HDM-4 Road User Effects Road User Effects.

HDM-4 Road User Effects Road User Effects

Road User Effects

RUE Research Most models in use draw on HDM-III
No major RUE studies since HDM-III Several studies addressed HDM-III calibration or investigated single components - e.g. fuel

Key Changes to HDM-III Unlimited number of representative vehicles
Reduced maintenance and repair costs Changes to utilization and service life modeling Changes to capital, overhead and crew costs New fuel consumption model New oil consumption model Changes to speed prediction model Use of mechanistic tire model for all vehicles

New Features in HDM-4 Effects of traffic congestion on speed, fuel, tires and maintenance costs Non-motorized transport modeling Traffic safety impact Vehicle emissions impact

Representative Vehicles
Motorized Traffic Non-Motorized Traffic Motorcycle Small Car Medium Car Large Car Light Delivery Vehicle Light Goods Vehicle Four Wheel Drive Light Truck Medium Truck Heavy Truck Articulated Truck Mini-bus Light Bus Bicycles Rickshaw Animal Cart Pedestrian Medium Bus Heavy Bus Coach

Road User Costs Components
Fuel Lubricant oil Tire wear Crew time Maintenance labor Maintenance parts Depreciation Interest Overheads Passenger time Cargo holding time Vehicle Operating Costs Time Costs Accidents Costs

Vehicle Speed and Physical Quantities
Roadway and Vehicle Characteristics Vehicle Speed Physical Quantities Unit Costs Road User Costs

Physical Quantities Component Quantities per Vehicle-km Fuel liters
Lubricant oil liters Tire wear # of equivalent new tires Crew time hours Passenger time hours Cargo holding time hours Maintenance labor hours Maintenance parts % of new vehicle price Depreciation % of new vehicle price Interest % of new vehicle price

Free-Flow Speeds Model
Free speeds are calculated using a mechanistic/behavioral model and are a minimum of the following constraining velocities. VDRIVEu and VDRIVEd = uphill and downhill velocities limited by gradient and used driving power VBRAKEu and VBRAKEd = uphill and downhill velocities limited by gradient and used braking power VCURVE = velocity limited by curvature VROUGH = velocity limited by roughness VDESIR = desired velocity under ideal conditions

Free-Flow Speeds Model
EXP( ^2/2) Vu = / / / / / (------) +(------) +(------) +(------) +(------) VDRIVEu VBRAKEu VCURVE VROUGH VDESIR EXP( ^2/2) Vd = / / / / / (------) +(------) +(------) +(------) +(------) VDRIVEd VBRAKEd VCURVE VROUGH VDESIR V = 2 / ( 1/Vu + 1/Vd ) Vu = Free-flow speed uphill Vd = Free-flow speed downhill V = Free-flow speed both directions  = Sigma Weibull parameter  = Beta Weibull parameter

Free-flow Speed and Roughness
VBRAKE VCURVE VROUGH VDRIVE VDESIR Speed Curvature = 25 degrees/km Gradient = %

Free-flow Speed and Gradient
VCURVE VBRAKE VROUGH VDRIVE VDESIR Speed Roughness = 3 IRI m/km Curvature = 25 degrees/km

Free-flow Speed and Curvature
VBRAKE VCURVE VROUGH VDRIVE VDESIR Speed Roughness = 3 IRI m/km Gradient = %

VDRIVE Drive Force Grade Resistance Air Resistance Rolling Resistance
- Driving power - Operating weight - Gradient - Density of air - Aerodynamic drag coef. - Projected frontal area - Tire type - Number of wheels - Roughness - Texture depth - % time driven on snow covered roads - % time driven on water covered roads

For uphill travel, VBRAKE is infinite.
For downhill travel, VBRAKE is dependent upon length of gradient. Once the gradient length exceeds a critical value, the brakes are used to retard the speed. - Braking power - Operating weight - Gradient - Density of air - Aerodynamic drag coef. - Projected frontal area - Tyre type - Number of wheels - Roughness - Texture depth - % time driven on snow covered roads - % time driven on water covered roads - Number of rise and fall per kilometers

VCURVE VCURVE is calculated as a function of the radius of curvature.
VCURVE = a0 * R ^ a1 R = Radius of curvature R = 180,000/(*max(18/ ,C)) C = Horizontal curvature a0 and a1 = Regression parameters

VROUGH VROUGH is calculated as a function of roughness.
VROUGH = ARVMAX/(a0 * RI) RI = Roughness ARVMAX = Maximum average rectified velocity a0 = Regression coefficient

VDESIR VDESIR is calculated as a function of road width, roadside friction, non-motorized traffic friction, posted speed limit, and speed enforcement factor. VDESIR = min (VDESIR0, PLIMIT*ENFAC) PLIMIT = Posted speed limit ENFAC = Speed enforcement factor VDESIR0 = Desired speed in the absence of posted speed limit VDESIR0 = VDES * XFRI * XNMT * VDESMUL XFRI, XNMT = Roadside and NMT factors VDESMUL = Multiplication factor VDES = Base desired speed

Speeds Computational Logic
Calculate Free-Flow Speed for each vehicle type Calculate the following for each traffic flow period: Flow in PCSE/hr Vehicle operating speed (Speed flow model) Speed change cycle (Acceleration noise) Vehicle operating costs Travel time costs Calculate averages for the year

Number of Hours in the Year
Traffic Flow Periods To take into account different levels of traffic congestion at different hours of the day, and on different days of the week and year, HDM-4 considers the number of hours of the year (traffic flow period) for which different hourly flows are applicable. Flow Flow Periods Peak Next to Peak Medium flow Next to Low Overnight Number of Hours in the Year

Passenger Car Space Equivalent (PCSE)
To model the effects of congestion, mixed traffic flow is converted into equivalent standard vehicles. The conversion is based on the concept of ‘passenger car space equivalent’ (PCSE), which accounts only for the relative space taken up by the vehicle, and reflects that HDM-4 takes into account explicitly the speed differences of the various vehicles in the traffic stream.

Difference Between PCSE and PCU
consider two factors: space occupied by vehicle speed effects used in highway capacity calculations PCSE (HDM-4) considers only space occupied speed effects considered separately through speed model

PCSE Length Gap Space (m)

Speed Flow Model To consider reduction in speeds due to congestion, the “three-zone” model is adopted. Speed (km/hr) S1 S2 S3 S4 Sult Flow PCSE/hr Qo Qnom Qult

Speed Flow Model Qo = the flow below which traffic interactions are negligible Qnom = nominal capacity Qult = ultimate capacity for stable flow Snom = speed at nominal capacity (0.85 * minimum free speed) Sult = speed at ultimate capacity S1, S2, S3…. = free flow speeds of different vehicle types Speed-Flow Model Parameters by Road Type

Speed Flow Types Speed (km/hr) Four Lane Wide Two Lane Two Lane Flow
veh/hr Road User Effects

Roadside Friction

Fuel Model Replaced HDM-III Brazil model with one based on ARRB ARFCOM model Predicts fuel use as function of power usage The HDM-4 fuel consumption model predicts that the fuel consumption is a function of the power usage. This power is predicted as: Tractive forces. The forces opposing motion which are predicted using mechanistic principles Engine accessories. The power required to operate the engine accessories such as the air conditioner Engine drag. The power required to overcome internal engine drag. The instantaneous fuel consumption (in mL/s) is calculated by multiplying the total power usage in kW by a fuel efficiency conversion factor. The minimum fuel consumption is a function of the idle fuel consumption. Road User Effects

Implications of New Fuel 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 – e.g. surface texture and type -- on fuel Model can be used for congestion analyses There are some key implications of the new fuel model over that from HDM-III: Lower Fuel Consumption. The predicted fuel consumption is lower than it was in HDM-III. This is due to improvements in vehicle technology and the reformulation of the model structure. Effect of Speed on Fuel Reduced. The HDM-III model tended to distort the effect of speed on fuel use due to an excessive amount of fuel being used to maintain engine operation. This was due in part to the 1970s technology of the HDM-III fuel models, but also to the use of CRPM (constant engine speed) instead of the actual engine speed. Considers Other Factors. The mechanistic models have been extended from those in HDM-III to include other factors such as the effects of surface type on rolling resistance. Congestion Analyses. By going to a purely mechanistic model as presented here it is practical to model the effects of traffic interactions and congestion on fuel consumption in ways not practical before. Road User Effects

Effect of Speed on Fuel Consumption
The early empirical models related fuel consumption principally to vehicle speed. A number of studies found that the relationship between fuel consumption (per unit distance) and vehicle speed is U-shaped. There are relatively high fuel consumption rates at both low and high speeds with the minimum fuel consumption arising at an “optimum” speed, generally around km/h. The reason behind this U-shape can be simply explained by considering the two extremes. At high speeds, the aerodynamic forces, which are related to the square of velocity, become dominant requiring large quantities of fuel to be consumed. While at low speeds which equate to low tractive power requirements, the idle fuel consumption that powers the engine drag and accessories dominates. Road User Effects

Congestion - Fuel 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 Road User Effects

Congestion Modelling When vehicles travel along a section of road their accelerations and decelerations approximately form a normal distribution as shown above. In the absence of congestion caused by traffic interactions the vehicle travels at an approximate steady state speed with only minor acceleration and decelerations. However, once traffic interactions become significant the level of accelerations and decelerations significantly increase. Since the acceleration noise is taken as the standard deviation of the distribution, this standard deviation increases with increasing traffic interactions. Thus, a high value for the acceleration noise indicates that there is a high level of accelerations; a low value a low level of accelerations. Road User Effects

Effects of Speed Fluctuations (Acceleration Noise)
Vehicle interaction due to: volume - capacity roadside friction non-motorised traffic road roughness driver behaviour & road geometry Affects fuel consumption & operating costs Road User Effects

dFUEL Values by Acceleration Noise and Vehicle Speed
Road User Effects

Effects Traffic Interactions on Fuel Consumption
This is an example of the additional fuel due to traffic interactions predicted using the ACCFUEL model. The vertical axis is the dFUEL which is defined as the additional fuel as a decimal. The total fuel consumption is given as: FC = (1 + dFUEL) FCSTEADY The FCSTEADY is the fuel consumption predicted without congestion effects for the mean speed. The additional fuel is a peak in the area km/h. This corresponds with the minimum fuel consumption of the fuel models. The reason why this is the peak is that whenever there is a deviation from that minimum fuel there is a marked increase in the relative fuel consumption. At low speeds there is a flattening of the dFUEL values. This is because of a limitation in the model to low speed predictions. HDM-4 reads this data as a matrix and linearly interpolates the dFUEL from the acceleration noise and the mean speed. Road User Effects

Tire Consumption Tread wear Carcass wear
amount of the tread worn due the mechanism of the tyre coming into contact with the pavement surface Carcass wear combination of fatigue and mechanical damage to the tyre carcass - affects number of retreads Tyres are consumed continuously as vehicles travel. There are two principal modes of tyre consumption: Tread wear: This is the amount of the tread worn due the mechanism of the tyre coming into contact with the pavement surface. Carcass wear: This is a combination of fatigue and mechanical damage to the tyre carcass. It is defined as the number of retreads (recappings) to which a tyre carcass can have before the carcass is unsuitable for further retreads. In many countries, blowouts or ablative wear—ie the ‘tearing’ of the tread caused by surface material—are significant factors governing tyre life. Each kilometre travelled results in a loss of tread. In addition, the forces acting on the vehicle result in stresses and strains to the tyre carcass. When the tyre tread reaches a certain depth, the tyre is either discarded or, if the economics warrant it and the carcass is in adequate condition, the tyre will be retreaded which sees new tread added to the existing carcass. Thus, in modelling tyres it is necessary to explicitly consider retreading, particularly for commercial vehicles where retreading is a common practice in many countries. Road User Effects

Factors Influencing Tire Consumption
The principal factors affecting tyre consumption include: Pavement Condition: Tyre consumption will increase with increasing roughness since that leads to increased vertical loading on the tyres. The type of surface, its condition and its texture all play important roles in the tyre consumption. Road Alignment: Tyre consumption increases significantly with the severity of road alignments, particularly with respect to horizontal curvature. Traffic Conditions: Traffic interactions give rise to accelerations and decelerations which have a strong impact on tyre consumption. Vehicle Loading: The tyre consumption is proportional to the vehicle loading and overloading, particularly when coupled with poor road condition, can lead to an increased frequency of carcass failures, especially on driven axles. Climate: The ambient temperature can have a significant impact on tyre consumption, particularly in tropical countries where it can lead to blowouts or stripping of tread. Tyre Properties: The type of tyre (radial vs bias ply), if it is a new tyre or a retread, the properties of the rubber compounds, and the inflation pressure all have an impact on the tyre consumption rate. Road User Effects

Effect of Congestion on Tyre Consumption
The congestion effects are established using the ACCFUEL model. In addition to outputting the additional fuel due to accelerations it also outputs the additional tyre consumption. This figure is an example of the output for trucks. This shows that the worst situation arises in stop-go situations due to the high levels of tyre slip and the high vehicle mass. These results are applied in a similar manner to the additional fuel. Road User Effects

Parts and Labor 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 n many analyses maintenance and repair costs are the largest single component of VOC, often upwards of 40% of the total. HDM-III had four models for predicting these costs, but the Brazil model was most commonly used. These models predicted quite different costs as a function of operating conditions, often reflecting the local maintenance practices and wage rates. In reviewing HDM studies it was found that few, if any, ever calibrated the maintenance and repair cost model. This was due to the basic complexity of doing so: it requires a large effort to collect the necessary data. Road User Effects

HDM-III (Brazil) Parts Consumption
HDM-4 Cars This slide shows the effects of roughness on parts consumption predicted using the HDM-III Brazil study equations. The predictions can be placed into three broad groups: Buses. Limited effects of roughness on parts consumption. This was also found in studies in southern Africa. One theory is that this is because they usually have truck chassis but carry light loads. Trucks. Moderate roughness effects. Cars. High roughness effects. The latter were partially attributed to the conditions which the Brazil cars were operated on: fixed schedules which saw vehicles operated at high speeds on rough roads. HDM-4 Buses Road User Effects

Parts Model Estimated from HDM-III Brazil model
Exponential models converted to linear models which gave similar predictions from IRI Roughness effects reduced 25% for trucks For cars, roughness effects same as for trucks For heavy buses, roughness effects reduced further 25% There were no data to re-estimate the parts consumption model parameters so they were estimated from the Brazil model values. The model was simplified to a linear one from the earlier HDM-III combination model with linear and non-linear terms. On the basis of expert advice, the effects of roughness were reduced from those in HDM-III to reflect improvements in technology. The amounts of these reductions are described on this slide. Road User Effects

Utilisation and Service Life
HDM-4 has either constant or ‘Optimal Life’ service life Utilisation function of hours worked for work vehicles; lifetime kilometreage for private vehicles For HDM-4 the user has two options for predicting the service life: Constant Life. The service life is independent of operating conditions. Optimal Life. The service life is a function of road roughness and related to the maintenance and repair costs. There are two utilisation methods depending upon if the vehicles are private or commercial. Commercial vehicles have utilisation a function of the hours worked; lifetime kilometreage for private vehicles. Road User Effects

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 The optimal life (OL) method is a new addition to HDM-4. The method implemented is a variation of that proposed by Chesher and Harrison (1987). The basic approach is that the service life is related to the operating conditions. Vehicles which have high maintenance costs will have shorter service lives than vehicles with lower maintenance costs. The OL method therefore integrates the HDM-4 maintenance and repair costs to the service life. Road User Effects

Optimal Life Method The basic approach behind the OL life is illustrated in this slide.The total running costs of the vehicle increase over time. The OL theory indicates that the optimal life is the point when the shaded area is equal to the replacement value of the vehicle. Since the maintenance costs are proportional to roughness, this means that vehicles operating on rougher roads will have shorter lives than those on smooth roads. Road User Effects

Roughness and Lifetime Utilisation
This slide is an example of the effect of roughness on utilisation predicted using the HDM-4 approach. Here, the utilisation is expressed as a percentage of the baseline utilisation. The effect of this is to increase the depreciation costs since the vehicle life is shortened with increasing roughness. Road User Effects

Constant Service Life Equations depend on the percentage of private use: LIFEKM = LIFE x AKM < 50% LIFEKM = S x HRWK x LIFE > 50% When using the constant service life the lifetime kilometreage differs depending on the percentage of private use. If more than 50% private use the capital costs are directly proportional to speed. Road User Effects

Capital Costs Comprised of depreciation and interest costs
HDM-III used a simple linear model affected by operating conditions through the effects of speed on utilization and service life HDM-4 uses ‘Optimal Life’ method or constant life method The depreciation costs in HDM-III were predicted using a linear model with the lifetime utilisation as the denominator. It did not take into account the residual value of the vehicle, although operating conditions were considered through their effects on utilisation and service life. Road User Effects

Depreciation in HDM-4 Depreciation calculated multiplying the replacement vehicle price by the following equation: The replacement vehicle price is reduced by a residual value which can be a function of roughness The denominator is the lifetime utilisation which may be constant or predicted with the OL method to be a function of roughness The depreciation approach in HDM-4 is similar to HDM-III except that it considers the residual value of the vehicle (as a percentage of the new vehicle price). The lifetime utilisation is predicted using the OL or constant kilometreage methods. The depreciation factor illustrated in this equation is multiplied by the replacement value of the vehicle, less tyres, to calculate the depreciation cost. Road User Effects

Roughness on Depreciation

Interest Costs Interest costs are the replacement vehicle price multiplied by the following equation: Function of speed and hours worked as well as the interest rate The interest costs are calculated in a similar manner to HDM-III. The only difference is that the annual utilisation is calculated as the product of the speed and the hours worked. Road User Effects

Oil Consumption HDM-III only function of roughness
Model contains two components Fuel use due to contamination Fuel use due to operation which is proportional to fuel use There are two components to the model. The oil loss due to contamination is the oil that is replaced when the vehicle has an oil change. It is therefore calculated as the capacity of the engine oil supply divided by the distance between changes. The oil loss due to use is that which arises when the engine is operated. This is predicted to be proportional to the fuel consumption. Thus, operating conditions will only influence the oil loss due to use. Using data from studies in South Africa model parameters were estimated. Road User Effects

Travel Time Components
Passenger Working Hours Passenger Non-Working Hours Crew Hours The time are calculated in three components: Passenger Working Hours. These are multiplied by the unit cost of working time Passenger Non-Working Hours. These are multiplied by the unit cost of non-working hours Crew Hours. These are multiplied by the crew time value--but only for vehicles which are not engaged in private use. For each vehicle, the speed is used to predict the travel time in h/1000 km. These values are then multiplied by the unit costs to get the travel time cost. Road User Effects

Road Safety HDM-4 does not predict accident rates
User defines a series of “look-up tables” of accident rates The rates are broad, macro descriptions relating accidents to a particular set of road attributes Fatal Injury Damage only While developed countries have had limited success in modelling accidents, the same cannot be said of developing countries. There is little consensus between models, dependent variables, and even analysis methods. It was decided to adopt a series of “look-up tables” for HDM-4. These give the accident rate as a function of key data, such as traffic volume and road width. When there is a change in any of these data there is a corresponding reduction in the accident rate. Road User Effects

Accident Groups Road type, class, use Traffic level
Geometry, pavement type, ride quality, surface texture, presence of shoulders Non-motorised traffic Intersection type

Accident Rates and Exposure
The accident rate is the number of accidents divided by the exposure It is typically expressed in number per 100 million vehicle-km Due to its complexity and uncertainty, traffic safety has traditionally been excluded from many highway investment analyses. For example, in HDM-III there was no mechanism for modelling traffic safety beyond treating it as an exogenous cost. The growing realization of the substantial costs of road accidents (often over 1 per cent of GNP) has renewed the desire to include possible accident savings along with reductions in vehicle operating costs and time savings in economic appraisals. At the onset of the ISOHDM project it was identified as a key factor to be included in HDM-4. The HDM-4 model is based on the concept of exposure. This is calculated in terms of 100 million veh-km using the equations shown here. The accident rate per 100 million veh-km is then established as the number of accidents per year divided by the exposure. Road User Effects

Accident Rates Examples
Number per 100 million vehicle-km - South Africa: Economic Warrants for Surfacing Roads, 1989, SABITA and CSIR - Canada : The Economic Appraisal of Highway Investment “Guidebook”, 1992, Ministry of Transportation

VOC Calibration Procedure (Level 1)
Vehicle mass & ESAL Road Capacity & Speed-Flow factors Vehicle service life Vehicle Utilisation (Annual KM & Hours) Desired speed Vehicle engine power, speed (rpm) & braking Tyre characteristics (type, rubber volume, etc) Vehicle depreciation Aerodynamic factors

Calibration Very Important

Non-Motorised Transport (NMT)

NMT User Costs and Benefits
Travel speed and time Wear and tear of some NMT vehicles and components Degree of conflicts with MT traffic Accident rates Energy consumption Road User Effects

Factors which influence NMT Speed
MT traffic volume and speed NMT traffic volume roadside activities roadway grade rolling resistance inclement weather road width (where NMT can travel safely) and/or number of lanes method of separating NMT/MT traffic roughness of road surface

NMT Speed Model VSk = f (VDESk, VROUGHk, VGRADk) Desired speed (VDES)
Speed limited by road surface roughness (VROUGH) Speed limited by road gradient (VGRAD)

NMT Time and Operating Cost
Components: Time-Related Cost: f(speed) Standing Cost: f(capital cost, average life, utilisation, interest charge) Repair and Maintenance Cost: f(IRI, NMT age and utilisation)

NMT Speeds (km/h)

NMT Time and Operating Costs ($/km)
Pavement Type Effects, IRI = 4 m/km, RF=10 m/km

Impact of NMT on MT MT Speed MT Operating Costs
modelled using the “side friction” or speed reduction factor (XNMT) XNMT value is used in the Free Speed model MT Operating Costs modelled using “acceleration effects” or speed-change cycles (acceleration noise)

Environment Models Environmental effects:
Emissions (hydrocarbons, NOx, ...) Energy balance Noise (forthcoming) Quantities computed but not included on the economic evaluation

Emission Models Developed by VTI in Sweden
Conducted statistical analysis of emissions as function of fuel use Developed simple linear model Model will be changed in future

Emissions Model Estimate quantities of pollutants produced as a function of: Road characteristics Traffic volume/congestion Vehicle technology Fuel consumption Hydrocarbon Carbon monoxide Nitrous oxides Sulphur dioxide Carbon dioxide Particulates Lead

Energy Balance Analysis
Compares total life-cycle energy consumption of different transport policies Three energy use categories: Motorised vehicles Non-motorised vehicles Road construction and maintenance

Energy Analysis Output
Total energy consumption Total consumption of renewable and non-renewable energy Total national and global energy use Specific energy consumption (per km)

HDM Series – Volume 4

HDM-4 Road User Effects Road User Effects.

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