Development of a Mode Choice Model to understand the potential impacts of LRT on Mode Shares in the Region of Waterloo

Abstract

A new light rail transit system (LRT), ION, began operations in the Region of Waterloo in the June of 2019, and the second phase is yet to begin construction. The main thrust of this growth management project for the region was achieving sustainability goals by promoting denser development and boosting transit ridership. The LRT is integrated within the existing transit system, and this study intends to understand its impact on transit mode shares. To understand the potential impact of introduction of a new transit system on mode shares, an analytical modelling approach is required. This research conducts spatial analysis in ARCMap to describe the current commuting patterns in the region of Waterloo, highlighting the spatial distribution of modal shares, top trip origins and destinations and trip distribution patterns by different modes. Furthermore, many nested logit and multinomial logit models were estimated to relate various socio economic, spatial and trip attributes to mode choice behaviour. The nested logit models did not prove to be a good fit for the available data, and the best multinomial logit model was finally used to understand the potential impacts of LRT. The final model estimation projects 0.09% increase in commuter transit ridership for the estimated average decrease of 0.14% in travel time, which is a result of both, introduction of ION and realignment of transit routes. It is however, essential to note that ION is a driver of urban growth and development with potential to attract denser and mixed land use developments, which are both key to increasing transit ridership. This study is a start towards understanding the impacts of LRT and findings may prove to be a valuable resource to discerning spatial distribution of commuter trips in the region. Additionally, the model serves as flexible template, which can be employed to assess the impacts of LRT in the future and inform transit policy decisions.