The application of macroscopic travel demand models to quantify traffic operational performance
measures, such as delay, queues, level of service, and corridor travel time has some significant
limitations. Due to the lack of temporal variation of traffic flow in Static Traffic Assignment
(STA) and allowance of demand over capacity in macroscopic travel demand models, the validity
and reliability of traffic diversion estimate from major road/bridge closures are often subject to
question. Dynamic Traffic Assignment (DTA), on the other hand, is a new and evolving
technique which is sensitive to time dependent congestion phenomenon and thus can properly
estimate traffic diversion to alternate routes during temporal/spatial traffic flow shifts induced by
network supply or traffic demand changes.
In summer 2013, the City of Edmonton closed the Stony Plain Road Bridge crossing over Groat
Road for four months as part of its roadway rehabilitation program. In order to estimate traffic
diversion and evaluate network traffic impacts during the construction period, a DTA model was
developed using the Dynameq program. Unlike most models where both the calibration and
validation data is collected from the same traffic condition, this model utilized the bridge open
(pre-construction) traffic data for model calibration, and the bridge closure (during-construction)
data for model validation. Additionally, since traffic demand before and during the short-term
bridge closure will likely be the same, the assessment of the model forecasting capability can be
considered more credible.
This paper presents the DTA model development and traffic impact evaluation process, which
covers data collection and analysis, traffic origin-destination demand adjustment, the DTA model
network preparation, as well as model calibration and validation using the traffic conditions
observed before and during the Stony Plain Road Bridge closure.
It is expected that the findings and lessons learned from this study will provide practitioners the
understandings and benefits of a DTA model in the application of traffic operational analysis.
Recommendations on how to apply a calibrated DTA model to a short-term network supply
change are also highlighted.
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