Short run fare elasticities for Bogotá’s BRT system: ridership responses to fare increases
The fare policy of the BRT system in Bogotá, in order to cover its operating costs, has consisted of steadily fare increases, since its creation until 2012. To date, no study has been done to estimate the users’ reaction to these changes in the short-term. That is, there is no information about pri...
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2020
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| In: |
Transportation
Year: 2019, Jahrgang: 47, Heft: 5, Pages: 2581-2599 |
| ISSN: | 1572-9435 |
| DOI: | 10.1007/s11116-019-10034-6 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/s11116-019-10034-6
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| Verfasserangaben: | Luis A. Guzman, Santiago Gomez, Carlos Alberto Moncada |
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| 520 | |a The fare policy of the BRT system in Bogotá, in order to cover its operating costs, has consisted of steadily fare increases, since its creation until 2012. To date, no study has been done to estimate the users’ reaction to these changes in the short-term. That is, there is no information about price-demand elasticities. This issue should be a key factor in deciding on price changes and evaluating the impact of these changes on ridership. In the case of Bogotá’s BRT (Transmilenio), estimating such elasticity is a need, but also a complex task: the travel demand is growing constantly and few fare changes happen at the same time throughout the whole system. To overcome this barrier, an econometric panel data model at station level was developed that takes advantage of highly disaggregated information on ridership for the Transmilenio system. The database provided information on entrances to the system’s stations between 2001 and 2012 at the daily level (phases 1 and 2). Monthly information on other factors that may influence ridership, like fuel prices, unemployment rates, population and traditional bus fares, were also included. After the introduction of a fare increase, the elasticity’s absolute value decreases from − 0.565 (1 week) to − 0.408 after a month. In addition, low-income users are more sensitive to these changes. We also test for differences between the effects during peak and off-peak hours. The results show higher values in the off-peak hours than in the peak hours. This should inform decision takers in Bogotá about the effect of fare changes on ridership responses and also on equity and accessibility. | ||
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