Characteristics of Premium Transit Services that Affect Choice of Mode (2014)

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1 S U M M A R Y Introduction Traditional travel forecasting models typically use travel time and cost to represent the usefulness of each transportation mode to serve potential trips. For transit options, time and cost are used to define optimal routing (i.e., boarding locations, routes, and alighting locations) and the probability that the traveler will select transit to make the trip. These techniques have often struggled to represent ridership demand for some higher-speed, higher-frequency transit services, particularly those classified as fixed guideway systems (labeled as “premium services” in this document). Forecasters have tried to represent the higher levels of demand for these services with a variety of techniques including defining separate transit choices in mode choice procedures and adjusting perceived travel times to represent the apparent preference for these services. Typically, these adjustments are applied on an aggregate basis with very little understanding of the underlying factors that cause models to under-represent premium transit ridership. To improve understanding of these underlying factors, this research focused on identifying and quantifying aspects of transit travel behavior in different urban contexts that affect traveler use of premium transit services. Data on transit service attributes, traveler attitudes, and awareness were collected and analyzed in Salt Lake City, Utah; Chicago, Illinois; and Charlotte, North Carolina to better understand traveler responses to premium transit services. Models were estimated to evaluate the influence of traveler attitudes, awareness, and consideration of transit service characteristics on traveler evaluation of premium transit services. The research also included a demonstration of how transit service attributes could be meaningfully incorporated into travel models to reduce the influence of unobserved factors and modal labels in mode choice models and improve forecasting capabilities of transit services. Two key phrases used in this report are defined for clarity: • Non-traditional transit service attributes are those attributes other than time and cost that are important to travelers in choosing to ride transit. These aspects of transit services include: – On-board amenities (seating availability, seating comfort, temperature, cleanliness of a transit vehicle, productivity features); – Station design features (real-time information, security, lighting for safety, shelter, proximity to services, cleanliness of the station, benches); and – Other features (route identification, reliability, schedule span, transit frequency, transfer distance, stop distance, parking distance, ease of boarding, fare machines). • Premium transit services are defined based on a series of attributes that together rep- resent a higher class of service. These attributes exist over a broad continuum of transit Characteristics of Premium Transit Services that Affect Choice of Mode

2 Characteristics of Premium Transit Services that Affect Choice of Mode services in operation and are not necessarily associated with a particular vehicle technology. For instance, a commuter coach service offering a seat with Wi-Fi service to all customers and a highly reliable schedule may be perceived as superior to a crowded rapid transit rail line with fewer amenities. An analytical approach and framework is described in this paper to acknowledge that these services often exist as a continuum between premium and non-premium and are not easily represented as separate and discrete modes. Surveys conducted in Salt Lake City, Chicago, and Charlotte were analyzed to evaluate the importance of different attributes on the attractiveness, awareness, and consideration of transit services. The role of traveler attitudes was also extrapolated from these data. Implementation testing was conducted in Salt Lake City to consider practical approaches to incorporating the key findings from this research into transit forecasting efforts. This research was conducted in two phases. The first phase was exploratory and identified the non-traditional attributes that affect traveler choice of mode. This first phase included surveys and analysis in Salt Lake City. The second phase quantified the contribution of the most important attributes to mode choice decisions and sought ways to incorporate the findings into travel models. This second phase included surveys and analysis in Chicago and Charlotte. During the course of the research, it was clear that inaccuracies in transit networks and representation of a traveler’s transit path in the model were limiting the usefulness of the other model improvements. This reality inspired a change in the model implementation portion of the research to consider how to represent characteristics of premium transit ser- vice in transit networks and paths; it also spurred modification of the mode choice model to reflect these characteristics rather than rely on mode or technology labels (e.g., “light rail,” or “express bus”). The innovations in this research provide a new process to incorporate these modal attributes in the transit element of the mode choice model. Key Findings from the Research Several aspects of the travel forecasting modeling system can be improved—based on the findings—to represent premium service attributes. These model improvements are useful because they specifically account for features of any transit service that may be considered “premium” (e.g., stops with shelter, available seating, or proximity to services around the station) regardless of whether these features are part of what would typically be identified as a premium service (e.g., light rail). One important finding of the research is that the combined importance of all premium service characteristics for both commute and non-commute trips was estimated to be between 13 and 29 minutes of in-vehicle travel time. This means that travelers value these premium service characteristics and would pay more or take a longer trip by the equivalent of 13 to 29 minutes in order to use one of these services. Although the combined value of the various premium transit service attributes is significant for all cities and access modes examined during the course of this research, considerable variation exists in the importance of premium service attributes between the different cities, access modes, and individual attributes. Figure 1 presents the details under- lying this finding, for each city and service attribute. Non-traditional attributes also affect the degree to which travelers may be aware of a potential transit option and are willing to consider it for making a journey. Inclusion of awareness and consideration of transit options in mode choice modeling is a relatively new concept. In this research, awareness and consideration were analyzed to understand the influence of these factors on decision-making. Several key findings were derived from

Characteristics of Premium Transit Services that Affect Choice of Mode 3 these analyses. First, many travelers were not aware of or apt to consider transit options that the models represented as available for their trip. Second, travelers were aware of and considered train alternatives more often than bus alternatives. Third, incorporating awareness and consideration into model estimation did improve the statistical fit of the mode choice models. The awareness and consideration models were not tested directly in the implementation phase of the research, but they did contribute to a restructuring of the mode choice models that reduced the number of available transit alternatives. The role of traveler attitudes was evaluated in the context of both awareness and con­ sideration of modal alternatives and modal choice. There is evidence that different attitudes about transportation affect the choice between transit and automobile, but there is no evidence that different attitudes about transportation affect the choice between bus and train. Although the former statement is interesting and supported by other research, it was not the focus of this study and was not given further consideration. Results of the Implementation Testing in Travel Models The implementation phase focused on ways to incorporate premium service character- istics into transit forecasting models. The approach described in this research is just one way to approach implementation; it is recognized that there are many ways to approach this implementation. The results of the test implementation demonstrate that incorporating non­traditional attributes in a travel model is possible and can be used to generate reasonable results. The test implementation succeeded in reducing the influence of the unobserved factors in the mode choice model (these are known as mode or alternative specific constants) by separately representing non­traditional transit service attributes. In addition, basing the alternatives in the mode choice model on transit paths, which were validated against observed behavior instead of pre­defined modal alternatives, allowed for reduction of the dependence on transit-technology-based mode choices (e.g., light rail, bus), which often prove problematic in forecasting. These transit paths were developed to represent traveler preferences for different aspects of the trip, like a shorter walk to transit, a preference for Sc al ed E qu iva le nt M in ut es o f In -V eh ic le T ra ve l T im e Figure 1. Scaled equivalent minutes of in-vehicle travel time for non-traditional transit service attributes.

4 Characteristics of Premium Transit Services that Affect Choice of Mode direct service (no transfers), or a preference for premium services (on-board Wi-Fi, station services, reliable service, etc.). Audience and Use of these Findings The audience for this research includes both travel modelers and transit planners. A concise presentation of the key findings and the information supporting these findings are presented in the final report with minimal technical jargon, making them accessible to a less technical audience. The technical details on methods and results are presented in Appendices A through J, published with the report. These findings may be useful indi- vidually or collectively to improve transit forecasting methods at metropolitan planning organizations (MPOs).