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Related Work

Similar Apps​

Waze​

Overview:
Waze is a community-driven navigation app that relies on real-time user input to detect and report traffic conditions, accidents, hazards, and road closures. It leverages crowdsourcing and GPS data to provide accurate, up-to-date route information.

Key Features:

  • Real-time crowd-sourced traffic updates (accidents, hazards, police presence, etc.)
  • Dynamic route optimization based on live traffic conditions
  • Community interaction and gamification to encourage user participation
  • Integration with Android Auto and Apple CarPlay

Strengths:

  • High responsiveness to real-world events
  • Strong community engagement model
  • Efficient routing through continuous data updates

Limitations:

  • Relies heavily on active user participation
  • Data may be inconsistent in low-traffic areas
  • Focused primarily on user reports, not infrastructure-based data

Relevance to the Project:
Waze demonstrates the power of real-time alert dissemination and user participation. However, our project aims to extend this concept by incorporating infrastructure-based sensor data (e.g., weather stations, radars, cameras) for higher reliability and automation in emergency alerts.

TomTom​

Overview:
TomTom is a GPS navigation system that provides route guidance and traffic monitoring using a combination of sensor data, map databases, and partnerships with local traffic authorities. It focuses on accuracy and reliability rather than user-generated data.

Key Features:

  • High-precision maps and routing algorithms
  • Real-time traffic information from official data sources
  • Integration with vehicle infotainment systems
  • Predictive routing based on historical traffic patterns

Strengths:

  • Reliable and verified traffic data
  • Strong integration with automotive platforms
  • Consistent performance in areas with limited user activity

Limitations:

  • Limited interactivity and community feedback
  • Less flexibility in reporting unexpected incidents
  • Proprietary and less open compared to crowdsourced solutions

Relevance to the Project:
TomTom illustrates the effectiveness of sensor-based and authoritative data collection. Our project can combine this reliability with the flexibility and immediacy of user-based alerts, offering a hybrid approach to intelligent traffic management.

Digital Twins for Mobility (DT4MOB)​

The Digital Twins for Mobility (DT4MOB) project is a national initiative focused on developing a mobility-oriented digital twin for urban and interurban environments.
It integrates IoT, AI, and data-driven modelling to create a real-time virtual representation of transportation systems, supporting simulation, prediction, and decision-making for sustainable and intelligent mobility.

Role in Our Project​

Our project leverages the data infrastructure and models provided by DT4MOB to deliver real-time and predictive functionalities to drivers.
The sensor network deployed by the Telecommunications Institute of Aveiro (ITAv) along the region of Aveiro including radars, weather stations, and other monitoring devices, feeds live data into DT4MOB’s digital twin environment.

By accessing this data, our application can:

  • Receive real-time information about road and weather conditions,
  • Identify and disseminate emergency or hazard alerts,
  • Support predictive awareness, such as anticipating congestion or adverse conditions,
  • Enable coordinated driving maneuvers through vehicle-to-infrastructure (V2I) interaction.

Using DT4MOB as our data backbone ensures that our application operates on reliable, standardized, and continuously updated information, enabling accurate, data-driven decision support for drivers and vehicles.

Learn more about DT4MOB here

Tutors:

Group:

Institution: Telecommunications Institute of Aveiro (ITAv)