Emily Grisé

Park ‘n’ Roll: Identifying and Prioritizing Locations for New Bicycle Parking in Québec City, Canada

Promoting active modes of transportation, such as cycling, is an ongoing challenge faced by many cities around the world. Fostering a bicycle culture in an auto-dominant region is riddled with challenges, but success has been achieved with investments in bicycle infrastructure, including bicycle parking. This study presents a new methodology to identify the optimal locations to install short-term (bicycle racks) and long-term (bicycle lockers or indoor locking facilities) bicycle parking using a GIS-based approach that considers multiple criteria. Using Québec City, Canada, as a case study, our methodology considers multiple criteria related to the demand for bicycle parking, including the destinations of existing and potential cyclists and proximity to a frequent bus service. A prioritization index is developed to identify the optimal locations for long-term and short-term bicycle parking. This is followed by a recommendation of the number of bicycle parking spaces required to meet existing and potential demand. This paper aims to provide practitioners with an easy-to-use method to aid in the planning of new bicycle parking infrastructure, which is designed to be flexible and adaptable to other contexts.

When I’m 64

The growth rate of adults older than 65 in Canada is increasing more rapidly than the population as a whole. This increase is reflective of the aging baby boomer population. That population is known to have a strong attachment to automobiles, which might be reflected in their travel behavior as they move toward different stages in their older life. The purpose of this paper is to contribute to the understanding of the travel behavior, mainly public transit usage, of Canada’s older population relative to younger cohorts. A pseudocohort analysis was conducted in Montreal, Quebec, Canada, of residents who were 50 or older to follow changes in public transit use of similarly aged respondents from 1998 to 2013. The results revealed that older generations used public transit more than younger generations did at the same age. In addition, the most recent survey year showed a stagnation of transit use across all age groups. Differences in transit use between males and females were more pronounced in earlier cohorts, but the difference was decreasing in more recent years. These findings add to the growing body of work suggesting that the nature of transportation behavior in seniors is changing, and accordingly planners and engineers cannot expect the baby boomer generation to behave the same way as previous generations. Addressing the transportation needs of seniors around the world will be an important challenge for planners and engineers, as the population of seniors is growing more rapidly than the population as a whole in the majority of developed countries. This growth imposes new challenges on the transportation system because of differences in the travel behavior of today’s older adults compared with that of previous cohorts of seniors.

Identifying the Bias

Data from automated vehicle location (AVL) systems, automatic passenger counter (APC) systems, and fare box payments have been heavily used to generate dwell time models with the goal of recommending improvements in efficiency and reliability of bus transit systems. However, automatic data collection methods may result in a loss of detail with regard to the dynamics of passenger activity, which may bias the estimates associated with dwell or passenger activity time. The purpose of this study is to understand better any biases that might exist from using data from AVL–APC systems or fare box payments when estimating dwell time. Manually collected data from Montreal, Quebec, Canada, are used to estimate detailed dwell time models. This study compared those estimates to models generated by using data similar to what was reported by AVL–APC systems and fare boxes. The results reveal an overestimation in the passenger activity component of dwell time, which is mainly attributed to excess dwell time that AVL–APC data and fare box payments generally do not capture. While AVL–APC and fare box technologies provide transit agencies with rich data for analysis, adjustments to such data collection methods are warranted to reduce the overestimation of dwell time and to provide a more accurate picture of what is happening on the ground to generate better interventions that can reduce dwell times.