Aberdeen is a vibrant Scottish city with a unique position as a centre leading the way in energy production, research and development. The transport trends in general in the city area have matched the economic performance of the city and the region as a whole with a high standard of living and high car ownership. Over the last decade, major transport interventions have been proposed, the City Centre networks modelled and future year assessment undertaken. The City Council have invested in modelling transport trends, in the short and longer term, to ensure value for money from investment. However, sight has never been lost of actual travel behaviour and the need to re-state trends at regular intervals. Traffic modelling is a representation of potential effects but rebasing to take account of the effects of external real world influences gives confidence in the current methods employed to support infrastructure investment decisions. This paper hopes to explain general transport trends but to then set them in the context of base data from an actual city and how those trends have been used to bring forward the various proposed infrastructure interventions to support background and future needs for transport networks. The paper provides some background to the traffic and economic changes in Aberdeen over the last 10 years and details some of the key interventions which have been implemented during this period. The Aberdeen and Aberdeenshire trends forecast to 2035 is then provided along with the key interventions proposed for the next 10 years to cater for the predicted transport demand changes. The traffic models used for assessing future traffic trends and interventions in Aberdeen are detailed along with a comparison of the survey data sets collated at pertinent intervals over the last 15 years. Finally, a comparison is made between model growth predictions and the actual growth in traffic within the Aberdeen City Centre area. Conclusions are made on the requirements for continual rebasing of models and the lessons learned from historical traffic growth predictions. (Author/publisher)
Abstract