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Abstract: Recent advancements in computing and geographic information systems (GIS) have revolutionized the development of planning support systems (PSSs) to study and simulate the future of travel demand in urban areas. While travel demand models have been widely developed and used to inform the urban planning process, very little has been done to explicitly account for demographic changes and the ageing of population within such models. The work in this paper bridges this gap by describing the development and application of IMPACT (integrated model for population ageing consequences on transportation), a GIS-based PSS capable of assessing the ramifications of demographic changes, including population ageing, on the performance and usage of urban transportation systems. Unlike existing operational PSSs, IMPACT possesses unique and novel features that enable it to model demographic changes and urban travel demand for various age cohorts. Through the simulation of scenarios for varying fertility rates, the paper explores and quantifies the impacts of ageing and population age-structure changes on travel patterns in the census metropolitan area (CMA) of Hamilton in Ontario, Canada. The analysis suggests a consistent increase in the number of trips and mobility levels of elderly drivers, as a result of population ageing over time. Furthermore, increases in fertility rates could significantly impact the usage and performance of the transportation system for certain periods of the day, by the year 2031. Results such as these are important to consider when planning the future of cities. The findings in this paper provide new insights into the future of urban travel in the Canadian context. [Copyright &y& Elsevier]

Abstract: Evolutionary simulation-optimization (ESO) techniques can be adapted to model a wide variety of problem types in which system components are stochastic. Grey programming (GP) methods have been previously applied to numerous environmental planning problems containing uncertain information. In this paper, ESO is combined with GP for policy planning to create a hybrid solution approach named GESO. It can be shown that multiple policy alternatives meeting required system criteria, or modelling-to-generate-alternatives (MGA), can be quickly and efficiently created by applying GESO to this case data. The efficacy of GESO is illustrated using a municipal solid waste management case taken from the regional municipality of Hamilton-Wentworth in the Province of Ontario, Canada. The MGA capability of GESO is especially meaningful for large-scale real-world planning problems and the practicality of this procedure can easily be extended from MSW systems to many other planning applications containing significant sources of uncertainty. [Copyright &y& Elsevier]