National Academies Press: OpenBook

Business Models for Mobile Fare Apps (2020)

Chapter: Chapter 6 - Conclusions and Future Research

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Page 62
Suggested Citation:"Chapter 6 - Conclusions and Future Research." National Academies of Sciences, Engineering, and Medicine. 2020. Business Models for Mobile Fare Apps. Washington, DC: The National Academies Press. doi: 10.17226/25798.
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Page 62
Page 63
Suggested Citation:"Chapter 6 - Conclusions and Future Research." National Academies of Sciences, Engineering, and Medicine. 2020. Business Models for Mobile Fare Apps. Washington, DC: The National Academies Press. doi: 10.17226/25798.
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Page 63
Page 64
Suggested Citation:"Chapter 6 - Conclusions and Future Research." National Academies of Sciences, Engineering, and Medicine. 2020. Business Models for Mobile Fare Apps. Washington, DC: The National Academies Press. doi: 10.17226/25798.
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Page 64

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62 Conclusions and Future Research This chapter presents overarching conclusions and numerous areas for future research. Overview The objective of this study was to identify different business models and technologies used by American and Canadian transit agencies for mobile fare payment apps. To meet these objectives, a three-part methodology was used. First, a review of prior literature pertaining to mobile fare payment apps was conducted. Second, an e-mail survey of transit agencies and operators that have mobile fare payment apps in the United States and Canada was conducted, and a total of 62 responses were received. Third, detailed case examples were collected via telephone interviews with representatives from six transit agencies. The agencies selected for the case examples had different approaches to mobile fare payments, and their approaches were compared and categorized to identify different business models. Based on this methodology, five different business models for mobile fare payment apps were identified. They are briefly summarized as follows. 1. Shared App: In the shared app model, multiple transit agencies in different regions use the same mobile fare payment app provided by a single vendor. This model is quick to implement and low in cost; a shared app can be configured in a few days to add specific fare types and some limited agency branding. However, this model typically does not include integration with preexisting fare payment systems, and validation of fares is typically done only by visual inspection. The survey results presented in Chapter 3 revealed that many small-sized transit agencies were using this model; the case example demonstrating this model was the City of Santa Monica’s Big Blue Bus. 2. White Label App: This model is referred to as white label because the app is developed by a vendor but rebranded to look as if it were made by the transit agency. A white label is relatively quick to deploy, comparatively low cost, and allows for configuration including specific fare types and agency branding. When offered as a stand-alone system, white label apps are usually not integrated with preexisting fare payment systems, and they typically rely only on visual inspection for fare validation. Based on the survey results from Chapter 3, it appears that this model caters to medium- to larger-sized transit agencies. The case example from Chapter 4 that best fits this model is Denver’s RTD. 3. White Label App with Validation Hardware: This model is similar to the previous one, except it also includes validation hardware such as readers installed on transit vehicles. An additional vendor is typically part of the contractual process to facilitate hardware installation and integration. The costs are usually higher, and the deployment time may be longer. Based on the survey results from Chapter 3, this model is typically used by larger C H A P T E R 6

Conclusions and Future Research 63 transit agencies with higher levels of ridership; however, there appears to be less consistency between transit agencies employing this approach. The case example from Chapter 4 that best fits this model is Austin’s CapMetro. 4. Open Payment App: This model applies to fare payment systems that are both standards- based (commonly called open payment) and account-based. Riders download the transit agency’s mobile fare payment app, which can be used to manage transit accounts by reloading value or purchasing passes. Transit accounts can be loaded into mobile wallets (e.g., Apple Pay, Google Pay) using virtual cards. Fare products can be validated in different ways, such as tapping NFC on the user’s phone at readers. Because this is still an emerging model and is usually part of a fully integrated system, costs are currently high; however, this could change in the future if other agencies adopt this model. The CTA is the case example for this model. 5. SDK Only: The last model is an SDK-only approach in which only an SDK is procured from a mobile fare payment app vendor. Then, the SDK can be integrated into other smartphone apps, such as real-time information apps. This model appears to have relatively low costs (both upfront and ongoing). In this model, validation is done visually by drivers, and there is typically no integration with the transit agency’s preexisting fare payment system. The case example from Chapter 4 that best fits this model is St. Catharines Transit Commission. No other transit agencies that responded to the survey presented in Chapter 3 were using this model; however, this appears to an emerging approach that could be used by small, medium, or possibly large transit agencies in the future. Another trend pertaining to mobile fare payment apps that was also identified is increased integration with other smartphone apps, such as real-time transit information or ridesourcing services. This is typically done using one of the following three methods: (1) deep links between applications, in which one app redirects users to another app; (2) APIs, which are sets of communication protocols for building software applications; or (3) SDKs, which include APIs as well as additional libraries, documentation, or tools. Future Research Numerous areas for future research have emerged from this study. They are summarized as follows. • Heavy Rail Systems: Although there are limited examples of mobile fare payment apps used in heavy rail systems in the United States and Canada, there are numerous urban heavy rail operators in Europe and Asia that accept mobile payments. Research about these inter- national agencies and operators would allow for better understanding of operational models. • Validation Technologies: Additional research on mobile fare payment solutions using Bluetooth and NFC technologies would be of use. Numerous respondents to the transit agency survey conducted as part of this synthesis stated that electronic validation technologies were being considered for future implementation in their mobile fare payment systems, and therefore, this is likely to be a fruitful area for additional research. • Accessibility: Accessibility of mobile fare payment apps only received limited treatment in this study; a single survey question asked transit agencies about app features to increase accessibility, such as large font and high contrast. Future research about additional features to increase accessibility of apps would be useful. • Fare Capping: A small number of transit agencies responding to the survey and participating in the case examples offered fare capping through their mobile fare payment app. However, fare capping may become a more frequently offered fare policy option as transit agencies transition to account-based systems, and therefore, fare capping and other new fare policies are topics for more detailed investigation in the future.

64 Business Models for Mobile Fare Apps • Business Arrangements for SDKs: Some of the case examples in this study highlighted agencies that had worked with vendors to create APIs or SDKs for mobile fare payments that could be integrated into other apps, such as transit information apps or ridesourcing apps. This trend is likely to increase, and therefore, future research could consider conver- gence of apps in the transit and shared mobility space. Future research could also investi- gate the roles and responsibilities of vendors providing APIs and SDKs. • Customer Adoption of Mobile Fare Payment Apps: In this study, the survey participants reported estimates of the adoption of mobile fare payment apps by their customers, such as the percentage of unlinked trips made using the mobile fare payment app. Future research that analyzes customer adoption levels in more detail would be beneficial—such as the type of fare media that customers were using previously. • Fare Evasion: Fare evasion by passengers utilizing mobile fare payment apps is a related topic that received only limited treatment in this study. A single survey question in this study explored this topic, and therefore, future research could be beneficial. • Future Changes: Because of the significant changes that have happened in the area of mobile fare payment apps in the 5 years prior to this study, it is anticipated that the business models presented in this synthesis will continue to evolve in the short and long term. In light of this, future research could help transit agencies adapt as mobile technology continues to change at a rapid pace.

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Five different business models for mobile fare payment apps are examined, as the world of apps used by transit agencies in the United States and Canada continues to steadily grow.

The TRB Transit Cooperative Research Program's TCRP Synthesis 148: Business Models for Mobile Fare Apps documents current practices and experiences of transit agencies that offer mobile fare payment applications to transit riders.

The report includes case examples from six cities: Santa Monica, Denver, Austin, Chicago, Dallas, and Ontario, Canada.

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