One of the most persistent challenges in public transportation is the “first-and-last mile” (FM/LM) problem: how riders travel from their trip origin, such as homes or workplaces, to the nearest transit stop, and then from the disembark stop to their final destination. In my recent PhD research, published in Travel Behaviour and Society, I examined how bike share systems can help bridge this gap in Hamilton, Ontario.

Hamilton Bike Share Service Area

Using GPS data from Hamilton Bike Share, I developed a new method to evaluate whether a trip functions as a FM/LM connection. Rather than relying on simple buffer zones around transit stops, I applied a spatiotemporal distance decay model that incorporates both geographic distance to or from transit stops and temporal distance, or transfer time, to or from the nearest bus arrival. This approach enables the assessment of whether a bike share trip is a FM/LM connection on a continuous scale ranging from 0 to 1, offering a more nuanced understanding of how riders actually use bike share in relation to transit.

Because bike share trips near transit stops may also be destined for nearby points of interest, such as restaurants, shops, or parks, I introduced a penalty term to account for the presence or the distance of these alternative destinations. Model parameters were selected based on existing literature and calibrated against travel patterns from the 2016 Transportation Tomorrow Survey.

The findings revealed distinct patterns. Many riders use bike share to connect between transit stops and workplaces, underscoring its role as a practical commuting tool. Interestingly, FM trips were found to be more challenging than LM ones, largely due to the higher frequency of left turns and U-turns. FM/LM trips also differed noticeably from overall bike share usage, highlighting the unique characteristics of these connections.

These insights point to opportunities for improving wayfinding, expanding cycling infrastructure, and promoting better integration between bike share and transit systems. Beyond Hamilton, this research provides a methodological framework that can be applied in other cities to analyze FM/LM connections using real-world trip data such as origin and destination pairs. By combining spatial analysis with observed travel behaviour, we can design more effective, connected, and user-friendly urban transportation networks.

For those interested in the full study, the article is available open access: https://doi.org/10.1016/j.tbs.2025.101122.