LONG-TERM DEMAND FORECASTING OF
MANAGED LANES
Challenges in
Addressing Key
Influential Risk
Parameters
Christopher Mwalwanda
13th TRB Transportation Planning Applications Conference
May 10, 2011
MANAGED LANE FORECASTING 101
• 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
OPERATING MANAGED LANE PROJECTS
SR 167, Seattle, WA
•2008
I-15, Salt Lake, UT
•2006
Minneapolis, MN
•I-394 , 2005
•I-35W, 2009
I-680, Alameda, CA
•2010
SR 91, Orange, CA
•1995
I-25, Denver, CO
•2006
I-95, Miami, FL
•2008
I-15, San Diego, CA
•1998
Houston, TX
•US 290 QuickRide
1998
•I-10 Katy Freeway
Managed Lanes,
2009
RECENT HOT/MANAGED LANE PROJECTS
I-405
I-25 North
•I-580
•SR 237
•SR 85 & US 101
Route 495
Lincoln
Tunnel
I-95 Section
100
US 36
•IH-635 /LBJ
•NTE
Existing Managed Lanes Projects
Planned or Under Construction
Studied
MoPac
Loop 1
Atlanta
(Various)
US 290
I-595
FORECASTING CHALLENGES
• 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
MANAGED LANE POLICIES
•
•
•
•
HOV’s
HOT’s
ETL’s
TOT’s
VARIABLE PRICING EXAMPLES
Facility
Location
Facility
Type
Pricing
Type
Comments
Fixed Variable Rates
SR 91
I-25 HOT Lanes
IH 10 Toll Lanes
Orange County, CA
Denver, CO
Houston, TX
ETL's
ETL's (HOT)
ETL's (HOT)
Preset
Preset
Preset
Varies by day of week and hour of day
HOV's free – reversible/Free Flow for Buses
HOV's free during peak periods
San Diego, CA
Minneapolis, MN
Seattle, WA
Salt Lake City, UT
Miami, FL
ETL's (HOT)
ETL's (HOT)
ETL's (HOT)
ETL's (HOT)
ETL's (HOT)
Dynamic
Dynamic
Dynamic
Dynamic
Dynamic*
Must keep free flow for HOV
Must keep free flow for HOV
Must keep free flow for HOV
Must keep free flow for HOV
Registered HOV
Dynamic Pricing
I-15 Managed Lanes
I-394 MNPASS
SR 167
I-15 Managed Lanes
I-95 Express Lanes
EXISTING ML OVERVIEW
Lanes
Project Name Length
Daily Volume
ML
GP
ML (000)
GP (000)
Annual
Revenue
(million)
Tolling Policy
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
* Reversible facilities
$35 -$40
HOV3+ discount in PM,
free all other times
Targeted Demand
― Captures
Sufficient
Targeted Daily
Demand
• Management of
Demand
― High Toll Rates
― Discourage
excessive usage
# of Years
Hyper-Congested
• Maturation of
Peak Period Congested
Market Capture
– Attracting User
Markets
– Peak Period HOV
Discounting
– HOV 2+ or 3+ Market
Segmentation
– Already Relatively
Mature Corridors
Moderately Congested
•
REVENUE
EVOLUTION OF MANAGED LANES
EVOLUTION IMPLICATIONS
• 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
REVENUE GROWTH IMPLICAITON?
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)
SR 91 - CA
Overall nominal growth:
7.5% AAGR (1998 - 2010)
[Inflation ~ 2.8%]
Real Growth ~ 4.7% AAGR
Annual Revneues (000's)
$50,000
$45,000
$40,000
$35,000
$30,000
$25,000
$20,000
$15,000
$10,000
$5,000
$0
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
REVENUE – POLICY IMPLICATIONS
2010 Estimates from Available Data
$120,000
$100,000
$80,000
$60,000
$40,000
$20,000
$0
SR 167_WA
I-394 MN
I-25_CO
(Reversible) (Reversible)
IH-10_TX
I-95_FL
SR 91_CA
30,000
25,000
20,000
15,000
10,000
5,000
0
SR 167_WA
I-394 MN
(Reversible)
I-25_CO
(Reversible)
2010 Estimates from Available Data
$3.50
$3.00
$2.50
$2.00
$1.50
$1.00
$0.50
$0.00
SR 167_WA
IH-10_TX
GP Daily AADT/Lane
Monthly Revenue/Mile/Direction
Revenue per Tolled Vehicle
Monthly Revneues
$140,000
General Purpose Daily AAADT per lane
2010 Estimates from Available Data
$160,000
I-394 MN
I-25_CO
(Reversible) (Reversible)
IH-10_TX
Average Revenue/Vehicle
I-95_FL
SR 91_CA
I-95_FL
SR 91_CA
MANAGED LANE TRAFFIC – KEY FACTORS
• 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.
LONG-TERM CONSIDERATIONS
A good forecaster is not smarter than everyone else, they
merely have their ignorance better organized
Anonymous
• Economic Growth
– Long-term Cyclical Trends/ Diversification of Growth
• Traffic Growth Profiles
– Seasonality/Weekly/Daily/Hourly Distributions
• Values of Time
– Income Growth and Distributions
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
ECONOMIC GROWTH
• 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
The past does not repeat itself, but it rhymes. Mark Twain
ECONOMIC GROWTH
“Forecasters tend to use
historical data for support
rather than illumination”
700,000
Montgomery County
Census
600,000
1972 Projection
1986 Projection
500,000
1992 Projection
400,000
2005 Projection
300,000
Harris Co.
200,000
100,000
0
1960 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
Brazoria Co.
6,000,000
700,000
600,000
Census
Fort
Bend
Co.
Fort
Bend
County
Census
5,000,000
Harris County
1972 Projection
1986 Projection
1972 Projection
4,000,000
500,000
400,000
1992 Projection
2005 Projection
1986 Projection
3,000,000
1992 Projection
300,000
2,000,000
2005 Projection
200,000
1,000,000
100,000
0
Galveston Co.
1960 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
0
1960 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
DETERMINING OPTIMUM TOLLS RATES
• 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?
TOLL RATE ESCALATION
• Does it Necessarily Fall in Line with CPI?
– Traditional Toll Facilities have not kept up with inflationary trends
– What about managed lanes?
SR 91 Toll Rate Trends
8.0%
6.0%
4.0%
2.0%
0.0%
Westbound
Eastbound
Regional LA CPI
11:00 PM
10:00 PM
9:00 PM
8:00 PM
7:00 PM
6:00 PM
5:00 PM
4:00 PM
3:00 PM
2:00 PM
1:00 PM
12:00 PM
11:00 AM
10:00 AM
9:00 AM
8:00 AM
7:00 AM
6:00 AM
5:00 AM
4:00 AM
3:00 AM
2:00 AM
1:00 AM
-2.0%
12:00 AM
Average Annual Growth (2001-2010)
10.0%
MAJOR REVENUE DETERMINANTS
• 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
MARKET CAPTURE RATES
• Are the Capture Rates Expected to be similar in both
directions?
– Diversion to managed lanes is very situational…
SR 91 Sample Profiling Example
1.60
$1.00
$0.90
1.40
$0.80
$0.50
0.80
$0.40
0.60
$0.30
0.40
$0.20
0.20
$0.10
0.00
EB Toll Rate per Mile
WB Toll Rate per Mile
EB V/C
11:00 PM
10:00 PM
9:00 PM
8:00 PM
7:00 PM
6:00 PM
5:00 PM
4:00 PM
3:00 PM
2:00 PM
1:00 PM
12:00 PM
11:00 AM
10:00 AM
9:00 AM
8:00 AM
7:00 AM
6:00 AM
5:00 AM
4:00 AM
3:00 AM
2:00 AM
1:00 AM
$0.00
12:00 AM
Toll Rate Per Mile
1.00
$0.60
WB V/C
Volume/Capacity Ratio
1.20
$0.70
MANAGED LANE MARKET SHARES
Bi-Directional MLMarket Share (TOTAL)
(Toll-payingMarket)
2010 Estimates from Available Data
40%
35%
29%
30%
25%
20%
20%
16%
14%
15%
12%
10%
10%
6%
5%
18%
10%
4%
3% 2%
0%
SR 167_WA
I-394 MN
(Reversible)
I-25_CO
(Reversible)
Peak (6-10 & 3-7)
IH-10_TX
Weekday
Note: Market Share reflects toll paying patronage only
I-95_FL
SR 91_CA
MODAL UTILIZATION CONSIDERATIONS
• 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.)
RISK PROFILING
• 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
MANAGED LANE RISK PROPAGATION
Base Case Estimate
Early Occurrence
Late Occurrence
Moderately Congested
2015
2020
2025
2030
2035
2040
2045
2050
Year
To expect the unexpected shows a thoroughly modern intellect.
Oscar Wilde
UNCERTAINTY RANGES
f( Full Universe of Variables)
f( Key Subset Variables)
REVENUE
BASELINE
f( Key Subset Variables)
f( Full Universe of Variables)
# of years
RECENT MANAGED LANE FINANCINGS
Managed Lane
Project
Capital Beltway
(Washington D.C.)
Public
Financing
Miles
Project Grant
Financial
Method (Ultimate) Costs /Subsidy TIFIA
Close
PPP/DB
I-595 Express Lanes Availability/
(Miami)
DBFOM
North Tarrant Express
(Fort Worth)
PPP/DBFOM
IH 635 LBJ
(Dallas)
PPP/DBFOM
* Commonwealth of Virginia grant
** FDOT qualifying development funds
14.0
$1.9B
$409M* $589M
Dec
2007
13.0
Marc h
$1.8B $232M** $603M 2009
June
$2.0B $573M $650M 2009
13.0
$2.7B
10.5
$489M $850M June 2010
$8.0
2010
$7.2
$7.0
2050
2.3%
3.6%
$5.0
$5.1
$4.5
$4.0
$4.0
1.5%*
$3.9
$3.4
$3.3
$2.9
$3.0
$2.6
4.7%
$2.0
$1.0
2020
4.8%
$6.0
$2.1
$1.8
$1.3
$1.2
$1.0
$0.6
$0.1
$2.1
$2.1
$1.6
$0.3
$0.0
26,000
NTE Segment 1
NTE Segment 2
Capital Beltway
26,250
33,602
23,000
*Escalated from 2040 results
LBJ
21,652
27,717
I-95
Miami
10,483
13,420
IH 10
24,300
31,106
S.R. 91
29,000
S.R. 91 S.R. 91 Existing Existing Lender Sponsor Lender Sponsor Lender Sponsor Lender Sponsor
(1999) (2010)
22,400
GP Daily
Vehs/Lane
Annual Revenue Per Mile per Lane (Millions Real $ 2010)
MANAGED LANE REVENUE RISK
INTERPRETATION AND CONCLUSIONS
• 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
THANK YOU
Christopher Mwalwanda
Vice President
Wilbur Smith Associates
cmwalwanda@wilbursmith.com