The paratransit sector in developing urban centers, such as the tricycle industry in Tuguegarao City, often suffers from fare inconsistencies and a lack of operational transparency due to reliance on manual negotiations. This study addressed these challenges by developing and validating the Trike-O-Meter (TOM), an IoT-based system for real-time monitoring and dynamic fare estimation. Utilizing an applied developmental research design, the study integrated a Geofencing 2.0 paradigm and the Haversine formula to automate fare triggers based on localized regulatory boundaries. Empirical results from field
testing demonstrated high technical precision, with a mean route deviation error (RDE) of 4.59 m and a mean absolute error (MAE) of 0.60 Philippine pesos in fare computation, successfully meeting all pre-defined accuracy thresholds. Furthermore, the system achieved an "Excellent" System Usability Scale (SUS) score of 84.5 and a 96.2% Task Completion Rate, indicating strong social acceptability among both drivers and passengers. Theoretically, this research contributes to the literature on Location-Based Services (LBS) by establishing a robust "predictive containment" model to mitigate GPS drift in high-density areas.
Practically, the TOM system provides a scalable blueprint for Local Government Units (LGUs) to professionalize informal transport sectors, ensuring economic fairness and standardizing urban mobility governance.
Table of Contents
1. INTRODUCTION
1.1 Research Objectives
1.2 Significance of the Study
1.3 Related Works
2. MATERIALS AND METHODS
2.1 Research Design
2.2 Technical Architecture and Spatial Logic
2.3 Participants and Sampling
2.4 Data Collection Instruments
2.5 Procedural Flow
2.6 Data Analysis
2.7 Materials
2.8 System Flowchart
3. TESTING AND EVALUATION
3.1 Fare Computation and Distance Accuracy Metrics
3.2 Algorithmic Fare Modeling
3.3 Spatial Tracking and Route Precision
3.4 Functional Reliability and Geofencing Performance
4. RESULTS AND DISCUSSION
4.1 Evaluation of Post-trip Tracking Accuracy
4.2 Impact of Route Complexity on Tracking Precision
4.3 Discussion and Implications of Tracking Performance
4.4 Evaluation of Fare Calculation and Estimation Accuracy
4.5 Distance-Based Fare Variance Analysis
4.6 Discussion and Implications of Fare Integrity
4.7 Evaluation of Overall System Effectiveness and Usability
4.8 User Satisfaction and Qualitative Feedback
4.9 Discussion on Human-Computer Interaction (HCI) and Social Impact
5. Conclusion
6. Recommendations
Objectives & Topics
This study focuses on resolving fare inconsistencies and the lack of operational transparency in the informal paratransit sector of developing urban centers, specifically examining the tricycle transport system in Tuguegarao City. The primary objective is to engineer and empirically validate the Trike-O-Meter (TOM), an Internet of Things (IoT)-based mobile tracking and dynamic fare estimation platform with post-trip tracking capabilities that automates fare computations according to municipal regulatory matrices while mitigating GPS drift and positional inaccuracies.
- Development of an IoT-enabled mobile architecture integrating GPS telemetry, Firebase Realtime Database, and cross-platform UI frameworks.
- Implementation of Geofencing 2.0 and the Haversine formula for state-based spatial boundary transitions and accurate distance measurement.
- Algorithmic modeling of dynamic fare calculations adhering to Land Transportation Franchising and Regulatory Board (LTFRB) guidelines, including multi-passenger cost-sharing.
- Empirical validation of spatial tracking accuracy, endpoint precision, and waypoint capture rates across varying route topologies.
- Evaluation of user satisfaction, operational task completion, and socio-technical impact utilizing the System Usability Scale (SUS) and ISO 9241-11 frameworks.
Excerpt from the Book
Technical Architecture and Spatial Logic
The technical framework of the TOM system utilized a multi-layered architecture designed for high-precision spatial analysis. The core of the distance computation relied on the Haversine formula, which factorized the Earth's spherical shape to reduce discrepancies in the "Great-Circle" distance measured between consecutive GPS coordinates. The algorithm was augmented with a "Geofencing 2.0" protocol that moved beyond traditional binary entry-exit triggers. Instead, the system utilized state-based transitions and temporal constraints to detect tricycle movement through predefined urban zones, automatically adjusting fares in accordance with the local government unit (LGU) fare matrix. The software stack was optimized to handle concurrent multi-passenger inputs and real-time traffic-aware rerouting, ensuring that the dynamic fare estimation remained responsive to environmental variables and route density.
The operational logic of the Trike-O-Meter (TOM) system is governed by a dual-pathed functional workflow that synchronizes passenger requirements with driver availability through a centralized cloud-based authentication gate. Upon application launch and role-based sign-up, the system branches into distinct modules: the passenger interface prioritizes spatial accuracy by requesting GPS permissions to automate location input or reverting to manual entry to ensure a valid booking sequence, which includes real-time route optimization and fare estimation. Simultaneously, the driver interface manages fleet readiness via an online toggle and a pending-request queue, which includes a critical capacity-validation node to prevent overloading before a trip is transitioned to the "Accepted" state. The trip execution phase is monitored for anomalies via an integrated issue-reporting mechanism, ensuring that the process from "Start Trip" to "Payment" and final "Post-Trip Approval" is seamless, transparent, and capable of generating the digital audit trails necessary for standardized paratransit governance.
Summary of Chapters
INTRODUCTION: Establishes the operational challenges in paratransit mobility, outlines the research objectives, details the significance of automated fare monitoring for drivers and commuters, and reviews related literature on IoT tracking and geofencing systems.
MATERIALS AND METHODS: Details the applied developmental research methodology, system architecture, spatial computational logic using the Haversine formula and Geofencing 2.0, participant sampling of drivers and passengers, software stacks, and procedural testing workflows.
TESTING AND EVALUATION: Formulates the mathematical and statistical metrics used to evaluate the prototype, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Route Deviation Error (RDE), Path Similarity Index (PSI), and geofence boundary detection matrices.
RESULTS AND DISCUSSION: Presents empirical findings from field trials across urban, suburban, and rural routes, demonstrating high spatial precision, negligible fare errors, excellent System Usability Scale (SUS) ratings, and positive human-computer interaction outcomes.
Conclusion: Summarizes the key achievements of the study, confirming that the TOM prototype successfully achieved its technical benchmarks, mitigated GPS drift, eradicated pricing disputes, and established a scalable model for paratransit modernization.
Recommendations: Outlines strategic avenues for future development, including multi-constellation GNSS integration, traffic-intensity surcharge algorithms, municipal smart city policy integration, longitudinal behavioral studies, and cyber-physical security protocols.
Keywords
Smart Transportation, Geofencing 2.0, Paratransit Modernization, Internet of Things (IoT), Dynamic Fare Estimation, Location-Based Services (LBS), Urban Mobility Governance, GPS Accuracy, Haversine Formula, Human-Computer Interaction (HCI), Route Deviation Error, System Usability Scale
Frequently Asked Questions
What is the primary focus of this research paper?
The paper focuses on the development, field implementation, and empirical validation of the Trike-O-Meter (TOM), an IoT-based tracking and automated fare estimation system created to modernize informal paratransit networks, specifically tricycles in Tuguegarao City.
What are the central thematic areas explored in the document?
The central themes include smart urban mobility, mobile application architecture, IoT-enabled spatial tracking, algorithmic fare modeling adhering to municipal regulations, and human-computer interaction in public utility transport.
What specific problem does the Trike-O-Meter aim to solve?
It addresses the lack of pricing transparency, arbitrary overcharging, passenger-driver fare disputes, and the absence of verifiable trip records inherent in traditional, negotiation-based paratransit operations.
Which scientific and technical methodology was employed?
The authors utilized an applied developmental research design combining hardware-software prototyping (C#, .NET MAUI/UraniumUI, Firebase) with empirical field testing across 10 distinct routes evaluated by 20 purposively sampled drivers and commuters.
What is covered in the main evaluation section of the study?
The evaluation covers quantitative assessments of spatial tracking fidelity (RDE, PSI, endpoint error), fare calculation accuracy (MAE, RMSE, Error Percentage), geofence detection rates, and user experience metrics under ISO 9241-11 standards.
Which key technologies define the algorithmic core of the system?
The core computational system relies on the Haversine formula for geodesic distance calculation, Geofencing 2.0 state-transition logic for boundary-triggered fare adjustments, and a proportional multi-drop cost-sharing algorithm.
How accurately did the TOM system perform during empirical field trials?
The system achieved a mean Route Deviation Error (RDE) of 4.59 meters, an endpoint accuracy of 2.35 meters, a Mean Absolute Error (MAE) in fare calculation of only ₱0.60, and a 100% geofence identification accuracy.
What usability results were recorded from drivers and commuters?
The system achieved an "Excellent" System Usability Scale (SUS) score of 84.5, a 96.2% Task Completion Rate, an average task completion time of 14.5 seconds, and an overall user satisfaction rating above 4.6 out of 5.
How does the system mitigate the effects of GPS drift in dense urban environments?
Through its "predictive containment" model and temporal state-based geofence logic, the software applies a logic gate buffer that validates route segments before committing distance measurements to the fare engine, preventing signal fluctuations from artificially inflating trip fares.
- Quote paper
- Canelin Raquel (Author), 2026, Trike-O-Meter (TOM): Real-Time Monitoring and Dynamic Fare Estimation for Tricycle with Post-Trip Tracking Mechanism, Munich, GRIN Verlag, https://www.grin.com/document/1764112
