Integrating bicycling with public transport can potentially benefit cyclists and transit operators. Successfully coordinating these transport modes, however, can be a difficult task when so little is known about the social and environmental barriers to this type of multimodal travel in the North American context. Using data derived from a survey of regional train service patrons in the Greater Toronto and Hamilton regions of Ontario, Canada, this study examines the challenges faced by those who cycle to/from the train, the barriers that keep passengers from commuting to/from the train by bicycle, and the sociodemographic characteristics of those cycling-and not cycling-to/from the train. Safety concerns, worries about bicycle security, and rules restricting when bicycles are permitted on trains were among the top challenges identified by individuals currently cycling to and/or from train stations. Among those who do not cycle to or from the train, appearance and comfort were the two primary concerns. Results also indicate that certain groups were more likely to cycle to/from the train than others. Notably, a large gender gap exists, approximately two-thirds (67%) of those cycling to their local train station were male. Results from this study may inform policy makers on how to successfully, and equitably, integrate cycling with regional rail transit.
This study investigated the effectiveness of the Smart Commute program, a well-established travel demand management (TDM) program in the greater Toronto and Hamilton area of Canada. The study exploited a data fusion technique to combine data collected through cross-sectional ex ante and ex post surveys at the workplace of each Smart Commute member. Two types of approaches were used: aggregate statistical analysis and disaggregate choice modeling with the fused-combined data set. The results clarify that aggregate investigation may not always uncover many behavioral details. Aggregate comparisons of the survey data showed that the performance of the Smart Commute program varied by sociodemographic attributes of the employees, including age, employment status, employment shift, and regional municipality of employment. The aggregate investigation also showed that the stated willingness to consider bike, walk, and telework options was not reliable in evaluating the effectiveness of any TDM policy. Complementary to the aggregate investigation, the study used an advanced mixed logit model framework to explore the effects of Smart Commute on commuters’ perceptions about travel attributes. The results of the empirical model revealed more variation in the perceptions of commuters about travel attributes after implementation of TDM interventions. In addition, the perceptions of travel times became more negative after TDM implementation.
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