Traffic Forecasting Irregularities

2nd
Interna)onal
Symposium
on
Freeway
and
Tollway
Opera)ons

Honolulu,
Hawaii
–
June
21-‐24,
2009

Irregulari)es
in
the
output
of
transport

planning
models’
forecasts
for
capital

infrastructure
planning
decisions

C.
Antoniou1,
B.
Psarianos1,
and
W.
Brilon2

1
Na)onal
Technical
University
of
Athens,
Greece

2
Ruhr-‐University
Bochum,
Germany

2nd
Interna)onal
Symposium
on
Freeway
and
Tollway
Opera)ons

Honolulu,
Hawaii
–
June
21-‐24,
2009

Errors
in

Transporta)on
Planning
Processes
Regarding
Large

Infrastructure
Projects

C.
Antoniou1,
B.
Psarianos1,
and
W.
Brilon2

1
Na)onal
Technical
University
of
Athens,
Greece

2
Ruhr-‐University
Bochum,
Germany

2 Transporation Planning and Large Infrastructure Projects

Outline

•  Mo)va)on
and
objec)ve

•  Background
and
evidence
from
the
literature

•  Applica)on
in
AUki
Odos
Motorway
(Athens,

Greece)

•  Findings
and
conclusion


Mo+va+on

Motorway
AUki
Odos

Motorway
Korinthos-‐Patra


Experiences

Source: Halkias, B., Tyrogianni; H.:
PPP Projects in Greece: The Case of Attika Tollway, Route-Roads No. 342, 2008


Ques+ons

•  Why have the traffic forecasts been so wrong?
•  What can we learn from existing experience for
future projects?


Ex
ante
-‐
projec+ons


Ex
post
-‐
studies

•  Parthasarathi, Levinson (2009):
•  62 % od forecasts were wrong
•  Underestimating highway traffic
•  Noland (2001):
•  Wrong or missing effect of induced traffic
•  Flybjerg e.a. (2006):
•  Railway projects: 72 % overestimated
•  Highway projects: 25 % of cases error > 40 %


%
errors
in
traﬃc
forecasts
(Flybjerg
et
al.,
2006)

underes)ma)on
overes)ma)on


Ex
post
-‐
studies

•  Sammer (2006):
•  Inadequate planning methods
(practice = state of the art)
•  Models are sometimes not understood sufficiently by
planners
•  Insufficient calibration of parameters and lack of
validation
•  Point estimation ↔ interval estimation (reliability)
•  Wegener & Fuerst (1999):
•  Impact of infrastructure on land use


Study
area:
A<ki
Odos
Motorway

65.2 km in length Greece

Opened in full length : 2004
Toll motorway

Greater
Athens
Area

1.5km
zone
in
each
side

of
the
motorway
axis


Study
area:
A<ki
Odos
Motorway

Traffic studies in advance of the project:
•  Rather rough analytical data background
•  Conventional transport planning methodologies
•  Studies were more directed
on predicting the toll income
than on really expected traffic
•  Neglecting of induced traffic


Induced
Traﬃc

= Traffic which has not been there before the
implementation of the new infrastructure
1. kind:
traffic generated by the existing land use;
the same origins but more distant destinations

2. kind
traffic generated by new land use,
which is induced by the new infrastructure


Induced
Traﬃc

= Traffic which has not been there before the
implementation of the new infrastructure
1. kind:
well treated by adequate transportation traffic
modeling

2. kind
open question


Case
study:
A<ki
Odos

Determination of modified land use
before and after the opening of Attiki Odos Motorway
•  Areal photographs
•  Interviews

Estimation of the number of households within a margin of 1,5
km around the motorway


Case
study:
A<ki
Odos

AUki
Odos
axis

1.5km
zone
limit


Major
Findings

•  Areas
that
were
already
well-‐built-‐up
prior
to
the

opera)on
of
AUki
Odos
motorway
show
more

moderate
growth
rates

(s)ll
around
or
even
exceeding
20%
annually

for
a
period
of
8
consecu)ve
years)

•  Areas
that
were
less
developed
and
had
more
room

for
growth
show
annual
growth
rates
exceeding

50%.


Case
study:
A<ki
Odos

•  For
2000,
the
number
of
households
in
the
influence

zone
in
2000
was
83.802

•  For
2008
the
number
of
households
in
the
influence

zone
(of
1.5km)
grew
up
to

141.038
households.

•  An
annual
increase
in
households
equal
to
8.5%
was

therefore
computed
for
the
en)re
influence
zone.


Conclusion

•  Considera)on
of
induced
traﬃc

-‐1st
and
2nd
kind

is
an
indispensable
element

of
transporta)on
planning

for
large
infrastructure
projects

•  Tradi)onal
aggregated
modeling
frameworks

are
not
longer
a
useful
basis

for
traﬃc
predic)on

on
large
infrastructure
projects

–  Ac)vity
based
modeling

–  Dealing
with
uncertainty


Thank
you
for
your
aGen+on
!


Mo)va)on

•  Traffic
forecasts
are
oaen
underesImaIng

traffic
demand
compared
to
the
actual
traffic
counts

•  An
explana)on
for
the
underes)ma)on
can
be

abributed
to
the
non-‐incorpora)on
of
induced
traffic

into
the
model
forecas)ng

•  The
gap
between
state-‐of-‐the-‐art
theory
and
pracIce

appears
to
be
widening

•  Problems
include:

–  “black-‐box”
effect

–  lible
or
no
evidence
for
the
validity
of
the
input
data
used

or
how
the
model
was
calibrated

–  the
results
are
presented
as
point
esImates,
rather
than

interval
es)mates
based
on
a
probability
func)on,
with

confidence
limits
specified

Objec)ve

•  Provide
insight
into
the
problem

•  Through
analysis
of
one
of
the
aspects

–  Lack
of
adequate
modeling
of
induced
demand

•  By
an
applica)on
in
a
recently
developed

tollway

–  AUki
Odos
Tollway
(Athens,
Greece)

Stated
causes
of
inaccuracy

(source:
Flybgjerg
et
al.,
2006)

Case
study:
Overall
methodology

Main
assump)ons

•  Several
assump)ons
are
required

–  Data
may
be
limited,
unreliable
and/or
difficult
to

obtain

•  Conserva)ve
assump)ons
were
made
that
would

lead
to
an
underes)ma)on
of
the
impacts,
for

example

–  For
a
large
part
of
the
influence
zone
(more
than
half),

in
which
the
changes
were
not
drama)c,
the
number

of
households
was
held
constant

–  For
the
influence
zone,
the
number
of
households
per

building
was
assumed
to
be
constant
(for
the

computa)on
of
the
change
in
the
number
of

households).

Traffic Forecasting Irregularities

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