As transit agencies strive to provide better service within increasingly tight budgets, many are turning to bus rapid transit (BRT) as a cost-effective option. In 2008 and 2009, NJ TRANSIT introduced an enhanced bus service (“go bus”) that incorporates several BRT features along two densely populated urban corridors. The agency sought to understand which features customers value the most, how the enhanced service affects perception of time savings, and whether the service caused a shift in travel patterns. In addition to revealing customer preferences and impacts of bus rapid transit features, this paper shows the benefits of utilizing multiple data collection techniques to answer a research question.
In order to assess the impacts of “go bus” service, NJ TRANSIT undertook a study that incorporated both quantitative and qualitative techniques, which included an onboard paper survey, focus groups, and web-based Maximum Difference Scaling (MaxDiff) survey. Each method brought to the forefront new information while also reinforcing data collected through other means. For example, the onboard survey revealed perceived travel time savings that exceeded actual time savings, while the focus group discussions confirmed this perception and allowed the agency to understand what made the trip seem faster to customers. These findings are significant for transit agencies, as measures to reduce perceived trip time may be easier and less costly to implement than measures to reduce actual trip time, while still enhancing the experience of the customer.
The use of MaxDiff was especially helpful in quantifying which features of service are most important to customers. This technique allows researchers to determine the magnitude by which certain attributes are valued in comparison to others by forcing respondents to make trade-offs between attributes. Through this method, NJT learned that travel time, frequency of service, and convenience attributes were valued twice as high as branding attributes. This information allows transit agencies to focus resources on attributes that are highly valued by customers, rather than wasting resources on features that have low impact.