Transcript Road User Effects Modelling in HDM-4

Road User Effects Modelling
in HDM-4
Christopher R. Bennett
Highway and Traffic Consultants
HIGHWAY DESIGN AND MAINTENANCE
STANDARDS MODEL (HDM-III)
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Developed by the World Bank and released in 1987
Used in over 100 countries for different types of
investment studies
Predicts pavement performance over time and under
traffic and effects of maintenance on pavements
Predicts the effects of pavement and operating
conditions on vehicle operating costs (VOC)
Fundamental relationships based on research
conducted in Kenya, the Caribbean, India and Brazil
1971-1984
DEVELOPMENT OF HDM MODEL
Highway Cost
Model
1971
de Weille
1966
Caribbean Study 1977-82
India Study 1976-82
Brazil Study 1975-84
HDM-III
1987
Kenya Study
1971-75
HDM-II
1981
HDM-VOC Model 4
1994
HDM TECHNICAL RELATIONSHIPS
STUDY (HTRS)
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Funded by ADB
Led by N.D. Lea International Ltd. (Canada)
Hosted and supported by Institut Kerja Raya
Malaysia (IKRAM)
Other participants:
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Bill Paterson (World Bank)
Works Consultancy Services (New Zealand)
Department of Transport (South Africa)
Van Wyk and Louw (South Africa)
TRL (U.K.)
University of Auckland, University of Pretoria, Michigan
Technical Institute
– Snowy Mountain Engineering Corporation (Australia)
– Various Individuals
HTRS Approach
 Key
areas for attention identified at
HDM-4 UK and Malaysian workshops
 Virtually no primary research
 Primarily consisted of reviewing existing
research and implementing/adapting
results
 Working papers circulated to a large
number of reviewers and comments
incorporated into final report
REVIEW OF PREVIOUS EXPERIENCES
WITH HDM
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Contacted academics, consultants, governments, lending
agencies
Identified studies in over 100 countries
Used the results to identify the key areas requiring
attention in HDM-4 and for preparing draft specifications
Very few studies undertook rigorous calibration/
adaptation of HDM
Summarised parameter values by region and study
Identified alternative models and relationships to those in
HDM-III
VOC and RDME results presented in two internal reports
Components of RUE
Road User
Effects
Vehicle Operating
Costs
Fuel
Consumption
Ty re
Consumption
Parts
Consumption
Oil and
Lubricants
Consumption
Labour Hours
Motorised Travel
Time Costs
Uncongested
Trav el Time
Interest
Depreciation
Ov erheads
Delay Due To
Congestion
Delay Due To
Road Works
Non-Motorised
Traffic
Impact on
Motorised
Traf f ic
Operating
Costs
Accident Costs
Environmental
Impact
Noise
Other Effects
Vehicle
Emissions
Trav el Time
Road User Effects Researched by HTRS
RUE Research
HDM
VETO
Scandanavian
Studies
NIMPAC VOC
ARFCOM
Australian
Studies
HDM Study
NZVOC
South African
Studies
CB-ROADS
RTIM
Kenya
Caribbean
Studies
COBA
U.K. Studies
TRDF Model
Intermediate
Brazil Study
MicroBENCOST
VOC
Winfrey
Claffey
Red Book
 Most
models in use
draw on HDM-III
 No major RUE
studies since HDMIII
 Several studies
addressed HDM-III
calibration or
investigated single
components - e.g.
fuel
Key Changes to HDM-III
 Unlimited
number of representative vehicles
 Reduced car maintenance costs
 Changes to utilisation and service life
modelling
 Changes to capital, overhead and crew costs
 New fuel consumption model
 New oil consumption model
 Changes to speed prediction model
 Use of mechanistic tyre model for all vehicles
New Features in HDM-4
 Effects
of traffic congestion on speed, fuel,
tyres and maintenance costs
 Non-motorised transport modelling
 Effects of roadworks on users
 Traffic safety impact
 Vehicle noise impact
 Vehicle emissions impact
Factors Influencing RUE
RUE Component
Fuel
Tyres
Parts
Labour
Depreciation and Interest
Crew and Overheads
Passenger Time
Oil
Road Works
Traffic Safety
Vehicle Noise
Vehicle Emissions
Geometry
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HDM-III
Condition
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Capacity
Geometry
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HDM-4
Condition
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Capacity
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Motorised Transport
Motorised
Transport
Motorcycles
Motorcycles
(1)
Passenger
Cars
Small Car
(2)
Large Car
(4)
CATEGORIES
Utilities
Light Delivery
Vehicle
(5)
Four Wheel
Drive
(7)
Trucks
Light Truck
(8)
CLASSES
Buses
Heavy Truck
(10)
Mini-bus
(12)
Medium Bus
(14)
Coach
(16)
TYPES
Medium Car
(3)
Light Goods
Vehicle
(6)
Medium Truck
(9)
Articulated
Truck
(11)
Light Bus
(13)
Heavy Bus
(15)
Non-Motorised Transport
NonMotorised
Transport
Pedestrian
Bicycle
Pedestrian
Bicycle
CycleRickshaw
Passenger
(Commercial)
Freight
(Commercial)
CATEGORIES
Animal Cart
Farm Tractor
Animal Cart
Farm Tractor
CLASSES
TYPES
Freight
(Private)
Maintenance and Repair Cost
Modelling
Parts and Labour Costs
 Usually
largest single component of VOC
 In HDM-III user’s had choice of Kenya,
Caribbean, India and Brazil models
 All gave significantly different predictions
 Most commonly used Brazil model had
complex formulation
 Few studies were found to have calibrated
model
Brazil Parts - Roughness
1.8
Parts Consumption as % New Vehicle Price/1000 km .
1.6
1.4
1.2
PC and LDV
1.0
0.8
MT
0.6
HT
AT
0.4
0.2
HB
0.0
0.0
2.0
4.0
6.0
8.0
10.0
Roughness in IRI m/km
12.0
14.0
16.0
18.0
20.0
Brazil Parts - Age
Parts Consumption as % New Vehicle Price/1000 km .
0.35
0.30
PC and LDV
AT
0.25
HT
0.20
MT
0.15
HB
0.10
0.05
0.00
0
50000
100000
150000
200000
250000
300000
Cumulative Kilometreage in km
350000
400000
450000
500000
Observations on Brazil Model
 there
are inconsistencies in the Brazil
predictions between vehicles
 users believe that HDM-Brazil often overestimates parts consumption
 users found the model difficult to calibrate
 because of the non-linearity of the parts
consumption relationships, assuming that all
vehicles are midway through their life gives a
distorted estimate
Observations Continued:
 some
analysts (eg RTIM) prefer the use
of a logit model over a continually
increasing roughness model
 the use of standardised parts results in
a distortion of the costs
 significant regional variations in
maintenance practices
Ratio of Parts to Labour
Study
Brazil
India
Kenya
Vehicle Class
Car and Utility
Light & Medium Truck
Heavy Truck
Bus
Car
Utility
Truck (7t)
Truck (14 t)
Truck (24t)
Bus
Car and Utility
Truck
Bus
PARTS
(x 103)
1.82
1.15
1.47
0.52
0.71
0.71
0.15
0.22
0.22
0.33
1.94
1.46
0.19
Notes: LH = labour hours per 1,000 km
PARTS = standardised parts cost per 1,000 km
Ratio = LH/(Parts x 103)
Results are for vehicle age - 100,000 km and IRI = 3
LH
Ratio
2.45
7.23
10.20
7.29
16.83
24.29
8.34
10.63
20.31
15.68
1.36
4.14
0.47
1.4
6.3
7.0
14.1
23.7
34.2
54.2
47.7
33.8
48.0
0.7
2.8
2.5
Data Sources
 Small
studies conducted in:
– Botswana
– New Zealand
– Pakistan
– South Africa
– St. Helena
– Sweden
 NO
major studies identified
NDLI Proposals
 Replace
HDM-III Brazil model with
linear model
 Standardise predictions to 100,000 km
 Eliminate roughness effects below 3 IRI
1995 RUE Workshop
Proposals
 Linear
model definite improvement over
HDM-III
 Significantly reduce the light vehicle parts
consumption
 Increase the heavy bus parts consumption
 Slightly reduce truck parts consumption
 Modify coefficients to account for
survivorship bias and technical
improvements
Proposal for HDM-4
 Linear
models:
– PARTS = {K0pc [CKM^kp (a0 + a1 RI)]
+ K1pc} (1 + CPCON dFUEL)
– LH = K0lh [a2 PARTS^a3] + K1lh
 Adjusted
roughness:
– RI = max(IRI, min(IRI0, a0 + a1 IRI^a2))
Adjusted Roughness
4.0
3.8
Adjusted Roughness in IRI m/km .
3.6
IRI0 = 4.0
3.4
IRI0 = 3.5
3.2
3.0
IRI0 = 3.25
2.8
IRI0 = 3.1
2.6
2.4
2.2
2.0
2.0
2.2
2.4
2.6
2.8
3.0
3.2
Actual Roughness in IRI m/km
3.4
3.6
3.8
4.0
Parameter Values
 Estimated
from HDM-III Brazil model
 Exponential models converted to linear
models which gave similar predictions
from 3 - 10 IRI
 Roughness effects reduced 25% for trucks
 For cars, roughness effects same as for
trucks
 For heavy buses, roughness effects
reduced further 25%
Implications of Changes
Vehicle
Passenger Car
Light Truck
Heavy Truck
Articulated Truck
Heavy Bus
Increase in Parts Consumption as Fraction of New Vehicle Price (x 10-3)
From 3-10 IRI
CKM = 100,000
CKM = 300,000
HDM-III
HDM-4
HDM-III
HDM-4
4.70
1.49
6.59
2.08
2.45
1.49
3.67
2.23
1.98
1.49
2.58
2.23
1.42
1.49
2.14
2.23
0.20
0.99
0.34
1.68
Age Effects
 Parts
modelled at 0.5 of vehicle life
 User will be able to enter an age
distribution and have this used in
calculations
Congestion Effects
 Parts
consumption is assumed to
increase under congested conditions
 Use equation:
– PARTS = PARTS (1 + CPCON dFUEL)
 Default
value for CPCON is 0.10
indicating that a 100% increase in fuel
results in a 10% increase in parts
Utilisation and Service Life
HDM-III
 Contained
three utilisation methods:
– Constant Kilometreage
– Constant Hours
– Adjusted Utilisation
 Contained
two service life methods:
– Constant Service Life
– de Weille’s Varying Service Life
Adjusted Utilisation
 Predicted
utilisation as function of
speed and ‘elasticity of utilisation’
 Default elasticity values derived from
Brazil study
 Some Brazilian vehicles had unusually
high utilisations
 Analysts tended to adopt default values
Elasticity Values Applied
Vehicle
Class1
Mean
PC
LDV-LGV
LT
MT
HT
AT
LB
MB
HB
0.40
0.54
0.54
0.52
0.58
0.64
0.43
0.62
0.55
Elasticity of Vehicle Utilisation
Std. Dev. Minimum Maximum Number
0.24
0.31
0.29
0.28
0.25
0.22
0.28
0.39
0.25
0.10
0.15
0.25
0.20
0.30
0.40
0.15
0.17
0.23
0.80
0.90
0.85
0.90
0.90
0.90
0.80
0.85
0.90
13
15
5
11
12
7
5
3
10
Per cent
Using
Default
31
30
40
27
33
29
20
66
20
Effect of Speed on Utilisation
180000
160000
Annual Utilisation in km/yr .
140000
120000
EVU = 0.0
100000
80000
EVU = 0.1
60000
EVU = 0.2
40000
EVU = 0.4
EVU = 0.6
20000
EVU = 0.8
EVU = 1.0
0
0
10
20
30
40
50
Speed in km/h
60
70
80
90
100
Service Life Modelling
 de
Weille’s method based on the
assumption that the faster the vehicle
travels the shorter the life
 No empirical data to support method
 Made costs very sensitive to speed
Speed on Service Life
1.5
1.4
1.3
50 km/h
25 km/h
100 km/h
75 km/h
Service Life Factor .
1.2
1.1
1.0
0.9
0.8
0.7
0.6
0
10
20
30
40
50
Predicted Speed in km/h
60
70
80
90
100
Methods Applied
Country
Australia
Bangladesh
Barbados
Botswana
Burundi
Ethiopia
Guatemala
India
Jordan
Indonesia
Myanmar
Nepal
Nigeria
Romania
Russia
Spain
South Africa
Thailand
Trinidad
Annual Utilisation
Constant
Adjusted
km
hours
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•
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•
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•
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•
•
•
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•
•
Service Life
Constant
Varying
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Implications of Methods
5.0
Constant Life - Varying Utilisation
4.5
Depreciation Factor (x 10^-6) .
4.0
3.5
3.0
2.5
Varying Life Varying Utilisation
2.0
1.5
Constant Life - Constant Utilisation
1.0
0.5
Varying Life - Constant Utilisation
0.0
0
10
20
30
40
50
60
Predicted Speed in km/h
70
80
90
100
Recommendations - Service Life
 NDLI
recommended use of ‘Optimal
Life’ (OL) model
 1995 RUE Workshop recommended OL
model
 Proposed that OL model be adopted for
HDM-4
Recommendations - Utilisation
 NDLI
proposed modified adjusted
utilisation method
 1995 RUE Workshop did not support
method
 TRL have proposed alternative method
for utilisation
 Recommended that TRL method be
adopted
Capital Costs
Modelling Approach
 Comprised
of depreciation and interest
costs
 HDM-III used a simple linear model
 Affected by operating conditions
through the effects of speed on
utilisation and speed on service life (de
Weille’s method)
 HDM-4 will use ‘Optimal Life’ method
Optimal Life Method
 Proposed
by Chesher and Harrison
(1987) based upon work by Nash
(1974)
 Underlying philosophy is that the
service life is influenced by operating
conditions, particularly roughness
 Relates life -- and capital costs -- to
operating conditions
OL Method
Discounted Area = New Vehicle Price
Running Costs
Costs per year
RUN(OL)
OL
Vehicle Age in years
Implementation
 NDLI
found that the OL method had
problems when applied with ‘typical’
field data
 Chesher (1995) proposed addressing
problems by adjusting age effects for
survivorship bias
Implicaitons of Age
Modification
Running Costs - Modified
D
E
C
Costs per year
B
Running Costs - Default
A
OL - Modified
Vehicle Age in years
OL - Default
HDM-4 Implementation
 User
defines ‘target’ OL in km at low
roughness (3 IRI)
 User defines financial replacement
value and utilisation characteristics
 The age exponent is calibrated
 Effect of roughness on service life
established
 Depreciation calculated
Roughness on Life
100
Optimal Life as Percentage of Baseline Utilisation .
90
80
70
60
50
40
30
20
10
0
0
2
4
6
8
10
Roughness in IRI m/km
12
14
16
18
20
Roughness on Depreciation
7
PC
LT
MT
HT
AT
LB
MB
HB
MC
6
Depreciation Cost in Baht/km .
5
4
3
2
1
0
0
5
10
15
Roughness in IRI m/km
20
25
Fuel Consumption
Fuel Model
 Replaced
HDM-III
Brazil model with
one based on
ARRB ARFCOM
model
 Predicts fuel use
as function of
power usage
TRACTIVE FORCES
Rolling, air, inertia, grade
and cornering resistance
ACCESSORIES
Cooling fan,
power steering,
air conditioner,
alternator, etc.
DRIVE - TRAIN
INEFFICIENCIES
TOTAL POWER
ENGINE FUEL EFFICIENCY FACTOR
ESTIMATED FUEL CONSUMPTION
INTERNAL
ENGINE
FRICTION
Forces Opposing Motion
 Calculates:
– aerodynamic resistance (Fa)
– rolling resistance (Fr)
– gradient resistance (Fg)
– curvature resistance (Fcr)
– inertial resistance (Fi)
 Uses
more detailed equations than
HDM-III
Modifications
 Made
modifications to ARFCOM
approach to improve predictions of
engine and accessorypower
 Replaced engine speed equations with
speed based function from simulation
Model Parameters
 Two
basic model parameters for use:
– idle fuel rate
– fuel conversion efficiency factor
 Parameters
can be readily derived from
other fuel models
 Expect to provide a range of values for
different vehicle types from various
published sources
Implications of New Model
 Lower
rates of fuel consumption than
HDM-III for many vehicles
 Effect of speed on fuel significantly
lower for passenger cars
 Considers other factors -- eg surface
texture and type -- on fuel
 Model can be used for congestion
analyses
Speeds
Speed Model
 Minor
changes to HDM-III probabilistic
model
 Same model form:
VSS 
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exp 
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1
1
1
1
1
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1
1
1
1
1
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 VDRIVE
VBRAKE
VCURVE
VROUGH
VDESIR  
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 Refinement
speeds
of some constraining

Congestion Effects
HDM-4 Congestion Modelling
 HDM-III
did not consider congestion
 HDM-95 considered effects of
congestion on speeds but not on other
VOC
 HDM-4 expanded the HDM-95
approach to consider other VOC
components
HDM-95 Speed-Flow Model
S1
Speed km/h
S2
S3
Snom
Sult
Qo
Flow in PCSE/h
Qnom
Qult
Recommended Model
Parameters
Road Type
Single Lane Road
Intermediate Road
Two Lane Road
Wide Two Lane Road
Four Lane Road
Width
(m)
<4
4 to 5.5
5.5 to 9
9 to 12
>12
Qo/
Qult
0.0
0.0
0.1
0.2
0.4
Qnom/
Qult
0.70
0.70
0.90
0.90
0.95
Qult
(PCSE/h)
600
1800
2800
3200
8000
Sult
(km/h)
10
20
25
30
40
HDM-4 Congestion Model
 3-Zone
model predicts as flows
increase so do traffic interactions
 As interactions increase so do
accelerations and decelerations
 Adopted concept of ‘acceleration noise’
-- the standard deviation of acceleration
Acceleration Noise
Uncongested
Congested
0
Acceleration in m/s/s
Acceleration Noise
 Modelled
with two components: traffic
induced and ‘natural’ noise
 Traffic noise function of flow
 Natural noise function of:
• driver’s natural variations
• road alignment
• roadside friction
• non-motorised transport
• roughness
Traffic Noise
 Modelled
using
sigmoidal function
 Integrated with
Three-zone Model
 The maximum traffic
noise and ratio
Q0/Qult governs
predictions
 Easy to calibrate
Natural Noise
Natural Acceleration Noise
 Driver
NMT
IRI
Friction
NMT Occupancy
IRI
Roadside Friction
and
alignment noise
combined
 Side friction, nonmotorised transport
and roughness
assumed to be
linear
 Maximum values of
0.20, 0.40 and 0.30
m/s/s respectively
Calculation Approach
 Run
as calibration routine once unless
vehicle characteristics changed
 Uses Monte Carlo simulation of a
vehicle travelling down a road with
different levels of acceleration noise
 Determines additional fuel as function
of noise
 Results in matrix of values of dFUEL vs
Mean Speed vs Accel. Noise
Typical Simulated Accel.
Profile
0.4
0.3
Acceleration in m/s/s
0.2
0.1
0.0
0
10
20
30
40
-0.1
-0.2
-0.3
Time at which vehicle had
travelled 1000 m
-0.4
Time in s
50
60
70
Simulation Results - Small Car
1.60
1.40
1.20
1.00
dFUEL 0.80
0.75
0.60
0.65
0.55
0.40
0.45
0.20
0.35
0.25
0.05
100
90
70
80
Speed in km/h
60
50
0.15
40
30
20
10
0.00
Total Acceleration
Noise (m/s/s)
Simulation Results - Artic.
Truck
1.60
1.40
1.20
1.00
dFUEL 0.80
0.75
0.60
0.65
0.55
0.40
0.45
0.20
0.35
0.25
0.05
100
90
70
80
Speed in km/h
60
50
0.15
40
30
20
10
0.00
Total Acceleration
Noise in m/s/s
Flow on Additional Fuel
40
35
NZ - Two-lane
India - Two-lane
Additioanl Fuel in mL/km
30
India - One-lane
25
20
15
10
5
0
0.0
0.1
0.2
0.3
0.4
0.5
Relative Flow
0.6
0.7
0.8
0.9
1.0
Tyre Consumption
HDM-4 Tyre Model
 Did
not prove possible to locate any
major new tyre research since HDM-III
study
 Swedish team recommended simple
procedure for adapting HDM-III
parameters as function of tyre life
 This was applied and parameters
estimated for light vehicles to allow for
consistent modelling
Mechanistic Model
 Tyre
consumption proportional to forces
on tyre
 Increase with 4th power of speed
 Does not consider ablative wear or
surface material properties
Oil Consumption
Oil Consumption Model
 HDM-III
only function of roughness
 Recommended by NDLI to eliminate
from HDM-4
 1995 RUE Workshop indicated should
be included
 Model contains two components
– Fuel use due to contamination
– Fuel use due to operation
Heavy Vehicle Trailers
Modelling
 User
defines trailer to be associated
with a towing vehicle
 Trailer leads to higher mecahnistic
forces
 Use standard HDM-4 speed, fuel, tyre,
capital co models
Maintenance and Repair
Costs
 Based
on unpublished NZ study
 Original research did not relate costs to
roughness
 Assumed linear increase of 20%
between 3 and 7 IRI
Additional Costs Due to
Speed Changes
Speed Change Cycle
 Two
principal components
– Deceleration from initial to final speed
– Acceleration from final to original (or other)
speed
 May
include idling or travel at reduced
speed
 Important for work zones and other
specific traffic interruptions
Speed Cycle Model
 Used ARRB
Polynomial Acceleration
model
 Time to accelerate and decelerate from
NZ research
Example of Speed Cycle
100
90
80
Speed in km/h _
70
60
50
40
30
20
10
0
0
5
10
15
20
Time in s
25
30
35
40
Acceleration Profile
2
Acceleration in m/s/s _
1
0
-1
-2
-3
-4
0
5
10
15
20
Time in s
25
30
35
40
Model Development
 Used
NZVOC Model
 Predicts additional fuel and time due to
speed changes
 Defined as:
ADDCST = (DECCST + ACCCST) - UNICST
 Costs
calculated as function of initial
and final speed for acceleration and
deceleration by vehicle class
Models
 Developed
regression models for the
additional time and additional fuel to
accelerate/decelerate
 Parameter values function of initial and
final speed
Work Zones
Bias Due to Use of Means
Use of Means
 HDM-III
uses mean speeds in
calculations
 For non-linear functions (eg fuel, time)
this leads to bias in results
 1995 RUE Workshop requested this be
considered in HDM-4
Findings
 Used
simulation model to predict the
fuel and time as function of COV
 Bias for travel time less than 1% so
recommended it be ignored
 Fuel consumption bias more significant
and data used to develop correction
equation:
FUELBIAS = 1.0000 - 0.0182 COV + 0.7319 COV^2
Time Versus Space Speeds
Speeds
 Time
Mean Speed - mean speed of all
vehicles passing a point
 Space Mean Speed - mean speed of all
vehicles over a section over a time
period
 HDM predicts time speed but space
speed is correct measure
Speed Corrections
 Ran
simulation to calculate space
speeds as function of time speed and
COV
 Error generally less than 2% but since
easy to correct for proposed equation:
SPEEDBIAS = 1.0000 + 0.0122 COV - 0.8736 COV^2
Emissions
HDM-4 Model
 Developed
by VTI in Sweden
 Conducted statistical analysis of
emissions as function of fuel use
 Developed simple linear model
Noise
HDM-4 Model
 Proposed
by NDLI to adopt UK CRTN
model for HDM-4