.
ASSESSING MEASURES WHICH REDUCE INCIDENT RELATED
DELAYS AND TRAVEL TIME VARIABILITY
Sonal Ahuja
Tom van Vuren
Mott MacDonald
Stuart Porter
Department for Transport
John Fearon
John Fearon Consultancy
1.0 INTRODUCTION
Generally, transport planning and forecasting work concentrates on the
representation of average conditions, ignoring the effect of travel time uncertainty
(due to day-to-day variability and incidents) on behaviour and travel conditions.
Traditionally, when assessing the benefits of incident reduction measures, only
delay impacts have been assessed, for example using QUADRO. However,
particularly in congested situations, travel time variability can have an important
effect on behaviour and on benefit calculations. The former was dealt with in a
previous paper presented at the 2001 ETC Gordon et al 2001; the latter is the
subject of a new tool developed for the Department of Transportation (DfT): INCA
(INCident benefit Assessment).
INCA is a spreadsheet-based application, which makes extensive use of a
database of the frequency at which five types of incident occur, their build-up
duration, and their severity, expressed as the number of lanes affected. Incident
variability is represented as variations above expected journey times, and is
calculated as a function of the probability of a journey encountering an incident,
the average delay per vehicle affected and the variance per vehicle affected.
INCA is based on travel time variability research carried out in the UK over a
number of years. It provides a tool to assess the travel time variability impacts of
any measure (including remedial measures) on the highway network. INCA was
developed under a contract sponsored by the (then) DETR HETA Division, by a
team of consultants consisting of Mott MacDonald, Imperial College London, TRL
and David Watling of the University of Leeds. The objective of INCA was to
enhance the user-friendliness of an earlier incident benefit model INCIBEN,
developed by John Fearon Consultancy under a separate contract to DfT.
Most of the initial work on travel reliability in the 1980s and 1990s was carried out
in respect of rail journeys. British Rail analysed the option of trading-off longer
journey times against a greater probability of reaching the destination at the
advertised arrival time. Their methodology evaluated passengers' conflicting
preferences for shorter journey times against their preferences for increased
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probabilities of: arriving on time; and shorter waiting times for late running trains.
However, reliability benefits have not generally been considered in multi-modal or
highway appraisals.
More recent research has concentrated on the assessment of travel time
variability on the highway network due to incidents that affect running
carriageways. DfTcommissioned MVA in 1995 to study whether road schemes
had substantial variability benefits in addition to time saving benefits. The study
indicated that the largest extra benefits arise from widening busy motorways
most of which arose from incidents causing less delay. This has also been
demonstrated in this study. DfT at the same time commissioned parallel research
into the potential problems of representing variability in demand and supply
models reported in Gordon, 2001. Further research has commenced recently into
demand and supply-related variability as described in Bates, 2002 to
complement the Highways Agency’s study of driver’s perceptions of variability
and their valuation of variability and this is described in Copley, 2002.
The methodology discussed in this paper is designed to assess proposals that will
improve journey time reliability and reduce Travel Time Variability (TTV) on
highway links affected by incidents that reduce their capacity. It is also applicable
where proposals may increase TTV - for example, extensive highway capacity
reallocations, such as bus lanes.
Travel time variability arises from two sources:
i)
ii)
Variations in journey time between different time periods, days and
seasons, caused by different traffic conditions, known and anticipated
factors such as long term roadworks; and
Variations of travel time at a specific time of day caused by unexpected
traffic congestion and unforeseen incidents, which reduce capacity, such
as unexpected worsening weather conditions, accidents and breakdowns.
Variations in travel time as classified under (i) are predictable. They are currently
measured in transport benefit assessment frameworks, such as COBA, which
estimate travel costs separately for each period. Variations in travel time as
classified under (ii) are unpredictable for individual journeys, although their
means and distributions can be predicted. They are not currently measured in
transport economic evaluation. This paper concentrates on the consequences of
affecting this unpredictable element of journey times.
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2.0 METHODOLOGY
The theoretical basis for evaluating travel time variability involves two elements:
1. The measure used to quantify travel time variability (TTV); and
2. The value of TTV benefits.
The value of TTV benefits needs to take into account whether different levels of
delay occurs and whether less allowance needs to be made for delays for the
same likelihood of late arrival. Both aspects (i.e. delay and schedule savings)
have a value applicable to all modes of travel.
Travel time variability benefits are therefore linked to scheduling constraints
and/or reductions in the standard deviation of journey times, and in both case
calculations cannot be solely link-additive: whole journey impacts must be
calculated. Those calculations need to be sophisticated enough to assess the
impact of alterations to the road network whilst also an allowance for the impact
of day-to-day variability must be incorporated.
Various occurrences generate unexpected travel time variability (TTV) when they
cause delays by completely or partially blocking the running carriageway. If they
cause a significant loss of capacity they are referred to as incidents. Where a
blockage reduces the capacity below the demand flow, queuing and delays will
occur. If the number of incidents or the severity of delays caused by them can be
reduced benefits arise from:
a) A reduction in delay due to incidents; and
b) A reduction in unexpected TTV caused by incidents.
Thus in most cases, any benefits arising from reduced incident delay need to be
calculated in addition to incident TTV benefits. The effect of an incident relates
to:
i)
ii)
iii)
iv)
v)
Its rate of occurrence;
The total flow of vehicles using that carriageway;
The numbers of lanes available; and blocked; and the capacity reduction
of the remaining lanes;
The “build up duration” of the incident, that is the length of time between
the incident occurring and the carriageway being cleared;
The amount of traffic diverting in response to the incident.
Analysis of data from studies undertaken by TRL has shown that incidents can
be categorised into five groups each of which have characteristically different
rates of occurrence, lane blockage effects and build up duration on the
motorways. These categories and their relative effects are as follows:
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a) Single Lane Accident - An accident which blocks only one lane of the running
carriageway. (These have medium average build up duration and a low
carriageway blockage effect.);
b) Multi-Lane Accident - An accident which blocks more than one lane of the
running carriageway for part of its duration. (These are relatively infrequent.
They have relatively long average build up duration and a medium
carriageway blockage effect.);
c) Breakdown - These are incidents where a vehicle suffers a mechanical or
other failure and obstructs the running carriageway. (These are by far the
most common types of incident on roads with no hard shoulder or hard strip,
but their effect is greatly reduced by the presence of a hard shoulder or hard
strip. They have relatively short build up duration unless an accident results
and they block fewest lanes);
d) Minor Debris - These are incidents where the running carriageway is
obstructed by debris generally from vehicles, for example, tarpaulins, ropes
and tyres. (These incidents have the shortest build up durations and a
relatively low carriageway obstruction effect); and
e) Major Fire, Load Shed, Spillage - These incidents are vehicle fires, the
shedding of a goods vehicle's load or spillage on the running carriageway
mainly of chemical or fuel products. (These incidents are the least frequent,
but have very long build up durations and the highest carriageway obstruction
effect).
Additional incidents are known to arise from (f) unexpected roadworks, or (g)
unexpected very adverse weather conditions, or (h) slow moving agricultural
tractors or wide loads obstructing the carriageway. The first two of these extra
incident categories have not been included in the incident TTV methodology and
the third currently needs to be handled manually. We expect further research
work to be carried out to enhance the segmentation of incidents and their
numerical characteristics.
The main steps in the calculation of incident delay and the travel time variability it
causes are as follows:
a)
b)
c)
d)
Select appropriate parameters which define the effects of incidents using
robust local data or the appropriate default values;
Calculate the delay and travel time variability caused by a series of typical
individual incidents and the probability of encountering each of those
incidents;
Calculate the TTV arising from the expected number of incidents for the
range of journeys on the series of links they traverse: and
Allow for other sources of variability.
It is necessary to undertake the above calculations for both the do minimum and
do something cases.
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Flows vary by time of day, day of the week and season. As incident delay is a
function of flow it is necessary to estimate delays for each of the different flow
conditions.
Some traffic management measures will affect only one link. However, for larger
schemes more than one link may be directly affected, while other links may be
affected by reassigned traffic, for example. In this case the incident delays for all
inter-urban links affected by the scheme should be calculated.
Where queues from an incident tail back beyond an upstream junction some
drivers may divert to alternative routes. The delays which occur are dependent
on the proportion of traffic diverting to avoid the incident, which in turn is
dependent upon the overall duration of the incident, the length of the backed up
queue and the availability of alternative routes plus reliable information.
A time/distance diagram representing typical vehicle paths passing an incident is
provided in Figure 1 to illustrate the key features and the traffic delay
consequences of an incident which reduces the carriageway capacity below the
demand flow. The mechanism by which such an incident delays vehicles is as
follows:
i)
Build-up Period (B) - This is the period for which the incident blocks part of
the running carriageway thereby reducing capacity. The back of the
queue builds up upstream of the incident due to the demand flow (F)
exceeding the reduced capacity (C’); and
ii)
Queue Decline Period (D’)- The effective period in which the queue
disperses after the incident is cleared from the running carriageway. As
the build-up of demand in the queue can only clear at the carriageway
capacity (C) this decline period may be longer than the build-up period.
During this period the front of the queue moves back upstream. (It is
subdivided into: a transition period (T) - the time from the incident being
cleared to the last vehicle which would have arrived during the build up
period passing the point where the incident had occurred; and a clearance
period (D’ - T), the time from the end of the transition period to the queue
clearing completely.)
To calculate the delays caused by an incident it is necessary to make a series of
related simplifying assumptions such as the following:
a)
b)
c)
The demand flow is constant for the whole of the build-up and queue
decline periods;
The incident delays are measured relative to the time at which a vehicle
would have passed the incident location;
The modelled incident delay per vehicle should not exceed a locally
determined maximum;
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d)
e)
Incidents are assumed to have no effect beyond the end of the link; and
Modelled demand should reflect that outflow averaged over a flow group
will not exceed a specific proportion of capacity.
Figure 1: A time/distance diagram showing the build-up and decline of a
queue caused by an incident
Note 1 - For a full definition of terms including the gradients shown above see ‘Ref. 18’
Note 2 - The back and front of queue is shown as a smooth line, which represents the mean queue length.
Actual queuing behaviour would involve oscillations about that line.
Note 3 - The trajectories of vehicles which lie at the boundary
of the various period are shown thus
Their intended trajectories are shown thus
Note 4 - The trajectories of vehicles which are
not delayed are shown thus
Note 5 - The trajectories of vehicles which are delayed are shown thus
These assumptions are compatible with assumptions made in COBA and
QUADRO. They are likely to have more effect for incidents which cause long
delays. To limit the effect of assumption (a) demand flow is split into flow groups
and where they represent relatively short time periods, longer incidents may spill
over into an adjacent flow group. Fortunately, for all but the busiest flow groups,
spill over effects between flow groups are likely to have no net effect on the
likelihood of any individual vehicle within each flow group being affected by an
incident. In the absence of an explicit spill-over mechanism and for the busiest
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flow groups an approximation for the remaining spill over effects is represented in
INCA by restricting their demand flows to 95% of capacity and the use of
assumption (c).
Where tailbacks from an incident reach an upstream junction some drivers may
divert to alternative routes. The total amount of this diversion will depend on:
a)
b)
c)
d)
the availability and attractiveness of alternative routes;
the flow on the main carriageway and the proportion of capacity reduction;
the duration of the incident; and
the average or maximum delay per vehicle caused by the incident
Within INCA there are two ways in which the maximum diversion level can be
set. Manually, based on the analyst’s own local knowledge or by using the DIVA
diversion calculation (discussed later in section 3). Whichever method is used,
the basic diversion mechanism modelled by INCA is the same:
•
•
•
•
The maximum diversion level is assumed to occur at the maximum flow level
on the main carriageway, which is taken as 95% of the road’s capacity. The
modelled diversion level is reduced from that value when the demand flow is
less than the maximum demand, eventually reaching zero where the
remaining capacity with the incident exactly matches the demand flow.
The resulting delays experienced by diverting traffic are assumed to be equal
to those calculated for the traffic remaining on the main carriageway, which is
affected by the capacity reduction directly.
An implicit diversionary model is also incorporated by capping any modelled
excessive average delays, or reducing their effect above a specified
threshold. The default value is capping delays at 30 minutes.
Usually, maximum diversion rates (user-set or DIVA-calculated) are the same
for do-minimum and do-something cases. However, if the scheme under
consideration is expected to affect diversion rates (for example a Variable
Message Sign system) then the maximum diversion rate may differ in the
before and after situation (although the delay cap and threshold factors
should remain fixed).
Travel Time Variability for Journeys
The detailed calculations of delays and travel time variability are discussed in the
‘INCA 1.0 Manual’. The basis for delay calculation is the link level – and as
delays for journeys are a simple summation of delays over links used in the
journey the calculation of journey delays is straightforward.
The calculation of travel time variability at journey level is more involved and this
is discussed next. First, we need to identify all variability components affecting a
journey:
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•
•
•
Incident variability on the scheme links, i.e. affected by the measure under
consideration);
incident variability on all other links, i.e. unaffected by the measure;
day-to-day variability on all links, i.e. variability not caused by incidents but by
e.g. fluctuations in demand, weather etc. In INCA, pending surveyed data, it is
assumed that the day-to-day variability variance is the same as the incident
variance in the do-minimum case.
Only some of the overall variability components are reduced by the scheme,
others remain, as shown in Figure 2. Variability is expressed as the standard
deviation of travel time. Hence, the effectiveness of the scheme, in terms of TTV
reductions, depends on all TTV components for the journey:
• the greater the (residual) day-to-day variability, the less the impacts of the
scheme on TTV and hence the smaller the benefits.
• the greater the contribution of variability on non-scheme links (for example,
for longer journeys), the smaller the TTV benefits.
• the greater the contribution of incident types not affected by the scheme, the
smaller the TTV benefits.
The above shows that to calculate TTV the standard deviation of travel time for
whole journeys affected by the scheme under consideration needs to be
established. This is done as follows for a series of typical origin to destination
journeys:
i)
ii)
Calculate the variance of travel time for a typical single incident of each
type for each individual link for each flow group; multiply that by the
appropriate probability and
From (i) calculate the variance and standard deviation of travel for typical
journeys whose route is via one or more links affected by the scheme.
Variance should take precedence over standard deviation, as variances per
vehicle may be summed over a series of links and incident types, where the
probability of incidents on one link/type is independent of the probability on other
links/types. Standard deviations may not be summed in that way.
As the overall journey variability is a function of all variance components,
variability on links used to reach the scheme also needs to be allowed for. This is
achieved by modelling typical feeder links in addition to the links directly affected.
The variance for each origin to destination journey can then be calculated by
summing the variances per vehicle over all links traversed. The total travel time
standard deviation for that particular origin to destination journey (by flow group)
can then be calculated for the do minimum and do something scenarios as the
square root of the summed variances (i.e. ignoring correlation in travel times).
The difference between the standard deviations produces the TTV saving.
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The travel time standard deviation will be a function of the total travel time for
each journey. Changes in the standard deviation will in general be higher for
shorter (time) trips than for longer (time) trips. Hence, a matrix of typical journeys
should be used to represent the range of trip lengths, trip purposes and vehicle
types affected by the scheme. As incident delays are dependent on link flows
and TTV on origin to destination flows, for smaller schemes it is acceptable to
give more attention to the accuracy of the link flows and use quite a small matrix
of representative movements.
without scheme
with scheme
change INCA parameters at
link level to reflect impact of
scheme on e.g.
- incident probability
- incident duration
- road capacity
- maximum diversion level
define network, incl. flows,
define flow group distribution
apply INCA with default or
local parameters
calculate variance of travel time
- for each incident type
- for each link
- for each flow group
calculate variance of travel time
- for each incident type
- for each link
- for each flow group
multiply the variances by
probability per incident
multiply the variances by
probability per incident
sum over all incidents
sum over all incidents
calculate variance of travel time
per journey in the trip table
calculate variance of travel time
per journey in the trip table
add day-to-day variance = domin incident variance
add day-to-day variance = domin incident variance
calculate travel time variability
(TTV) and costs using
appropriate VOT
calculate travel time variability
(TTV) and costs using
appropriate VOT
sum over all journeys and flow
groups
sum over all journeys and flow
groups
'before' TTV costs
'after' TTV costs
TTV benefits
Figure 2: Components of TTV benefit calculations
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3.0 DIVA – INCA’S DIVERSION MECHANISM
Any diversion has a major impact on the delays and travel time variability
experienced following an incident and is modelled in DIVA, which aims to enable
the user to provide a more realistic representation of alternative routes and the
use made of them in case of an incident.
The general diversion method in INCA is shown in Figure 3 and relies on:
• the definition of a maximum diversion rate
• calculation of incident capacity for each link
• calculation of an estimated diversion rate varying with the actual link flow in
the time period assessed, based on an assumed linear increase between 0
and the maximum diversion rate as a function of flow
maximum
(user defined)
DIVERSION
RATE
0
incident
capacity
FLOW
95% of full
link capacity
Figure 3: Basic diversion mechanism
This simple model can be varied between links by inputting different user defined
maximum diversion rates for each link. Rather than the analyst estimating the
maximum diversion rate, DIVA within INCA can calculate this maximum rate for a
selected link. It is currently an experimental routine, which is limited to a single
link and calculates the diversion rate for the link as a function of:
• the availability of an alternative route
• its key characteristics (length, capacity, speed-flow relationship, existing
demand)
• incident characteristics (duration, capacity reduction, etc)
• capacity of the affected link
• flow level on the main road
Operationally a method has been developed that calculates
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•
•
•
the equilibrium diversion rate in the current INCA application (for each single
incident type separately), driven by existing flow levels;
the amount of delay at which diversion starts saving time
and the equivalent maximum diversion rate applicable to the extreme point on
the curve when demand reaches 95% of the non-incident capacity.
The basis of the approach is similar to QUADRO: in DIVA an alternative route is
defined for a selected link, and when an incident occurs traffic will spread across
both routes until an equilibrium is reached. At that equilibrium, travel time via the
main route (including incident delays) equals travel time via the alternative
(unless the capacity of the diversion route is exceeded)1. To calculate the
equivalent maximum diversion rate, a mechanism is employed which increases
an assumed maximum diversion rate incrementally from 0 until
• either an equilibrium over both routes is reached in the busiest flow group
• or the alternative route capacity is exceeded.
The calculations are carried out for the busiest flow group only, which is the one
where the highest diversion rate would have greatest effect. The calculations use
the input flow multiplier (which implies a chosen year) and the same maximum
diversion rate is input into the diversion model for the other flow groups. This
produces a hierarchy of actual diversion rates, which usually differ per flow
group. The calculations are carried out for each incident type separately as these
differ in their build-up duration and their impacts on capacity – hence, they will
differ in the need for and attractiveness of diverting, even at equivalent basic flow
levels.
4.0 THE INCA PROGRAM
INCA (INcident Cost-benefit Analysis) is a spreadsheet program. The program is
based on the original research by John Fearon Consultancy, who developed the
precursor INCIBEN. INCA differs from INCIBEN in two important respects: an
improved user interface and an enhanced diversion mechanism (DIVA). INCA is
based on 4 input sheets and a results sheet; they operate as in Figure 4.
5.0 PROGRAM APPLICATIONS
INCA has been used by 5 consultancies in the UK in the assessment of Active
Traffic Management (ATM) of a Motorway, Motorway widening and for accident
remedial measures. It has proven to be a flexible tool to assess travel time
variability impacts of such schemes. Here we provide 3 examples of the
application of the INCA methodology for hypothetical proposals affecting busy
inter-urban roads. It describes how incident delay and TTV benefit results could
be obtained by using the INCA spreadsheet, and by modifying certain of the key
input parameters, which are:
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•
•
•
•
•
incident rate (e.g. safety-improving measures, and refuge facilities)
incident duration (e.g. measures which reduce the time taken for emergency
or recovery vehicles to arrive)
number of lanes blocked (e.g. traffic management measures)
capacity reduction factor (e.g. traffic management measures)
maximum diversion percentage (perhaps recalculated through DIVA, or
manually overwritten to account for information provision etc).
In principle, any measure or policy affecting the occurrence or severity of incidents
could be represented through appropriate changes in these 5 parameters.
INCA Program Flowchart
Start
Primary Input Sheet
Is
Diversion
Mechanism
(DIVA)
Applied
YES
Enter: Forecast Year Factors,
Base Year Flow Data
Flow Group Definition
Diversion
Mechanism Sheet
Secondary Input Sheet
Enter: Incident Definition Parameters,
Specify Type of Incident in Operation
Enter: Incident Rates, Incident Duration etc
Define Diversion Links,
their Speed Flow Char.
NO
Diversion Calculations
Find Flow where Equilibrium Occurs
Calculate Diversions for Incidents
Trip Matrix VOT Input Sheet
Define Movements and Movement Flows,
Vehicle and Trip Purpose & Proportions
enter Values of Time for Delay and TTV
Internal Process:
Delay & Travel Time Variability Calculations
Calculation of Benefits
Internal Process:
Delay and Travel Time
Variability Calculations
Calculation of Benefits
Final Results Sheet
Output Results: Display Benefits,
Generate Reports
Stop
Figure 4: INCA Program Flowchart
Accident Reduction Measures, New Hard Strips or Extra Vehicle Refuge
Facilities
Accident reduction measures aim to reduce the frequency or severity of
accidents. They will have a direct impact on incident rates which should be
adjusted for the link in question in the ‘Do Something’ scenario relative to those
used in ‘Do Minimum’. Appropriate reduction values should be obtained from
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monitoring studies. Figure 5 below shows the benefits that INCA has calculated
of accident reduction measures normalised to a single accident reduction per
year for three different road types:
a) Dual two lane all purpose road with 100% hard shoulder (D2AP 100% HS)
b) Dual two lane all purpose road with 50% hard shoulder and 10% Diversion
Rate (D2AP 50%HS 10% Diversion).
c) Dual three lane motorway (D3M)
The graph shows the increased value of benefits with decreasing road capacity
(All), the damping impact of diversion (A compared with B), and the relative sizes
of delay and TTV benefits (A compared with D).
Fig. 1 Benefits in £ per year at 1994 prices of reduction by 1
accident
£60,000
D3M
Benefits (£ per year at 1994prices)
£50,000
A
D2AP100%Hard
Shoulder
£40,000
B
D2AP50%Hard
Shoulder 10%Diversion
£30,000
`
C
£20,000
D2AP100%Hard
Shoulder Delay Only
Benefit
D
£10,000
£0
0
5000
10000
15000
20000
25000
30000
Traffic Flow (Vehicles/Day/Lane)
Figure 5: Benefits of accident remedial measures on different road types
The proportion of travel time variability benefits to total benefits derived by reducing
one accident on different road types is given in Figure 6. This increases as road
quality decreases from less than half to in excess of 80% (on modern single lane
carriageways with 50% hard strip and 20% diversion rate: S2 50%HS 20%
Diversion). This illustrates the importance of incorporating TTV considerations in
overall benefit calculations. In Figure 6, the TTV benefits derived by accident
remedial measures on a dual two lane all purpose road with 50% hard shoulder
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(D2AP 50% HS), are slightly less than those derived on dual two lane all purpose
road with 100% hard shoulder (D2AP 100% HS) due to the effect of 10% traffic
diverting on the D2AP 50% HS road.
% benefits from TTV for different road types at 95% capacity
90.00%
80.00%
70.00%
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
0.00%
D3M
D2AP100%HS
D2AP50%HS10%
Diversion
S2 50%HS 20%Diverson
Figure 6: Proportion of travel time variability benefits to total benefits
derived by reducing one accident
£30,000
Total benefits obtained / kilometer of road section by
reducing 1 accident (@ traffic flow = 23,333 vehicles/hr)
D3M Delay Only Benefit
£25,000
D3MBusy Feeders
D3M MixedFeeder
D3MQuietFeeder
£20,000
£15,000
£10,000
£5,000
£0
D3M road type with varying
1 flow levels on feeders
Figure 7: Effect of changing flow levels on feeders on benefits derived by
reducing one accident per kilometre of D3M type road.
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The relative flow levels on feeders affects the travel time variability benefits, with
quiet feeders leading to increased benefits as shown in Figure 7, for reasons
described in section 2.0. As with varying flow levels on feeders in the scheme the
delay benefits remain constant, the TTV only benefits increase, even more
prominently as the flow levels on feeder links decreases.
Incident Duration Reduction Measures
CCTV surveillance and/or dedicated recovery services on instant call will reduce
the duration of incidents either directly or indirectly. For example CCTV
surveillance provides opportunities to increase the percentage of vehicles
diverting during an incident (and its aftermath), thus reducing the maximum
queue of vehicles and the resultant queue decline duration. Similarly measures
to increase the capacity of key bottlenecks on diversion routes can have an
equivalent effect. Their appraisal requires a series of sensitivity tests using
different diversion rates, perhaps calculated using DIVA.
Measures such as the provision of radio information and Variable Message Signs
(VMS) will reduce the duration of incidents, by encouraging some drivers to divert
over a number of routes.
A hypothetical example calculation of the effect of a dedicated recovery vehicle is
given below, using assumptions about the expected duration reductions
resulting, and as input into the INCA spreadsheet.
Incident Type
Single Lane
Accident
Multi Lane Accident
Breakdown
Minor Debris
Major Fire
19%
Assumed duration
reduction
-22%
% of TTV
benefit
34%
4%
34%
42%
1%
-12%
-15%
0
0
32%
34%
0
0
% of incidents
(all values based on INCA benefit calculations reductions in the Root Mean Square of the build-up duration)
Table 1: Incident duration and travel time variability benefit
The example described assumes that recovery vehicles reduce the duration of 3
incident types by 12%, 15% and 22% respectively and results in approximately
equal benefits attributable to each type of incident. This implies that the most
critical assumption relates to the duration saving for the most severe (but least
frequent) of the 3 incident types - multi lane accidents.
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Running Carriageway Widening or Capacity Reallocation
Widening of the running carriageway will reduce the proportion of capacity lost
when incidents occur. This will reduce the total delay for each incident and hence
reduce the incident related TTV. Changing the link capacity and number of
running lanes in the do something scenario should enable us to assess the effect
of this type of scheme (Figure 8).
% of TTV benefits derived by converting 1 km of
dual 2 lanes all purpose road to dual 3 lane
motorway (Total annual benefit = £5.57 million)
Total Annual
TTV Benefit
41%
59%
Total Annual
Delay Benefit
Figure 8: TTV benefits obtained by road widening.
Capacity reallocation to buses, coaches and taxis or high occupancy vehicles
(HOVs) will give priority to those classes of vehicles, but could lengthen the time
required for any incident to clear and hence increase the incident related TTV for
some and perhaps all categories of traveller. Reducing the link capacity and
number of running lanes in the do something scenario should enable us to
assess the effect of this type of scheme.
8.0 SUMMARY
INCA is based on travel time variability research carried out in the UK over a
number of years. It is a tool to assess the travel time variability impacts of a
measure on the highway network. The methodology is designed to assess
proposals that improve journey time reliability and reduce Travel Time Variability
(TTV) on highway links affected by incidents that reduce their capacity.
The above examples illustrate how general or incident-related parameters can be
adjusted in an INCA application to reflect the impacts of a measure, and to
calculate the delay and travel time variability benefits. They illustrate the relative
size of travel time variability related benefits, as a function of the type of scheme
and the traffic situation under consideration. INCA is being used by a number of
© Association for European Transport 2002
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organisations for a variety of trunk roads and motorway schemes, including ATM.
For more information, plus a copy of the software and manual, please contact the
first author.
REFERENCES
Bates J., Black I., Fearon J., Gilliam C. and Porter S. (2002) Supply models for
use in modelling the variability of journey times on the highway network, to be
presented at AET European Transport Conference, Cambridge 2002.
Copley G., Murphy P. and Pearce D. (2002) Understanding and Valuing Journey
Time Variability, to be presented at AET European Transport Conference,
Cambridge 2002.
Dale, Porter S, and Wright (1996) Are there quantifiable benefits from reducing
the variability of travel times?, Proceedings of the PTRC European Transport
Forum Seminar E, Transport Planning Methods, Cambridge 1996.
DETR (1999) The Appraisal of Measures which aim to reduce Travel Time
Variability – (TAMTTV). HETA Division DETR, Draft for 1st Tranche of Schemes,
London.
Gordon A, van Vuren T, Watling D, Polak J, Noland R. B., Porter S. and Taylor N
(2001) Incorporating variable travel time effects into route choice models,
Proceedings of European Transport Conference, Cambridge, Seminar on
Methodological Innovations, Cambridge 2001.
Noland, R. B. and POLAK J. (2001) Travel time variability: A review of theoretical
and empirical issues, Transport Reviews, 22(1), 39-54.
MVA, (1996) Benefits of Reduced Travel Time Variability.
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Notes
1
The concept of equilibrium when an incident occurs may be questioned – under such circumstances
knowledge of the travel conditions on alternative routes, and even the main road itself, will be limited and
error-prone. However, a spread across alternative routes is expected and the equilibrium assumption leads
to a defined and tractable solution of the proportion that reroutes. Until further research becomes available
on rerouting behaviour at an incident, and techniques are developed to represent such behaviour in our
models, we believe that the equilibrium assumption is defensible.
© Association for European Transport 2002