As part of my PhD, I set out to understand how different bike share riders actually move through a city. We often talk about bike share users as if they form a single, homogeneous group. But once you dig into the data, a far more nuanced landscape emerges.

Most Heavily Used Roads by Bike Share Riders in Hamilton, Ontario
Most Heavily Used Roads by Bike Share Riders in Hamilton, Ontario

In a new paper published in Cities, I analyzed every GPS‑tracked Hamilton Bike Share trip taken in 2024. The system’s riders fall into three main categories: Monthly and Seasonal Members, Pay As You Go users, and McMaster Monthly Pass holders, a group that dominated the system usage after the McMaster Student Union introduced a low‑cost U‑Pass in September 2024. Instead of relying only on trip origins and destinations, I reconstructed the actual streets riders used. This made it possible to see how different groups navigate the network at the road‑segment level and what kinds of environments they prefer.

Each membership group follows its own temporal trends. Monthly and Seasonal Members behave like classic commuters, with pronounced morning and late‑afternoon peaks, even in the depths of winter. Pay As You Go riders and McMaster pass users only show that pattern in the summer months, which suggests more discretionary or weather‑sensitive travel.

These groups also move through different parts of Hamilton. McMaster pass users cluster heavily on and around McMaster University campus, while Monthly and Seasonal Members and Pay As You Go riders are more active downtown and along the roads in Van Wagner’s Beach area. The system is not serving one archetypal trip but several overlapping mobility patterns, each tied to a different rider type.

Across all groups, the built environment plays a major role in shaping where people ride. Commercial areas consistently attract more bike share traffic, while low‑density residential neighbourhoods see less. Riders gravitate toward streets with bikeways, and steep slopes discourage travel. Weather leaves its own signature: warmer temperatures boost ridership up to a point, rain suppresses it, and daylight encourages it.

Understanding rider heterogeneity is essential for designing bike share systems that reflect real travel behaviour. This study shows that different groups ride for different reasons, at different times, and in different parts of the city. Infrastructure, land use, and topography all shape where riding happens.

If you would like to explore the full technical details, the paper is open access in Cities and available here: https://doi.org/10.1016/j.cities.2025.106703.