A consistent foundation for province-wide highway system analysis and forecasting is necessary to assess highway usage/deficiencies, service level indicators, and develop traffic forecasts to support transportation planning and infrastructure investment decisions. The level of data requirements and effort associated to develop and maintain a typical 4-step transportation demand forecasting model is challenging for applications in larger geographies. Provincial highway forecasts in Ontario typically carried out on a corridor or a project-specific area and there is a need to advance analysis by determining the impacts on system-wide performance on a consistent and regular basis. The project goal was to develop a province-wide strategic highway traffic forecasting, system analysis and performance measurement tool for Ontario. A wide variety of data were gathered and harmonized, including network data such as auto and commercial traffic volume, highway infrastructure inventory and planned improvements. Demand data such as population, employment, financial and tourism forecasts from provincial and federal sources were also obtained and utilized. Various methods were investigated and employed in order to develop a provincial-level forecasting framework which can be applied to the entire network, or to criteria based subsets. This framework has the capability to carry out systematic province-wide forecasting of highway traffic and corresponding service level indicators, test different growth scenarios for determinants of traffic (commuter, tourism/recreational, commercial), and identify current and future highway system deficiencies and bottlenecks. The end product was to develop a user friendly desktop application to allow for multi-skilled users to produce relevant products including reports in the form of tables, charts, and GIS maps. Although the tool focuses on inter-urban traffic, it is relevant to „best practices‟ in urban transportation planning in two ways: [a] the tool complements and extends urban travel demand models by providing an integrated, holistic treatment of externally-based and through trips; and [b] the tool provides a practical approach for forecast development, using disparate data sources and GIS.
