Spatial and Temporal Utility Modeling to Increase Transit Ridership

Abstract

The objective of this research project was to develop a better understanding of the possible alternatives that a large employment center, like the University of California at Santa Barbara, can adopt in order to better utilize transit, mitigate traffic, and reduce demand for on-site parking. Although this project was oriented to the UCSB campus, the techniques and approaches developed in this project were designed to be equally applicable elsewhere. There were three major elements of this project: 1) develop an understanding of commuting employees through the use of a survey, 2) identify spatially whether there exist areas in which transit service is competitive or nearly so to the use of the single occupant vehicle (SOV), and 3) identify whether there are areas in which special express bus routes could provide competitive access to the University. The survey involved more than 2300 university students, faculty, and staff. The survey revealed a number of important points including that nearly 30% of the staff and faculty have never used transit, do not know where their nearest route or bus stop is, and have no knowledge of how to obtain such information. It is clear that converting some employees to using transit will require both incentives and better information. The project also identified a number of other important features of those commuting to work at the University, including the fact that most go directly to and from work without making multi-purpose trips. This fact alone makes it easier to develop special express bus routes to serve the University or convert employees to transit riders. The survey also revealed that a sizable minority ride their bikes to the University. Although data from the survey could not be used to show that there is a statistically significant pattern of ridership within the context of access to Class 1 bike routes, there is reason to believe that a more comprehensive study might reveal whether this occurs. Such a finding could help in understanding the role of bikes in commuting to work, when safe routes are available. The project also focused on the provision of transit services by mapping access times in the journey to the University and the journey to home from the University. Overall, access times are quite large, especially when comparing transit service times to that of using a personal vehicle. As a part of this research, a methodology was developed to map access times, using bus routes, schedules, and time of day. The model identifies for all areas of a region surrounding a large employment center, approximate combined walk and transit travel times. Mapping the ratio of transit service times to personal vehicle travel times to the University, allows one to map those areas where transit service is competitive to using a personal vehicle (i.e. ratio is close to or less than 1). Map results tend to indicate that most of the south coast region is poorly served by transit when commuting directly to the University, where the public transit trip takes at least twice the time as compared to using a personal vehicle. There are, however, some areas that are served with good access to the University; a program might be designed to encourage those residents who live in such areas to use transit. Finally, a special routing model was developed that can be used to design express bus service routes. This model was applied to the South Coast Region about the University, and several routes of high employee coverage were identified. Express routes that are relatively short and which could potentially serve a large number of university employees with commuting service to and from the University may make it possible to reduce SOV trips to the University. All of the tools developed as a part of this project were designed within the context of future application at other centers of large employment. This research was supported by the California Partners in Advanced Transit and Highways (PATH) under Grant Number SA4318 from the University of California, Berkeley, funded by the California Department of Transportation under Agreement Number 65A0161.

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