In the context of public transit networks, repeated calculation of accessibility at multiple departure times provides a more robust representation of local accessibility. However, these calculations can require significant amounts of time and/or computing power. One way to reduce these requirements is to calculate accessibility only for a sample of time points over a time window of interest, rather than every one. To date, many accessibility evaluation projects have employed temporal sampling strategies, but the effects of different strategies have not been investigated and their performance has not been compared. Using detailed block-level accessibility calculated at 1-minute intervals as a reference dataset, four different temporal sampling strategies are evaluated using aggregate sample error metrics as well as indicators of spatially clustered error. Systematic sampling at a regular interval performs well on average but is susceptible to spatially-clustered harmonic error effects which may bias aggregate accessibility results. A constrained random walk sampling strategy provides slightly worse average sample error, but eliminates the risk of harmonic error effects.
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