Abstract
We have collected information on 46 bus rapid transit (BRT) systems throughout the world to investigate the potential patronage drivers. From a large number of candidate explanatory variables (quantitative and qualitative), 11 sources of systematic variation are identified which have a statistically significant impact on daily passenger-trip numbers. These sources are fare, headway, the length of the BRT network, the number of corridors, average distance between stations; whether there is: an integrated network of routes and corridors, modal integration at BRT stations, pre-board fare collection and fare verification, quality control oversight from an independent agency, at-level boarding and alighting, as well as the location of BRT. The findings of this paper offer important insights into features of BRT systems that are positive contributors to growing patronage and hence should be taken into account in designing and planning BRT systems.
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Notes
An alternative definition suggested in the literature by the Institute for Transportation and Development Policy ITDP, http://www.itdp.org/) and BRTuk (http://www.brtuk.org/) is “A flexible, frequent, dependable bus transit system that combines a variety of physical and operating elements into a permanent and integrated system with a quality image and a unique identity”.
Given that this is a multinational study, it would have been desirable to express fare normalised by some form of cost-of-living index. We did test for the normalisation of Fare divided by GDP per capita to approximately capture this effect. However this normalised variable was not statistically significant; and in addition its natural logarithm was much more correlated with some explanatory variables than with the dependent variable. For example we observed correlations of -0.2628 with the natural logarithm of headway versus 0.1411 with the natural logarithm of daily passengers-trips (dependent variable). Given this, the stand-alone Fare is used in the model as an explanatory variable (see Table 2).
We also investigated the use of population density to normalise the length of the BRT network (i.e., BRT length/population density); however this lead to similar correlation problems to those observed with the normalised fare variable (i.e., fare/GDP per capita). The only normalised variable that we found was statistically significant with low relative correlation was the average distance between stations divided by population density.
This level of integration means that a BRT system is connected with other public transport routes to establish a network which allows for door-to-door service.
While many operators have their own internal quality control, the particular data item we were able to collect related to quality control from an independent entity/agency.
An OLS regression model is also estimated (see Appendix Table 5), which delivers a fare elasticity of −0.402 and a headway elasticity of −0.30, both higher than the estimates of the random effects regression model. Given that the random effects regression model (Table 2) is econometrically appealing given the data than OLS regression, the elasticities reported in Table 2 are preferred.
A referee commented on the loss of speed as a consequence of closer station spacing. There are often clear trade-offs between speed and access to the system from an origin location. A BRT system, compared to a railway, typically has closer bus stop spacing than train station spacing which provides better access times.
The passenger-trip number of Hangzhou BRT was collected in 2006. Therefore, only Line 1 (27.2 kilometres) is used as its total length. We also used the year that the passenger-trip number was collected in the mode to address the difference in the data collection period, but it is not statistically significant.
We are only able to identify population density at the city level, not at the BRT corridor or catchment area level.
We have not been able to obtain data on car ownership at the city level for all of the BRT system locations. Also GDP per capita will be highly positively correlated with car ownership, and hence including both would be problematic.
The model estimated is not a demand model in the fuller sense of accounting for competing modes and the influence of the socio-economic and spatial context; rather it is a representation of a model designed to identify the potential influence of BRT design, service and fares on passenger trips per day, holding all other possible influences constant at an average level that is captured by the model constant.
This also requires some appropriate pricing mechanisms such as congestion pricing.
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Acknowledgment
This study is undertaken as part of the research program LS1 of the Volvo Research and Education Foundation Bus Rapid Transit Centre of Excellence. We thank Corinne Mulley for her comments and three referees for some very insightful comments that have contributed materially to improving this paper.
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Hensher, D.A., Li, Z. Ridership drivers of bus rapid transit systems. Transportation 39, 1209–1221 (2012). https://doi.org/10.1007/s11116-012-9392-y
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DOI: https://doi.org/10.1007/s11116-012-9392-y