Smartphone Applications To Influence Travel Choices: Practices and Policies

Chapter 4. Transportation Apps and Their Impacts on Traveler Behavior

travel app on a smartphone
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Smartphone apps have a strong influence on the travel choices people make. Much of this influence is economic and social psychological in nature. That is, smartphone apps often deploy psychological, cognitive, emotional, and social mechanisms to influence our economic and non-economic decision making. Transportation apps are no exception: whether it is the convenience of a ridesourcing app, like Uber; transforming the ride-for-hire industry; or expanding gamification opportunities on traffic apps, like Waze to develop ever smarter driver routing, transportation apps are profoundly influencing how travelers interact with the transportation system and traveler behavior. This chapter discusses how transportation apps may impact travel behavior.

There is simultaneously a huge variety of previously identified behavioral economic/social psychological mechanisms and very little specific research on their application in the transportation sector (Metcalfe & Dolan, 2012) (Solof, 2010). This primer identifies the mechanisms believed to be the most impactful and prevalent and describes, with examples, where and how they are deployed in current smartphone transportation apps (as of mid-2015). These mechanisms include:

Cognitive Impacts: How apps mitigate cognitive difficulties and drive new and different usage of the transportation system.
Actual and Perceived Control: How apps give travelers more or more perceived control over their travel choices and experiences.
Privacy Safeguards: The role of data privacy in shaping usage of apps and transportation behavior.
Trust: Limitations imposed on the potential of smartphone apps and transportation services by consumer trust issues.
App Design: How the design and user experience in apps impacts their usage and success in shaping behavior.
Reframing Norms and Defaults about Transportation Choices: Changing what travelers consider the "normal" or "right" choice for a given transportation decision.
Price, Actual Value, and Perceived Value: Impacting how travelers conceptualize and respond to transportation pricing.
Information Availability: How information in various forms, or lack thereof, can shape transportation behavior.
Social Pressure: How our social networks and connections are exploited by apps to change our choices and transportation experiences.
Risk Analysis: The impact of our perception of risk and how smartphone apps mitigate or increase it and thus influence our choices and behavior.
Delivery of Incentives: What smartphone apps do to shift our behavior with deliberate incentives.

Public agencies and governments need to better understand these mechanisms, how they are being deployed, and what benefits and costs they can incur for behavioral change in our transportation system. Each mechanism listed above is explored in greater detail below.

Cognitive Impacts

Smartphone apps are able to mitigate a number of cognitive difficulties, principally by reducing the cognitive effort required to make sense of complex situations and the steps required to achieve specific tasks. This is applicable for both the average user and those with cognitive challenges and disabilities (Szczerba, 2014).

One of the primary offerings of smartphones is always-on, always-connected, and at-your-fingertips convenience. Mobile devices enable users to communicate instantly around the clock, monitor financial markets, track weather forecasts, and get GPS guided directions in real time on an as-needed basis. In transportation, smartphone apps have made it easier to access and sort through complex public transit schedule data (e.g., Google Maps, Transit App, Moovit); alleviated the cognitive burden of route planning, both static and dynamic (e.g., Waze); and made it simpler and easier to pay for and access transportation services (e.g., TriMet ticketing, Zipcar app). Although smartphone apps in general may mitigate some cognitive difficulties, a number of studies have documented the negative impacts of the personal and business use of smartphones on employee productivity and sleep patterns and may be linked to stress and anxiety associated with social networking and compulsive checking for updates (Johnson & Barnes, 2014) (The British Psychological Society, 2012). In the transportation space, a growing body of research highlights the distracting impact of smartphone app usage on driving safety: whether checking social media, texting friends, or simply keeping track of smartphone route directions, studies indicate that the cognitive load involved is a serious distraction from safe and effective driving. Outside of driving safety, negative impacts on cognitive load in transportation choice and usage are unknown.

Apps deliver their cognitive benefits both through the physical nature of any mediated service (as in the case of Lyft or Uber) and through the design of the app itself. Transportation apps often involve search and decision heuristics, and how these are designed can have important impacts on whether or not the app is used and therefore whether or not it changes behavior. A well designed app will improve search heuristics by reducing the cognitive load necessary to make efficient and effective searches. It may also improve decision heuristics by making it easy to sort through options and come to a decision.

Ways in which app designs have been optimized to improve these heuristics include:

Providing users with immediate notification of an application’s status: trip aggregator apps, such as Moovel (formerly RideScout) and Citymapper, quickly take users from a trip plan to a full range of options that could meet their particular travel objectives. This reduces the cognitive load in searching for ride options.
Using a theme and visual language consistent with the offering and brand: ridesourcing apps, like Uber and Lyft, have distinct visual themes that often match their overall brand identity (e.g., sleek and efficient for Uber, more playful and social for Lyft) and help set them apart. Waze uses its icon consistently through the app and employs a cartoonish aesthetic that helps reinforce their primary value proposition as crowdsourcing user data for improved traffic routing. All these mechanisms reduce cognitive load in distinguishing between offerings and set up users for habitual and familiar use of these services.
Preventing errors where possible and assisting users when errors occur: Trip aggregator apps and public transit apps allow users to access public transportation schedules and route itineraries without access to the Internet. For example, Moovit crowdsources information from its app users to ground truth the public transit arrival predictions it receives from public agencies. These features can mitigate the significant cognitive effort necessary to respond to unforeseen failures in a particular journey (e.g., a missed bus or poor Internet connection).
Employing a simple, easy to read, pleasing, intuitive user interface: Regardless of the end objective of a particular transportation app, it is generally agreed that a simple and accessible interface improves user retention. For example, Lyft focuses on the one main goal users are likely to have when using their app, that is, hailing a vehicle. Their app is designed to emphasize the large button at the bottom of the screen that indicates to drivers that a user needs a ride. Lyft understands that the easier it is to request a ride, the more likely they are to see their service used.

While the user interface and user experience research supports the notion that design can significantly impact cognitive load and therefore user success in using an app, little publicly available research has been done on this in the context of the transportation world. Additionally, with an aging population in the developed world, heuristic practices for older adults that account for declining vision, hearing, motor skills, and cognitive function will become of increasing importance to ensure that transportation apps meet the mobility needs of disabled and older populations (Silva, Holden, & Nii, 2014) (Calak, 2013).

Actual and Perceived Control

App designers typically agree on the importance of user experience, interface, simplicity, reliability, and performance to designing a successful smartphone application. However, the actual and perceived user control that an app provides over a task or challenge are often overlooked attributes. Actual control refers to the explicit control an app user gains over some decision making process or experience. The underlying principle of perceived control is that the more a user feels in control, the more satisfied and comfortable they are. In the transportation space, actual control may relate to improving the user’s sense of being on time or knowledge of when they will arrive: trip planning and public transit information apps from Moovit and Moovel (RideScout) through to Routesy and OneBusAway do so through accessing real-time data and providing timely and tailored updates. Perceived control may relate to the sense of material or security comfort while using a transportation service the user gains from employing an app, regardless as to whether or not that comfort is objectively improved. For instance, studies have shown that bus patrons without real-time arrival information perceive wait times as being longer, implying that apps providing real-time information can improve level of satisfaction from bus services (Watkins, Ferris, Borning, Rutherford, & Layton, 2011).

Studies on actual and perceived control benefits associated with smartphones are limited. However, according to one study, smartphone apps that enable riders more control over their trips may be the key to filling information gaps (Latitude, 2011) and by implication to enabling different transportation choices. According to their deprivation study, users that gave up a personal vehicle for one week wanted to be empowered and informed about available public transit options including: routing, electronic schedules, and system delays. Anecdotally, this study suggests that the new crop of informational apps— providing real-time updates on bus arrivals, for example—and innovative transportation service apps— providing access to taxis or ridesourcing within minutes—may be able to provide a level of control over personal transportation options comparable to a privately-owned vehicle. This has important implications for the modal share of drive-alone trips, particularly for commuting when high levels of certainty are required around arrival times. Informational apps that help mitigate the challenges of intermodal trips, particularly the issues of missed connections between multiple public transit services, could also increase public transit use in general.

The social networking capabilities of smartphones, independent of any specific transportation function, may play their own role in changing the transportation experience of travelers. Surveys have shown that "Millennial" demographic users favor smartphone ownership over car ownership because of the connectedness that such technologies enable: having dedicated time to access social networks may be a significant advantage to public transit ridership over driving (Tuttle, 2015) (Nelson, 2013) (Sakaria & Stehfest, 2013).

Privacy Safeguards

With many transportation apps tracking and storing sensitive information, such as user location data, concerns are emerging on how to safeguard user privacy (King & Raja, 2012) (Saif, Peasley, & Perinkolam, 2015), and on how this might impact usage of transportation apps and services. Ajusto, a transportation app released by Desjardins Insurance, tracks driver behavior in return for a promise of car insurance savings. As with many transportation apps that collect sensitive user data, Ajusto has critics worrying about how user location data are being used, such as the potential for data aggregation with other information on a user’s smartphone and the sharing of user data to third parties (CBC News, 2015). Studies have shown, for example, that an individual’s identity can be discovered purely by compiling travel habits in conjunction with other publicly available datasets (Rossi & Musolesi, 2014), (de Montjoye, Hidalgo, Verleysen, & Blondel, 2013).

Community-based navigation app, Waze; public transport app, Moovit; and community-based running and cycling app, Strava, are transportation apps revolving around the collection and analysis of user data. They raise questions about how these data are being shared, particularly as the three apps partner and share data with cities. Currently Waze, Moovit, and Strava only report sharing data that is anonymous and aggregated, but the potential for leaking or misuse remains (Forbes, 2014), and anonymizing/aggregation does not remove the potential to identify users based on detailed location information. To enable greater privacy controls for users and to combat privacy concerns, transportation apps that track location sensitive information, such as Strava, allow users to turn off the tracking in the app’s settings, but it is not clear what happens to the data that may still reside on Strava servers. Typically, the answer may reside in the long and complex terms and conditions that every user implicitly signs but very few read (Gayomali, 2011) (Blodget, 2011).

While privacy concerns are often publicly raised, it is unknown if they have any significant impact on the rate at which these apps are adopted and therefore how transportation choices are made. Revelations from Uber of a "god view" where all vehicle and user locations could be seen from a central web portal (Dent, 2014) or explicit statements that user locations are being deployed as a central part of the app service (e.g., in Moovit’s crowdsourcing feature for public transit arrival times) have not deterred the user bases of either company (70,000 users for Uber in San Francisco alone; (Shontell, 2014) and 10 million worldwide users for Moovit (Moovit, 2015)). It appears that anything other than gross breaches of privacy will not be sufficient to impact app use or app-mediated transportation services, but there is limited research on the subject, at present. It is likely that consumers balance app utility against perceived privacy breaches. In Lyft’s and Uber’s case, the powerful mobility benefits of having access to ridesourcing may in the traveler’s mind outweigh the knowledge that user data are being collected and used for uncertain purposes.

Trust

For a smartphone app to be effective, it usually has to meet the minimum standard of delivering on what is promised. This is particularly sensitive for transportation app providers, who may be fielding data on complex and unreliable public transit arrivals. Often, when there is a failure in real-time arrivals data (the purview of the serving agency), the party that is blamed is the transportation app, even if the app has no actual control over data quality (pers. comm. RideScout 2015).

More generally, having incorrect or missing information on roadway incidents, public transit departures and arrivals, and service availability can all undermine the trust and confidence of the user and may reduce the likelihood they adopt a particular app or service. For consumers using smartphone apps to access a particular product offering, such as a carsharing or microtransit, service reliability is particularly key. An unavailable vehicle or late driver, although again not necessarily the fault of the app itself, undermines the trust and confidence of the program. As such, real-time, reliable, and on-demand availability are the key characteristics contributing to a user’s trust in an application and may be strong factors in how impactful the app is in shaping transportation behavior and choice.

Trust is also about perceived/actual security and comfort in using a service. This is especially important with transportation apps that facilitate some type of ridematching, whether it be a ridesourcing service or casual carpooling. One mobile app, Slugg, has attempted to build trust among unknown carpool drivers and passengers by connecting app users to their company email addresses. Other apps, Lyft and Uber, attempt to build trust among for-hire drivers and passengers through the use of a public ranking system. In both cases, the mutual social screening that goes on can be a significant driver of trust in the safety and comfort of ridematching services.

Reframing Norms and Defaults of Transportation Choices

Mobility apps are increasingly breaking traditional travel norms and instituting new patterns and habits. In the early years of smartphone apps, usage was mostly passive. For example, users might download an app with public transit timetables and reference their smartphone for static schedule information. The advent of high-speed mobile data coupled with GPS location services enabled a new change in travel norms: using smartphones for real-time mapping and mobility data (such as public transit information). Now users can get directions and destination information on a real-time, as-needed basis, breaking the prior norm of having to pre-plan journeys or use other static sources of information like paper schedules or websites.

Advancements in real-time technology now also enable travelers to alter their routing dynamically based on congestion and incident information. For example, the Here Drive+ app allows drivers to record various routes to work (Manticore blog, 2014). The app stores a commuter route, which can then be referenced by the driver in the future for side-by-side route comparisons with estimated travel time and bottleneck locations. Whereas the prior norm might have been that drivers accept being unknowing recipients of traffic congestion or public transit riders accept the inevitability of being subject to delays outside their control, real-time apps are changing these norms by placing more control and information in the user’s hands. It is becoming more normal to plan dynamic trips because there is much greater certainty over available options and travel times due to real-time information.

Advances in mobile ticketing and access apps have impacted the norm of service access. When moving from one city to another, travelers might previously have had to relearn the unique means by which public transit tickets are priced, delivered, and validated in that new location. In addition, ticketing was often viewed as a time consuming transaction involving proper change, a fare agent, or both. The norms of assumed complexity and variability are being replaced now by new mobile ticketing apps in Phoenix, Portland, and Austin (GlobeSherpa, 2015) and elsewhere, which are standardizing and simplifying the process of buying and validating things like bus tickets (Linton, 2015).

Relatedly, there has long been a norm (and reality) of complexity in multimodal/intermodal trips. The challenge in navigating a bikesharing bike to a bus to a carsharing vehicle would, until recently, have defeated all but the most committed traveler. The smartphone and its transportation apps are increasingly enabling seamless door-to-door travel on multiple modes (e.g., Moovel, formerly RideScout). This is creating new informational and transactional norms, with digital wallets and wireless communication capabilities changing expectations for how easy the process of booking and accessing a particular service might be. Innovative transportation services that provide such amenities are increasingly in use (such as the dramatic growth of Lyft and Uber, where no cash ever changes hands) and putting pressure on traditional transportation modes, like public transit and taxis, to adopt and conform to these new expectations.

In recent years, smartphone apps have again caused a re-evaluation of travel patterns and habits by incorporating mobility matching, mobility networking, and real-time data to create a new travel experience. Today, smartphone users can use their devices to match themselves with other travelers to carpool, hire a driver, and rent their personal bicycle or vehicle, changing a number of different behavioral norms:

Carpooling apps are breaking the norm that getting in a car with strangers is necessarily a bad thing. They are doing so by making the matching process easier and improving the verification process by which riders and drivers are matched (Kerr, 2014).
Ridesourcing apps are breaking the norm that you need to be wealthy to have a private driver. Indeed, Uber’s early marketing campaigns revolved around the slogan "Everyone’s Private Driver" (White, 2012). They are doing so by disrupting the taxi industry so that any individual can operate an on-demand vehicle and making it easier and cheaper to access mobility services.
Carsharing and bikesharing are changing norms around ownership of transportation modes. For long periods in the history of the automotive and bicycle industries, owning a vehicle was the primary norm: it was the gateway to mobility. But with the rise of carsharing and bikesharing services (Shaheen & Cohen, 2014) (Shaheen, Martin, Chan, Cohen, & Pogodzinski, 2014) and apps that enable easy access to such services, the norm of ownership is beginning to evolve into a norm of access.

Smartphone apps can also impact defaults and norms through explicit and direct behavioral interventions (for example, rewarding a desirable behavior and punishing an undesirable one). These are distinct from app features of functionality that may change behavioral norms by virtue of their utility. There is limited research on deliberate behavioral interventions by transportation apps, but studies of smartphone apps in other sectors emphasize very few formal techniques for behavioral change, such as goal setting and self-monitoring (Nauert 2015).

Anecdotally, it appears that gamification is one of the primary behavioral interventions deployed in transportation apps. It is likely that apps, such as Waze, which gamify the act of providing transportation information (e.g., reporting an accident), have done so with the deliberate intent of shaping behavior toward the goal of generating more real-time information on traffic incidents. Given that Waze now provides data to multiple cities (Waze, 2015) and to the Google Maps application (Google, 2015), gamification is emerging as an effective intervention to shape behavior. In terms of norms, Waze is changing perceptions of the level of connectedness of individual drivers and the value of their own information. Moovit, a public transit information app, also deploys a form of gamification but in the public transit space. Moovit awards points to users for reporting late arrivals, incidents with vehicles, and other information pertinent to other travelers (Moovit, 2015). How successful this mechanism is in attracting new users or driving public transit ridership is unknown, but Moovit is doing its part to shift norms around the value of any individual traveler’s experience to operation of the wider system.

Transportation app interventions may improve policing and security. The Bay Area Rapid Transit (BART) District, a San Francisco Bay Area transit agency, recently launched an app for policing. This app provides tools for users to report potential criminal incidents on the subway. Such an app may be primarily oriented toward helping the BART police secure the system, but it may also have the behavioral effect of discouraging criminal activity (similar to the effect of having security cameras on public transit (Nieto, 1997)). In the vein of punishing and stopping bad behavior, apps like TowIt (recently launched in several U.S. cities as of mid-2015) seek to empower the majority of the traveling public against a scofflaw minority. TowIt allows users to report double parked or otherwise obstructive vehicles with license plate recognition and an interface for the police or other authorities to review the offenders and presumably take action (CBS San Francisco, 2015). This is a behavioral intervention to punish violators for an actual offense, but it is also attempting to curtail double parking, which remains far too prevalent.

Price and Value Impacting App Adoption and Usage Choices

Transportation apps on smartphones may shape behavior by changing perceptions on price and value. This is in terms of purchase/use cost, actual value for money, and perceived value on both monetary and non-monetary grounds. Transportation apps may both increase awareness of favorable price/value (e.g., mode comparison apps) and actively drive down the cost of a transportation service through a linked transportation mode (e.g., Uber or Lyft). Finally, transportation apps may change the value equation by either providing additional non-monetary benefits or making other non-monetary benefits more prominent to the consumer.

At the most basic level, app purchase price is believed to impact a consumer’s choice on whether or not to use an app. As of 2013, 90% of apps across iOS and Android were free or ad supported—an increase of 84% from 2010 (Gordon, 2013). Clearly, transportation apps that are freely available are likely to be downloaded more. But this is only part of the story.

Public transit apps that convey fare information or aggregator apps that allow comparisons of fares across services (e.g., What’s The Fare (What’s The Fare, 2015)), may act to commoditize transportation in favor of users (driving down prices), as well as shape choice toward cheaper options. The price elasticity of multimodal travel is not well understood, however, and convenience, comfort, trust, safety, and reliability may come above the price of a service in determining choice. Nevertheless, improved pricing information—particularly for complex journeys—can have an impact, and smartphone apps are a highly effective way to deliver this information.

App-based services, like Lyft and Uber, have expanded the on-demand mobility market by competing alongside legacy taxi services, increasing the supply of drivers, and driving down prices (although some of this price depression may well be subsidized by both companies in an attempt to corner market share (Said, 2014)). Such apps and associated services create perceived consumer value through enhanced services (e.g., digital dispatch and on-demand vehicle tracking), making the expense of paying for a private car seem a better value for the money.

Consumer "value" is not always about lower prices or indeed about prices altogether. New companies, such as Leap in San Francisco (now defunct) and Von Lane in Texas (Leap, 2015) (Box, 2014), offer "enhanced" transit services with more comfortable vehicles and amenities like onboard drinks. These are higher priced offerings that provide more "transportation experience" value, which their apps publicize. In the same vein, apps that provide access to environmentally-less costly transportation services, like Scoot Networks (an electric scooter sharing system in San Francisco), may shift behavior toward those modes both by simply making it easier to do the right thing and also publicizing the benefit of doing so to others (Welch, 2009). Apps that offer convenience value (e.g., time savings, access to smartphones while on the move) may be particularly impactful in shaping behavior, even if the financial cost is ultimately greater. Similarly, purely informational apps that make it easier to use public transit services may enhance the perceived value of such services by expanding notions of what trips can be conveniently made on transit, and they may help to dismantle notions that public transit is necessarily unreliable or only for people with no other options.

Information Availability

Transportation of any kind is a complex activity, requiring integration of multiple kinds of information to successfully execute any given trip. This is true of all modes, but particularly so for non-drive alone modes, where having incorrect information or not understanding the complex information available can be a barrier to successfully getting from origin to destination. Imperfect information about alternatives, difficulty comparing alternatives, and challenges with multi-modal integration generally lead to behavioral biases toward particular modes, like driving alone.

Transportation apps act to address this information availability problem in a number of ways:

Schedule information apps, which gather and parse public transit schedule data, static or dynamic, and present it more simply and accessibly for the user. Such apps shape behavior by reducing fear of failed trips using public transit modes and overall cognitive load in configuring such trips. Transit Screen, Transit App, Moovit, One Bus Away, and any number of public and private apps meet this profile.
Mode aggregator apps, which pull in availability and schedule data on multiple different public and private modes, remove the information/awareness barrier to using different modes. Moovel (formerly RideScout) is an example of this approach, but increasingly other public transit information apps, like Transit App and Swiftly, are starting to integrate other modes (Transit App, 2015).
Real-time incident reporting apps seek to raise awareness of issues and problems arising during the journey. Google Maps apps for Android and iOS now offer dynamic re-routing to avoid traffic problems in real time (Torres, 2014); Moovit tracks real-time locations of its users and self-reported incidents to adjust predictions for other users (Moovit, 2015); and apps like CaltrainMe integrate social media feeds to quickly funnel problem reports with train services direct to users (CaltrainMe App, 2015).
Providing price and quality information: Studies suggest that consumer purchase decisions are impacted by three strategies: 1) best value, 2) highest quality of service regardless of price, and 3) price aversion (Tellis & Gaeth, 1990). Best value is choosing the mode with the highest value-to-cost ratio; highest quality of service is selecting the best service regardless of price; and price aversion is selecting the lowest cost alternative to minimize costs. Transportation apps support these three strategies in a number of ways. Numerous aggregator and trip planning apps (e.g., Moovel (formerly RideScout), Google Maps) communicate cost and service information (including anticipated trip time) so a best value decision can be made, as well as supporting a simple price aversion search where the lowest cost service is sought regardless of quality. The wide variety of apps providing access to specific niche services, some more luxurious than others (e.g., Von Lane, Uber, Bridj, Chariot, Via), assist those seeking the highest quality of service regardless of price, with aspects of their design and branding signaling the higher service quality.

It is arguable that the first and best role of transportation apps changing behavior is through their information provision functions. However, with the predominance of transportation information being held publicly and the majority of transportation apps being developed by private companies, a data transfer gap exists that can limit app success. If real-time data are not available for a given service, few apps can address this, although efforts by Moovit and others to develop crowdsourcing tools can help to remedy gaps.

Social Pressure

Facilitating connections into, and awareness of, social networks and social messaging are bedrock features of the smartphone app ecosystem, whether transportation-focused or otherwise. Some of the most successful deliberate behavioral change interventions in the energy, environment, and infrastructure sectors have involved leveraging social dynamics (Opower, 2015). This is with good reason: people are likely to adjust their behavior in response to social psychological mechanisms (Schultz, Nolan, Cialdini, Goldstein, & Griskevicius, 2007) (Lindenberg & Steg, 2007). Transportation apps may be leveraging and deploying these mechanisms in a number of ways.

Community building is an effective and widely used mechanism to exert social pressure and shape behavior. For example, Waze makes users feel as though they are part of a unique and exclusive community by deploying instruments to convey a strong identity. This might entail customizing cartoon avatars for each user or the ability to "like" other user’s recommendations. By showing the locations and avatars for each user on a live map that all users see, Waze is deploying a mechanism called "social proof" (Cialdini, 2006), where a particular behavior is encouraged by providing examples of other people already doing so. Both the community building and social proof arguably increases trust in Waze’s navigation recommendations, which may in turn feedback into a greater propensity on the part of individuals to supply more information about their own driving experience.
Both Waze and other crowdsourced data apps, like Moovit and GasBuddy, deploy competition and status seeking to drive desired behavioral change. Reporting incidents on these apps can lead to accumulation of points and/or status of some kind (perhaps a new avatar that signifies seniority in the community). In some cases, these apps provide leaderboards as a competitive gamification mechanism to further increase engagement.
The urge to conform to established social norms can be a powerful motivator. This force is evident in the mutual ratings systems deployed in a number of transportation apps, particularly those where trust and security are a concern. Ridesourcing services, such as Lyft and Uber, allow passengers to rate their driver and drivers to rate their passengers (Uber, 2015). The driver star rating is presented to passengers and used by Lyft and Uber to weed out underperforming or problematic drivers. The passenger rating is presented to drivers when they are agreeing to pick up a hail request, and they have the option to reject if the star rating is too low. These generate subtly different but nonetheless firm social pressures to be a "good" participant in the ecosystem. Such mutual pressures not only ensure smooth operation of the service and a great ride for customers, but they may also attract additional users to the service given their implied promise of a trustworthy and safe experience. It is important to note that improving safety (e.g., biometrics, licensing) across these platforms is a separate issue from the psychological issues addressed through encouraging trust on the platforms through star ratings.
A variant of conforming to social norms is the use of shadow policing as deployed by carsharing services, like Zipcar and car2go. This takes the form of a requirement to report vehicle damage at the beginning of a carsharing reservation (Zipcar, 2015). It provides a strong incentive to users to both avoid blame for damage by making an early report and to avoid damaging the vehicle in the first place since the chance of being reported by the next user is high. While this mechanism was deployed long before smartphone apps arrived, apps for both services are now easing this reporting and making it easier to track the condition of a vehicle before booking (car2go, 2015).
Some apps are generating entirely new social norms and using them to attract users. The PlugShare electric vehicle services app encourages private owners of electric vehicle charging stations to share their equipment with other users of the PlugShare app. This is currently a fee-free service, but PlugShare encourages beneficiaries to leave tokens of thanks, such as gift cards or a bottle of wine (Tesla, 2015). This new norm encourages existing sharers to continue providing free electricity, and other PlugShare users to join the community.

Social psychology is rich with heuristics and interventions that purport to change human behavior in response to social cues or pressures. Transportation apps have barely scratched the surface of what might be possible, but success with Lyft’s and Uber’s mutual ratings system and Waze’s gamified community, for example, suggest notable future potential.

Delivery of Incentives

Incentives, rewards, and loyalty programs are a long-standing mechanism for changing behavior. Retailers have long issued "punch cards" to reward loyalty and repetitive behavior. Increasingly, smartphones applications are using both broadcast and segmented incentives in an effort to impact behavior, often through gamification. Psychologists and marketing professionals have argued that the closer a user gets to a reward, the more motivated they become to follow through (Wax, 2015). Transportation apps deploy a wide range of incentives.

Taking perhaps the simplest approach—financial incentives/rewards for specific desired behaviors—both ridesourcing and e-Hail taxi apps regularly deploy ride credits for downloading, using and sharing their services. These typically range from $5 to $25. Uber, for example, offers a bonus of the same magnitude to third-party transportation apps integrating Uber’s services through their Application Programming Interfaces (APIs), as a reward for attracting new users onto Uber’s platform. They have also advertised a tiered pricing incentive for existing drivers to encourage heavier use of their service by drivers. Incentives are also widely used outside of on-demand ride services. Parking apps, like BMW ParkNow and ParkingPanda, offer discounted parking for users of the service. The GasBuddy app (helps drivers find locations of gas stations and provides prices) enters users into a drawing for $100 in free gas, if they report a local gas price (Gas Buddy, 2015).

A variant of the classic financial incentive is to offer points or non-monetary currency, much as airlines have offered for decades through frequent flyer programs. The GasBuddy app offers non-monetary currency by rewarding users with 200 points for posting and updating a gasoline price. Accumulated points may be redeemed to participate in prize raffles. The Changers app helps reduce a user’s carbon dioxide (CO2) footprint by tracking their transportation-related CO2 emissions (Markham, 2014) and uses a virtual "green currency" as a proxy for personal carbon offsets. By tracking a user’s trips, travel modes, and distance, the Changers app calculates a CO2 value for each journey and then adds or subtracts "ReCoins"—a virtual currency within the app from the user’s balance. By forecasting a CO2 balance for the trip, the app awards or penalizes ReCoins based on whether or not a user saves more CO2 than the forecast estimates for that trip. The currency can be used to "buy" offsets for other trips that have higher CO2 emissions. More generally, transportation app usage of points are typically categorized into one or more of four types (Zichermann, 2011):

Experience points: Points are awarded for simply participating on a regular basis in the app. Waze and Moovit are prime examples of awarding experience points for basic interactions. Such repeated awards create habits of using the app and associated services.
Redeemable points: These points are awarded (or purchased) in-app and redeemable within the app ecosystem. These are common for airline frequent flyer programs and are also used by Amtrak and some rental car companies. Not surprisingly, they have considerable potential for keeping users entirely inside a given transportation service. They have also been deployed by public-minded transportation apps, such as NuRide, Stanford University’s CAPRI, and now just beginning with Metropia, to reward both transportation alternatives to driving alone and congestion avoidance when driving.
Skill points: Such points are awarded for the completion of a particular task in-app or using the service. An example is GasBuddy, which awards specific blocks of points for reporting gas prices. Waze has a complex system of points awarded for different tasks, such as "resolve 50 map problems" and "complete 500 map edits" (Waze, 2015). While not explicitly involving points, some services, like Scoot Networks, in San Francisco progressively reveal app functionality in return for users completing important training and qualification tasks (like learning how to ride an electric scooter).
Reputation points: The mutual rating systems provided by Lyft, Uber, and other ridesourcing companies through their smartphone apps are a form of reputation points, forming the basis of mutual trust in their services.

Incentives in the form of discounts on third-party services or products are a common motivator across retail and advertising. Transportation apps and services are also starting to explore their potential. Public transit agencies, like TriMet, are experimenting with pushing location-aware promotions to encourage ridership growth on new and existing transportation lines. In Montreal, the STMMerci public transit app rewards riders with location and use-targeted discounts from 340 enrolled merchants and 1,000 event partners to reward riders based on how often they use public transportation. Top-tier riders are offered a 50% discount on a product or service, second-tier riders receive a 30% discount, and third-tier riders receive a 10 to 15% discount. Non-currency incentives can be just as effective as monetary ones when delivered directly, and better yet, they can compound the benefits of monetary incentives when concurrently provided. Indeed, many of the other mechanisms discussed in earlier sections are non-financial.

Conclusion

It is clear that behavioral mechanisms from economics and psychology are already being deployed widely in transportation apps, with a variety of benefits. Some of these benefits include:

Alleviating cognitive burdens with powerful search tools (e.g., Google Maps);
Improving actual and perceived traveler control over journeys (e.g., OneBusAway);
Improving trust in carpooling services (e.g., Carma);
Changing norms around transportation, such as the ease of mobile ticketing (e.g., RideTap formely GlobeSherpa);
Impacting price directly by enabling cheaper services (e.g., Uber) and changing perceptions of value across multiple modes (e.g., Moovel, formerly RideScout);
Improving information availability and so shaping service usage (e.g., Transit App);
Harnessing existing social pressures and generating new ones to shape travel behavior in a desired direction (e.g., Waze); and
Delivering financial and non-financial incentives in favor of one behavior or another (e.g., GasBuddy, Changers).

Sometimes these economic and social psychological mechanisms are deployed for the sole benefit of the app developer. More commonly, however, these mechanisms are driving both app usage and positive benefits to the consumers and the wider transportation system. For example, Waze’s multiple use of gamification mechanisms and its popularity led to an acquisition by Google and the use of their data by a number of major U.S. cities (Olson, 2014). Public agencies have not been as successful as private app developers in fully benefiting from their apps or the data that they make available for private apps. For example, public transit agencies open up their real-time arrivals data, and both the apps and users benefit, but it may also be worthwhile considering ways to bring benefits back to the agencies (perhaps in the form of sharing real-time usage data to help operators improve services).

The wide usage of behavioral economics and social psychological principles in apps suggests the importance of understanding the theory and practice of behavioral change in the context of smartphone apps for transportation. Additional work would help to test some of the hypotheses and build on anecdotal evidence that successful behavioral change mechanisms from other sectors can be usefully deployed to change transportation behavior. The next chapter reviews opportunities (e.g., planning and data collection) and challenges (e.g., privacy and digital divide), as well as best practices for deploying smartphone apps.

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