Inclusive Accessibility: Translating person-based mobility perceptions into an aggregated measure

Keywords: space-time prism, active travel, mobility, equity, smartphone data
Abstract Type: Paper Abstract

Abstract

Past accessibility measures, either place-based or person-based, are mainly physicalist and biased in acknowledging the behavioral aspects of travel and its socio-economic differences. Besides, the scale of measurement and level of data requirements makes place-based measures inadequate in capturing the complexities around human travel and person-based measures impractical to apply in planning practices. This study introduces an inclusive accessibility measure, a bottom-up approach addressing these heterogeneous mobility perceptions into multimodal accessibility measures at scale and across social groups. We quantify accessibility constraints into hard (explicit space-time limitations) and soft constraints (implicit and perceptual factors influencing travel, such as safety perceptions, comfort, and willingness to travel). Using clustering algorithms on a mobility survey dataset of 477 travelers, we categorize them into social cohorts based on their sensitivity to network and built environment features. Next, we apply ML-based regression algorithms to estimate network-level soft constraints for each travelers’ group using their survey data as train sets. Finally, we derive an aggregated inclusive accessibility measure in reference to the time-geographic space-time prism (STP), specific to each social group minimizing their perceived soft constraints to a certain threshold, and demonstrate its differences from the aggregated classic STP measure. This method overcomes the limitations of both past place-based and person-based measures that simultaneously preserves detailed mobility perceptions for different social cohorts based on their similarities in travel perceptions, as well as capable of performing accessibility analysis at a larger geographic scale, making the approach suitable for equity-oriented need-specific transportation planning.