Traffic Forecasting Irregularities

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

2nd Interna)onal Symposium on Freeway and Tollway Opera)ons Honolulu, Hawaii – June 21-‐24, 2009

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 Korinthos-‐Patra

Motorway AUki Odos

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

Greece

Greater Athens Area 1.5km zone in each side of the motorway axis

Study area: A<ki Odos Motorway


65.2 km in length

Opened in full length : 2004

Toll motorway

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

AUki Odos axis

1.5km zone limit



Case study results

•  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%.

Major Findings


•  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.



•  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

Conclusion


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

Design

Transcript of Traffic Forecasting Irregularities