Abstract
There is a need for indicators of transportation–land use system quality that are understandable to a wide range of stakeholders and that can provide immediate feedback on the quality of interactively designed scenarios. Location-based accessibility indicators are promising candidates, but indicator values can vary strongly depending on time of day and transfer wait times. Capturing this variation increases complexity, slowing calculations. This paper presents new methods for rapid yet rigorous computation of accessibility metrics, allowing immediate feedback during early-stage transit planning while being rigorous enough for final analyses. The approach is statistical, characterizing the uncertainty and variability in accessibility metrics related to differences in departure time and headway-based scenario specification. The analysis was carried out on a detailed multimodal network model including both public transportation and streets. Land use data were represented at high resolution. These methods were implemented as open-source software running on a commodity cloud infrastructure. Networks were constructed from standard open data sources, and scenarios were built in a map-based web interface. A case study is presented, describing how these methods were applied in a long-term transportation planning process for an urbanized, polycentric Randstad region in the Netherlands.
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