The equity of public transport crowding exposure

Public transport crowding exposure is known to cause discomfort, stress and dissatisfaction. However, the dis-tribution and equity of crowding exposure across socioeconomic groups has been largely unexplored. This paper opens a new research topic connecting crowding exposure in public transport to travelers' socioeconomic characteristics. We present a framework for assessing the equity of in-vehicle crowding exposure based on automatic data sources. Two metrics are considered for quantifying the travelers' in-vehicle crowding exposure: (1) the excess perceived travel time and (2) the relative excess perceived travel time. The proposed methodology computes the two metrics based on travel diaries and in-vehicle loads inferred from automated fare collection data. We implement Lorenz curves, Gini and Suits coefficients to evaluate horizontal (across the population) and vertical equity (considering income as well as mobility ability and need). The vertical equity is further discussed using clusters of socioeconomic groups and results from spatial lag regression models to assess the distribution of crowding exposure across socioeconomic characteristics. The results for the Stockholm Region case study indi-cate that crowding exposure varies substantially across the service area, with the highest values found in the denser urban areas close to Stockholm City. We find that the distribution across socioeconomic groups is rela-tively even, but travelers from areas that are wealthier, higher educated, have higher share of rental housing or lower vehicle ownership areas tend to be exposed to more crowding. The paper provides tools to support public transport planners in decision-making, showing where to intervene to reduce crowding exposure efficiently to achieve urban equity and sustainability.

Automated summary

This paper explores the distribution and equity of crowding exposure in public transport across socioeconomic groups. It presents a framework for assessing equity based on automatic data sources and proposes two metrics to quantify crowding exposure. The results show that crowding exposure varies across the service area, with wealthier and higher educated individuals from rental housing or lower vehicle ownership areas being more exposed. The paper provides tools to support public transport planners in reducing crowding exposure and achieving urban equity and sustainability.