Gamified In-Ride Interactions

• Gamified in-ride interactions are structured applications of game mechanics enacted during transit journeys to enhance engagement, promote behavior change, and support learning outcomes.
• They employ layered game dynamics, adaptive challenges, and real-time sensing modalities such as BLE and physiological monitoring to drive interactive experiences.
• Implementations in public transit, exergaming, and rideshare contexts demonstrate improved usability, adherence to target zones, and shifts in social perceptions, underscoring multidisciplinary impact.

Gamified in-ride interactions are structured applications of game elements and mechanics, deployed during real or virtual journeys, that aim to transform passenger or rider engagement, behavior, and learning outcomes. These systems typically exploit contextual triggers—such as physical location, physiological state, or social context—to deliver real-time feedback, challenges, or narratives, with diverse objectives ranging from enhancing public transport uptake to promoting socially responsible consumption and physiological self-regulation. Current deployments in public transit, virtual exercise, and rideshare settings incorporate a range of technical architectures, motivational frameworks, and evaluative metrics tailored to the affordances and risks of in-motion environments.

1. Game Elements, Dynamics, and Components for In-Ride Contexts

Gamified in-ride systems employ a stratified structure of game dynamics (overarching psychological constructs), mechanics (motivational processes), and components (tangible objects or feedback). Cardoso et al.’s MARGe system for public buses presents a representative example. Its core elements include:

• Dynamics: Progression is manifested through visible “Awards” and “Player Level,” with narrative turns each bus journey into a multi-stage adventure.
• Mechanics: These leverage explicit rewards (badges unlocked after quiz completion), resource acquisition (UI “Easter eggs”), and challenges (location-triggered checkpoints plus quizzes).
• Components: Points are awarded or deducted in quizzes (with a floor at zero to avoid demotivation). Badges and achievements act as trophies. Predefined quests and adventures are mapped to real bus lines, global/friends leaderboards track point totals, and “Levels” accumulate per total score (Cardoso et al., 2020).

Hein et al. integrate non-player character (NPC) avatars as dynamic, physiology-adaptive game elements. For example, NPCs provide zone-based real-time feedback by mirroring user effort: alignment within the heart-rate target zone keeps the avatar alongside the rider; deviation causes the avatar to pull ahead or lag, with visual cues for correction. This supports embodied feedback loops between exertion and game performance (Hein et al., 18 Aug 2025). In rideshare settings, interactive fiction, time-based challenges, trivia, and open-ended dialog prompts structure the game space to reveal latent labor dynamics and foster dialogue between drivers and passengers (Hsieh et al., 13 Dec 2025).

2. Technical Infrastructure and Sensing Modalities

Technological underpinnings for gamified in-ride interactions vary by domain and use case, but share a reliance on real-time environmental or physiological sensing and cloud-based synchronization.

• Location Sensing: MARGe employs Estimote iBeacons (Bluetooth Low Energy) for passive, sub-30 m ranging. Experiments established that a single Proximity beacon per bus can robustly detect a rider’s presence and trigger contextual game stages (broadcast rates of 7.5–10/minute; minimal signal dropout) (Cardoso et al., 2020).
• Physiological Sensing: Hein et al.’s exergame pipeline streams raw ECG at 130 Hz from a Polar H10 strap to a Python server (Lab Streaming Layer), uses Hamilton’s algorithm and a 3–45 Hz FIR filter in NeuroKit2 for heart-rate (HR) detection, and provides processed HR data over TCP to Unity 3D, which adapts VR visualizations in real time (Hein et al., 18 Aug 2025).
• Backend and UI: MARGe leverages a React Native frontend and a Firebase (NoSQL JSON) backend for real-time adventure tracking, authentication, and cross-device progression. Gamified rideshare prototypes are implemented in Figma for prototyping and rating but highlight the importance of in-ride resource constraints (e.g., ride duration, mobile compatibility) in deployment decisions (Hsieh et al., 13 Dec 2025).

3. Interaction Workflows and Adaptation Logic

Interaction sequences in gamified in-ride systems are tailored to balance engagement with the strictures of being in motion or co-present with others.

• Public Transit Adventures: Riders initiate an adventure in the app, which subscribes to BLE beacon triggers. Each adventure interleaves location-detection, information, quiz, and navigation screens. Stage completion and quiz success drive progression, score accrual, and badge unlocking—synchronized to the cloud for leaderboard updates (Cardoso et al., 2020).
• Physiology-Adapted Exergaming: Users pedal on a smart trainer while a VR environment adapts in real time. Eight visualization archetypes were tested, but the adaptive NPC (avatar) approach achieved the strongest adherence to HR zones. The avatar's offset is linearly mapped to the absolute deviation from the target HR zone:

$$d_i = \operatorname{sign}(HR_i - \tfrac{\underline{z}+\overline{z}}{2}) \times \min\left(\frac{\delta_i}{\overline{z}-\underline{z}} D_{\max}, D_{\max}\right)$$

where $HR_i$ is current HR, $[\underline{z}, \overline{z}]$ is the target zone, and $D_{max}$ is the maximum offset. Zone computation uses Tanaka’s formula for $HR_{max}$ and standard five-zone partitioning (Hein et al., 18 Aug 2025).

• Rideshare Passenger Engagement: Games are launched via Figma prototypes and conducted in workshops. “Driven!” uses narrative branching; “Trivia Ride” intermixes factual and general knowledge questions, sometimes requiring live driver collaboration for hints or context; open-dialog activities scaffold boundary-crossing communication. Drivers can enable or disable high-interaction options to preserve agency and safety (Hsieh et al., 13 Dec 2025).

4. Evaluation Methodologies and Empirical Results

Gamified in-ride interaction systems are typically evaluated using a blend of usability metrics, psychometric scales, and task-based performance indicators, appropriate for constrained in-ride usage.

• Usability and Engagement (Public Transit): MARGe's tasks yielded mean completion times of 15–98 seconds for individual actions and ~5 minutes for a full adventure. Error rates remained low, and System Usability Scale (SUS) scoring placed the system at the 83rd percentile (“A” grade) (Cardoso et al., 2020).
• Physiological Outcomes (Exergaming): In a lab study (N=18), adaptive NPC feedback resulted in users spending 74.9% (SD=2.0%) of time in the target HR zone versus 65.5% (SD=2.1%) for random NPCs; normalized HR was also steadier and higher than baseline. Subjective exertion and enjoyment ratings did not differ significantly with gamification, but intrinsic motivation decreased for random (non-adaptive) avatars (Hein et al., 18 Aug 2025).
• Knowledge Shifts and Attitude Change (Rideshare): In nine workshops (19 drivers, 15 passengers), post-intervention surveys showed that passenger estimates of driver take-home pay dropped from ~50% to ~30%, appreciation of deactivation risk increased, and empathy metrics rose, especially after “Driven!” and collaborative trivia. Willingness to support labor rights increased in several sessions. Game prototypes were specifically ranked for fun, replayability, content embedding, and driver-passenger interaction (Hsieh et al., 13 Dec 2025).

5. Design Principles and Guidelines

Research highlights several critical design principles for effective and ethical gamified in-ride interactions.

• Passive Contextualization: Location or physiological triggers should be automatic, requiring no scanning or manual input, to avoid user friction and safety risks (Cardoso et al., 2020).
• Layered Motivation: Systems should balance intrinsic motivators (narrative, progression) with extrinsic incentives (points, badges, leaderboards) to maximize broad engagement (Cardoso et al., 2020).
• Short, Modular Content: Games must fit within 5–20 minute ride durations; narratives and challenges must be easily paused or resumed (Hsieh et al., 13 Dec 2025).
• Obfuscated Serious Content: Transformational and persuasive elements should be interwoven playfully to minimize resistance and maintain engagement (Hsieh et al., 13 Dec 2025).
• Agency, Consent, and Safety: Especially in rideshare contexts, drivers must control level of passenger interaction, and games should respect safety and social boundaries. No distracting interactive gameplay for drivers while in motion (Hsieh et al., 13 Dec 2025).
• Immediate Feedback and Resources: Immediate correction and scoring, streaks, or rewards promote learning and reduce demotivation (e.g., quiz scoring floors at zero) (Cardoso et al., 2020).

6. Application Domains and Prospective Research Directions

Gamified in-ride interaction systems are being deployed and studied across diverse domains:

• Public Transportation: To increase ridership and engagement by transforming journeys into interactive adventures that layer educational, exploratory, and competitive elements on routine commutes (Cardoso et al., 2020).
• Active Mobility and Exergaming: To facilitate physiological self-regulation, especially in VR cycling or fitness training, through real-time, embodied feedback (NPCs, environmental cues), intended to maintain safe and effective exercise intensities (Hein et al., 18 Aug 2025).
• Rideshare Social Advocacy: To surface hidden labor and algorithmic management conditions to passengers, fostering empathy, accurate knowledge, and political engagement through narrative or collaborative gameplay (Hsieh et al., 13 Dec 2025).

Research identifies a set of ongoing challenges and future research vectors, including the need for:

• Comparative studies on physiological computation methods for adaptation thresholds (Hein et al., 18 Aug 2025).
• Longitudinal deployments to assess sustained behavioral change or intrinsic motivation (Hein et al., 18 Aug 2025, Hsieh et al., 13 Dec 2025).
• Cross-cultural replication and customization for variable regulatory and cultural norms in public transport or gig work (Hsieh et al., 13 Dec 2025).
• Integration with official app backends or city infrastructure for at-scale impact assessment (Cardoso et al., 2020, Hsieh et al., 13 Dec 2025).
• Advanced simulation platforms that both immerse and inform, for policy or training contexts (Hsieh et al., 13 Dec 2025).

7. Societal and Ethical Considerations

Emerging research underscores the importance of grounding gamified in-ride interactions in factual accuracy, agency, and privacy:

• Content must be apolitical, statistically robust, and transparently sourced (“ground-truth answers”) to avoid misinformation or over-simplification, especially in domains of labor relations and health (Hsieh et al., 13 Dec 2025).
• Customization options and settings interfaces enable participant control over social exposure and content depth, a crucial requirement for rideshare safety and comfort (Hsieh et al., 13 Dec 2025).
• Narrative and mechanic design should leverage psychological distancing and intermixing to enable engagement with sensitive or emotionally charged topics in a safe yet effective manner (Hsieh et al., 13 Dec 2025).
• Regular and diverse usability testing in real operational conditions is necessary to uncover edge-case errors and unanticipated social or technological impacts (Cardoso et al., 2020).

In sum, gamified in-ride interactions represent a robust interdisciplinary domain, integrating ubiquitous computing, affective computing, serious game design, and social computing to address key challenges of engagement, behavior change, and justice in contemporary mobility systems (Cardoso et al., 2020, Hein et al., 18 Aug 2025, Hsieh et al., 13 Dec 2025).