Future accessibility impacts of transport policy scenarios: equity and sensitivity to travel time thresholds for Bus Rapid Transit expansion in Rio de Janeiro

Abstract

Highlights• The study estimates how the TransBrasil BRT project in Rio de Janeiro will likely impact employment accessibility for different income groups • The full implementation of the proposed BRT could benefit up to 58% of the city population, increasing job accessibility by 11% on average • This BRT project could promote substantially larger accessibility gains for lower-income groups and reduce inequalities in access to opportunities • The study shows that the conclusions of equity assessments of transport projects depend on the travel time threshold chosen for cumulative opportunity accessibility analysis AbstractThe accessibility impacts of transport projects ex-post implementation are generally evaluated using cumulative opportunity measures based on a single travel time threshold. Fewer studies have explored how accessibility appraisal of transport plans can be used to evaluate policy scenarios and their impacts for different social groups or examined whether the results of project appraisals are sensitive to the time threshold of choice. This paper analyzes how different scenarios of full and partial implementation of the TransBrasil BRT project in Rio de Janeiro (Brazil) will likely impact the number of jobs accessible to the population of different income levels. The analysis is conducted under various travel time thresholds of 30, 60, 90 and 120 minutes to test whether the results are sensitive to the boundary effect of the modifiable temporal unit problem (MTUP). Compared to a partial operation scenario, the full implementation of TransBrasil that extends this corridor into the city center would lead to higher accessibility gains due to network effects of connecting this BRT to other transport modes. Nonetheless, the size of the accessibility impacts of the proposed BRT as well as its distribution across income classes would significantly change depending on the time threshold chosen for the accessibility analysis. Considering cut-off times of 30 or 60 minutes, both scenarios of TransBrasil would lead to higher accessibility impacts in general and particularly for low-income groups, moving Rio towards a more equitable transportation system. However, under longer thresholds of 90 and 120 minutes, an evaluation of this project would find much smaller accessibility gains more evenly distributed by income levels. The paper highlights how time threshold choice in cumulative opportunity measures can have important but overlooked implications for policy evaluation and it calls for further research on the MTUP in future transport and mobility studies.

Disparities in travel times between car and transit: Spatiotemporal patterns in cities

“…Due to varying data availability across the study cities, we select the first Wednesday in May after the updating date of GTFS as shown in Table 3. Routing between a given origin-destination pair is conducted using an open-source multi-modal routing engine, OpenTripPlanner (OTP) 62 , similarly used in previous studies 18,63,64 .…”

“…For each pair of origin-destination, OTP finds the fastest door-to-door trip given a set departure time and the combination of transport modes available. The maximum walking distance is set to 800 m. To better balance the errors and the computation time, we use a hybrid sampling approach with 15-min resolution that helps us avoid the Modifiable Temporal Unit Problem (MTUP) 18,35 . The departure times are set to every 15 min from a randomly selected start time until an average weekday is sampled by 96 evenly spaced times.…”

“…While there is strong evidence that car-based travel is often faster than public transit, the spatial and temporal patterns of this time discrepancy are crucial to better inform urban planning and policy efforts to encourage travel mode shifts. Recent studies show how transit travel times can vary greatly by route and time of day 17,18 , while a growing body of research using GPS and mobile phone data to record travel speed shows that overall transport performance varies significantly by time of day due to congestion in cities [19][20][21] . Traditional static views on travel time calculation have largely relied on simplistic assumptions, using constant vehicle speeds 22 and overlooking how travel speeds vary over the course of the day 23 .…”

A data-driven complex network approach for planning sustainable and inclusive urban mobility hubs and services

“…The time of day for which the O-D matrices are calculated is also important to consider, because service levels and transit departure times vary throughout the data. While Pereira (2019) calculated average travel time matrices based on travel at regular intervals throughout the day, Boisjoly and El-Geneidy (2017) showed that calculating accessibility for travel at 8 a.m. only was a reliable indicator of relative accessibility at other times in Toronto. Given our study examines accessibility to hubs, we consider Boisjoly and El-Geneidy’s (2017) single-time approach to reliably reflect commuting dynamics in our study cities.…”

The Rio Olympic Games: A Look into City Dynamics through the Lens of Twitter Data

“…Apart from these Olympic areas, other improvements in road infrastructure were carried out in different parts of the city. Transport infrastructure projects promote accessibility gains [89]. However, access inequalities in the city of Rio seem to remain [90].…”