Modelling the impact of employer-based TDM strategies on daily activity-travel demand

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Transportation Demand Management (TDM) strategies are now integral parts of transportation planning processes of all Canadian municipalities. Various TDM strategies are considered for reducing demand for private auto mobile and increasing the use of publication transit and active mode of transportation (walking, biking, etc.). In general, the TDM strategies include a wide variety of initiatives ranging from quantitative measures (e.g. parking cost, transit fare subsidy, biking facility etc.) to qualitative measures (e.g. promotion of biking, providing ride-matching services, social marketing, etc.). From strategic planning and evaluation points of view, quantification of the effects of any TDM strategies (either quantitative or qualitative in nature) is very important for the selection of alternative strategies, prioritization of higher impact strategies and capital budgeting. However, there has been a lack of proper analytical tool for such quantitative evaluation of TDM strategies. In this regard, an important progress has been made by the Region of Peel (in collaboration with the University of Toronto) in 2015 in terms of collecting data for quantitative evaluation of some key employer-based TDM strategies. An innovative survey called OFF-SET survey was conducted in the Peel Region that collected household travel diaries of 633 commuters along with a series of stated preference enquiries on respondent's commuting mode choice sensitivity to the corresponding employer-based TDM strategies. This dataset is available for this project and it allows a unique opportunity of investigating the link between commuting mode choices and daily activity-travel demand in context of various TDM strategies. It is important to develop an analytical tool (mathematical model) that can link daily activity-travel demand and commuting mode choice decisions so that the effects of any employer-based TDM strategies on peoples' daily travel demand can be investigated. Objective of this project is to develop an econometric choice modelling framework (mathematical model that