Saferoute: An Adaptive Road Travel Safety Monitoring System

Saferoute: An Adaptive Road Travel Safety Monitoring System

Mrs. Harini A

AP-CSE Department of Computer Science and Engineering, Nehru Institute of Engineering and Technology

Mahadi Wasif.N

Department of Computer Science and Engineering, Nehru Institute of Engineering and Technology

Abinesh.R.S

Department of Computer Science and Engineering, Nehru Institute of Engineering and Technology

Muhammed Rabbanee.M.S

Department of Computer Science and Engineering Nehru Institute of Engineering and Technology

Vinothini.V

Department of Computer Science and Engineering Nehru Institute of Engineering and Technology

Abstract – The proposed system, SafeRoute, presents an adaptive road travel safety monitoring framework designed to automatically detect suspicious travel behavior and verify user safety before triggering emergency alerts. Unlike traditional safety applications that rely solely on manual SOS activation, SafeRoute uses a rule-based, threshold-driven monitoring algorithm that continuously evaluates GPS location, movement speed, route deviation, battery status, and network conditions. The system dynamically adapts safety thresholds based on selected transport mode (walking, bike, or car) to reduce false alarms. A multi-stage verification mechanism ensures that alerts are escalated only after confirmation failure. The system also integrates secure PIN-based deactivation and passive post-arrival monitoring to enhance protection. The proposed framework aims to improve proactive road travel safety while maintaining reliability and minimizing false positives.

1. INTRODUCTION

   Road travel safety has become an increasing concern due to rising incidents of accidents, forced vehicle stops, and emergency situations during solo travel. Most existing safety applications depend on manual SOS triggers, which assume that the user is capable of actively requesting help. However, in many critical situations, the user may be unable to activate emergency alerts due to panic, unconsciousness, device seizure, or forced shutdown.

   To address these limitations, SafeRoute introduces an adaptive and automated safety monitoring system. The system continuously monitors travel behavior using GPS and device-level parameters. Instead of immediate escalation, it follows a structured multi-stage verification process to confirm user safety before alerting trusted contacts. This approach enhances reliability, reduces false alarms, and ensures proactive safety monitoring during road travel.

2. LITERATURE REVIEW

   Litrature review content

   Several existing safety and tracking systems primarily focus on manual emergency activation and real-time location sharing. Research in rule-based monitoring systems highlights the importance of threshold-based detection mechanisms for anomaly identification. Location tracking applications commonly use GPS-based monitoring but lack multi-stage safety verification logic.

   Studies in context-aware systems demonstrate that adaptive thresholds based on user activity can significantly reduce false positives in detection systems. Additionally, event-driven state-machine architectures are widely used in safety-critical systems to manage monitoring, verification, and escalation stages.

   However, most travel safety applications lack integrated transport-mode adaptation, battery shutdown intelligence, secure deactivation control, and post-destination passive monitoring. SafeRoute aims to combine these mechanisms into a unified adaptive safety framework.

3. EXISTING SYSTEM

   Existing safety applications primarily offer:

   • Manual SOS activation
   • Continuous location sharing
   • Basic alert messaging
   • Emergency contact notification Limitations of existing systems include:
   • Dependence on user-initiated alerts
   • Lack of automated suspicious behavior detection
   • No transport-mode adaptive thresholds
   • No battery behavior analysis
   • No secure deactivation mechanism
   • High false alarm rates

   These limitations reduce the reliability and effectiveness of emergency response systems during critical situations.

4. PROPOSED SYSTEM

   SafeRoute proposes a rule-based adaptive safety monitoring system structured into four major stages:

   1. Continuous Monitoring
   2. Suspicious Condition Detection
   3. Multi-Stage Safety Verification
   4. Alert Escalation

      The system continuously tracks:

      • GPS location
      • Speed and movement
      • Route consistency
      • Battery status
      • Network condition

   Transport mode selection (Walking, Bike, Car) dynamically adjusts inactivity thresholds to match travel context. If abnormal behavior is detected, the system initiates a two-stage verification process before escalating alerts.

   Secure PIN-based deactivation prevents forced disabling of safety mode. After reaching the destination, passive monitoring continues briefly to detect unexpected movement.

   1. System Architecture

      The system architecture consists of the following modules:

      1. User Interface Layer (Login, Journey Setup, Monitoring Dashboard)
      2. Monitoring Engine (GPS, Battery, Network tracking)
      3. Detection Engine (Threshold-based rule evaluation)
      4. Verification Engine (Multi-stage safety confirmation)
      5. Alert Module (Notification to trusted contacts)
      6. Cloud Backend (Firebase Realtime Database)
   2. Software Requirements
   • Firebase Realtime Database Cloud Data Storage
   • Firebase Authentication User Authentication
   • GPS API Real-Time Location Tracking
   • Notification API Alert Generation
   • Battery & Network Status APIs Device Monitoring
   • Figma UI/UX Prototyping

   No training dataset is required as the system uses deterministic rule-based logic.

5. CONCLUSION

SafeRoute presents an adaptive and automated approach to road travel safety monitoring. By combining rule-based detection, transport-mode adaptation, and multi-stage verification, the system enhances reliability while minimizing false positives.

Continuous monitoring of GPS location, route deviation, battery behavior, and network status enables proactive identification of suspicious situations. The integration of secure PIN-based deactivation and passive post-arrival monitoring

further strengthens user protection.

The use of deterministic logic ensures low computational overhead and practical deployment on mobile devices. Overall, SafeRoute provides a scalable, intelligent, ad proactive solution for improving travel safety and emergency responsiveness.

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