BRT fare elasticities from smartcard data: Spatial and time-of-the-day differences
place - south america, place - urban, mode - bus rapid transit, ridership - behaviour, ridership - elasticity, ridership - demand, policy - fares, policy - equity, technology - passenger information
Fare, Elasticity, BRT ridership, Equality, Transmilenio, Bogotá
The changes in public transport ridership can be quantified as fare elasticities that are useful inputs to inform planning and policymaking, particularly for Bus Rapid Transit (BRT) systems in developing city contexts. This research provides new evidence to the limited literature in the Global South about revealed preferences fare elasticity from disaggregated data and improves our knowledge of BRT passengers’ travel behavior providing insights on the important role of achieving an affordable fare. Using a 9-year smartcard data, we show that in a spatially segregated city, such as Bogotá, the BRT fare has differential effects according to the socio-economic characteristics of its users and the time-of-the-day. To estimate the fare elasticity considering the socio-economic heterogeneity of users, we proposed a clustering algorithm based on the station-demand profiles and an indicative average per capita income associated with the station catchment area. The results show significant statistical differences in elasticities between the three identified clusters. The stations located in the urban periphery, associated with low-income areas, show null or low response to the fare changes, as opposed to the stations located on the east edge of the city (wealthiest zones). These findings show that a flat fare in the BRT system has differential fare effects on users, therefore, considering those differences when designing the fare policy scheme would contribute to starting to close accessibility gaps in the city.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Guzman, L.A., Beltran, C., Bonilla, J., & Gomez Cardona, S. (2021). BRT fare elasticities from smartcard data: Spatial and time-of-the-day differences. Transportation Research Part A: Policy and Practice, Vol. 150, pp. 335-348.