UIP recognises the importance of demand assessments and forecasting when sizing infrastructure, carry out economic and financial assessments, testing different scenarios and packages improvements, developing strong business cases and establishing the impact of disrupters such as new modes, technologies and policies.
Demand assessments are carried out at different levels and focused on different modes as such:
- Regional and National demand assessments for Rail, Road, Air, Sea and Inland Waterways for Freight and Passenger movements
- including High Speed Rail and Road Tolls
- City/Urban multi modal models to support urban master-planning
- including metro/light rail, Bus Rapid Transit (BRT)
- Sub Area traffic models to size road networks and carry out Traffic Impact Studies (TIAs)
- Integrated Hub studies for Rail/Coach/Logistics and Air facilities
- Micro Simulation of stations, hubs and local areas
- including pedestrian and vehicles
UIP regularly utilize transport models as a tool to develop comprehensive transport planning solutions which are both optimal and feasible. We also understand that the quality of any model is dependent on the knowledge and understanding of the modelling team as well as the quality of the data available. We recognize that any traffic model is only as credible as the modellers using it and as such pay high regard to the training of our staffs. As the majority of our modellers are from an engineering or planning background, we use traffic modelling along with our engineering/planning judgment to obtain high quality for our clients.
UIP is aware that traffic modelling projects often involve several modelling levels and/or software platforms. This can sometimes lead to issues without the expert’s capability.
Understanding the strengths and weaknesses of each software and
how to manage data transfer between modelling levels/suites is
critical to the usefulness of the outputs. Our modelling team have
considerable experience on projects where several modelling levels
have been employed, and therefore have a good understanding of
managing information transfer between models appropriately
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