
ECONOMIC GROWTH
• Key Factors:– Motorist value of time (varied and situational)– Anticipated time savings “Error of anticipation”
• Equilibrium Sensitivity to Market Capture Rates– Elasticity is 4.0 (not 0.4) i.e. A small 10% change
in Traffic can result in 40% change in Revenues– Major Revenue Declines with higher gas prices
• Short-term or Long-term?
DETERMINING OPTIMUM TOLLS RATES
• Does it Necessarily Fall in Line with CPI?– Traditional Toll Facilities have not kept up with inflationary trends – What about managed lanes?
TOLL RATE ESCALATION
-2.0%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12
:00
AM
1:0
0 A
M
2:0
0 A
M
3:0
0 A
M
4:0
0 A
M
5:0
0 A
M
6:0
0 A
M
7:0
0 A
M
8:0
0 A
M
9:0
0 A
M
10
:00
AM
11
:00
AM
12
:00
PM
1:0
0 P
M
2:0
0 P
M
3:0
0 P
M
4:0
0 P
M
5:0
0 P
M
6:0
0 P
M
7:0
0 P
M
8:0
0 P
M
9:0
0 P
M
10
:00
PM
11
:00
PM
Ave
rag
e A
nn
ual
Gro
wth
(20
01-2
010)
SR 91 Toll Rate Trends
Westbound Eastbound Regional LA CPI
• Revenue Days/ Annualization Factors– Difference between 275 and 365 can yield significant
revenue changes
• Ramp-up Assumptions– Brownfield versus Greenfield– Duration of Ramp-up (typically short for MLs)
• Peak Spreading Characteristics– Composition of Demand (Work versus Non Work)– Radial versus Circumferential– Corridor Volume Capacity
MAJOR REVENUE DETERMINANTS
• Are the Capture Rates Expected to be similar in both directions?– Diversion to managed lanes is very situational…
SR 91 Sample Profiling Example
$0.00
$0.10
$0.20
$0.30
$0.40
$0.50
$0.60
$0.70
$0.80
$0.90
$1.00
12
:00
AM
1:0
0 A
M
2:0
0 A
M
3:0
0 A
M
4:0
0 A
M
5:0
0 A
M
6:0
0 A
M
7:0
0 A
M
8:0
0 A
M
9:0
0 A
M
10
:00
AM
11
:00
AM
12
:00
PM
1:0
0 P
M
2:0
0 P
M
3:0
0 P
M
4:0
0 P
M
5:0
0 P
M
6:0
0 P
M
7:0
0 P
M
8:0
0 P
M
9:0
0 P
M
10
:00
PM
11
:00
PM
To
ll R
ate
Pe
r M
ile
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
Vo
lum
e/C
ap
ac
ity
Ra
tio
EB Toll Rate per Mile WB Toll Rate per Mile EB V/C WB V/C
MARKET CAPTURE RATES
Note: Market Share reflects toll paying patronage only
MANAGED LANE MARKET SHARES
3%
10%12%
16%
29%
18%
2%
6%4%
10%
20%
14%
0%
5%
10%
15%
20%
25%
30%
35%
40%
SR 167_WA I-394 MN (Reversible)
I-25_CO (Reversible)
IH-10_TX I-95_FL SR 91_CA
Bi-
Dir
ec
tio
na
l M
LM
ark
et
Sh
are
(T
OT
AL
)(T
oll-
pa
yin
gM
ark
et)
2010 Estimates from Available Data
Peak (6-10 & 3-7) Weekday
• Long-term Commercial Vehicle Trends– Global/Local Effects of Trade Policies– Just-in-Time Delivery– Supply Chain Strategies– Evolution in Truck Sizes– Vehicle Operating Costs
• Aviation and Intercity Rail Trends– Competing versus Complementary Modes– New Transportation Policies (fuel efficiency etc.)
MODAL UTILIZATION CONSIDERATIONS
• Defining Risk– Where is the Risk – How to Quantify– How Significant is the Risk– Discrete versus Ranges
• Dependent on Data Availability– Historical Profiling– Accuracy/Variability of Forecast Sources– Data Filtering– New Modeling Approaches– Value of Reliability
• Incorporate all the Key variables to create realistic ranges– Correlation Dependency– Unknown/Unforeseen Variability– Prioritization of Key Factors
RISK PROFILING
2015 2020 2025 2030 2035 2040 2045 2050
Year
Base Case EstimateEarly OccurrenceLate Occurrence
Moderately Congested
MANAGED LANE RISK PROPAGATION
To expect the unexpected shows a thoroughly modern intellect. Oscar Wilde
# of years
RE
VE
NU
E
f( Key Subset Variables)
BASELINE
f( Key Subset Variables)
f( Full Universe of Variables)
f( Full Universe of Variables)
UNCERTAINTY RANGES
RECENT MANAGED LANE FINANCINGS
Managed Lane Project
FinancingMethod
Miles (Ultimate)
Project Costs
Public Grant
/Subsidy TIFIAFinancial
Close
Capital Beltway (Washington D.C.) PPP/DB 14.0 $1.9B $409M* $589M
Dec2007
I-595 Express Lanes (Miami)
Availability/DBFOM 10.5 $1.8B $232M** $603M
Marc h2009
North Tarrant Express (Fort Worth) PPP/DBFOM 13.0 $2.0B $573M $650M
June2009
IH 635 LBJ (Dallas) PPP/DBFOM 13.0 $2.7B $489M $850M June 2010
* Commonwealth of Virginia grant ** FDOT qualifying development funds
$0.6$1.0
$0.1 $0.3
$1.2$1.8
$1.3$1.6
$2.1$2.6
$2.1 $2.1
$4.0
$7.2
$3.4
$4.5$3.9
$5.1
$2.9$3.3
$0.0
$1.0
$2.0
$3.0
$4.0
$5.0
$6.0
$7.0
$8.0
S.R. 91 (1999)
S.R. 91 (2010)
Existing Existing Lender Sponsor Lender Sponsor Lender Sponsor Lender Sponsor
S.R. 91 IH 10 I-95 Miami
LBJ NTE Segment 1 NTE Segment 2 Capital Beltway
Ann
ual R
even
ue P
er M
ile p
er La
ne (M
illio
ns R
eal $
201
0)
2010 2020 2050
GP
Dai
lyVe
hs/L
ane
22,4
00
29,0
00
23,0
00
24,3
00
31,1
06
10,4
83
13,4
20
26,0
00
21,6
52
27,7
17
26,2
50
33,6
02
MANAGED LANE REVENUE RISK
4.7%
4.8%
3.6%
1.5%*
2.3%
*Escalated from 2040 results
• Quantification may unintentionally create an aura of precision and confidence – Clear Understanding of the Assumptions is a MUST.
• Context of how will the ranges be utilized– Project Feasibility – Bonding/Capital Improvement Plans– Identification of Subsidy Requirements
• How to narrow the likely ranges?– Detailed data on current ranges– Assessment of Key Variables – Explore Alternative/New Influential Variables
INTERPRETATION AND CONCLUSIONS