Public transport availability inequalities and transport poverty risk across England

Abstract

The general transit feed specification is becoming a popular data format for the publication of public transport schedules, making possible the collection of a nation-wide public transport schedule dataset, which enables monitoring of transit supply at an up-to-date and more precise level across a country than previously possible. In this paper, we use general transit feed specification data to measure local-scale public transport availability across England based on service frequency and spatial proximity to public transport stops/stations. Moreover, to demonstrate the usefulness of public transport availability measures, we examine inequalities of public transport provision and identify areas at risk of transport poverty across England. Furthermore, we estimate population (number of households) who are likely to suffer from transport poverty, accounting for public transport availability, time-based job accessibility by public transport or walking, household income and car ownership levels. Based on the criteria, we have used to identify public transport risk, we find that investment in the development of public transport services should prioritise West Midlands, East of England, South East and South West as those regions have more households who are likely to suffer from transport poverty. This paper contributes by (1) defining more comprehensive transit availability measures than existing measures at a variety of geography levels and (2) integrating fours aspects (i.e. public transport availability, job accessibility by public transport or walking, household income and car availability) to analyse transport poverty comprehensively.

A New Combined Model for Assessing the Perceived Accessibility of Public Transport by Consumers in the Megapolis

“…The lowest level of satisfaction was revealed regarding affordability. This corresponds with earlier research where the cost of a ticket for travel affects the risk of "transport poverty" [22][23][24][25] and must be under hard control. The ability to get the service itself (Availability) and its attributes: bus waiting time, bus punctuality, bus frequency, and bus schedule, also affect passenger dissatisfaction.…”

“…For example, in a series of transport studies in the Bogotá Region (Colombia), an analysis of the impact of poverty on transport accessibility showed that the higher the household income, the better the transport accessibility [19][20][21]. As some researchers have demonstrated in their studies, even in prosperous countries there is a problem of economic inequality in the provision of public transport and the risk of "transport poverty" [22][23][24][25].…”

“…This explains the similar level of satisfaction of respondents with different frequency of using UGPT. Some researchers have summed up that there is a problem of economic inequality in the accessibility of public transport in different countries [24,25]. The similar perception of affordability (by Price) of respondents in Moscow can be explained by the high level of social support of the population with low incomes.…”

Tracking the Transit Divide: A Multilevel Modelling Approach of Urban Inequalities and Train Ridership Disparities in Chicago

“…The service area is created from a road-network buffer, rather than a circular buffer which is used in most existing studies (Figure A3). The road-network buffer more accurately captures each stations' service area [41] and is defined as 800 m, as it is typically used as the upper limit of where most users will travel to access rail stations by foot [42]. Due to the simplicity of buffers, they do induce bias where the influence of neighbourhoods beyond the 800 m buffer is not considered [41].…”

“…The road-network buffer more accurately captures each stations' service area [41] and is defined as 800 m, as it is typically used as the upper limit of where most users will travel to access rail stations by foot [42]. Due to the simplicity of buffers, they do induce bias where the influence of neighbourhoods beyond the 800 m buffer is not considered [41]. This could influence the interpretation of the results, particularly with the segregated nature of Chicago's neighbourhoods, as mainly African American neighbourhoods live near rail stations [10].…”