Overview
Cognitive City Navigator is an experimental system that explores how semantic reasoning, graph structures, and neural representations can be combined to support intelligent decision-making in urban environments.
Instead of treating a city as a collection of static routes and locations, the system models it as a living knowledge graph—where transportation, points of interest, and contextual signals interact dynamically. The goal is not just navigation, but context-aware guidance that adapts to user intent and real-world constraints.
Motivation
Traditional navigation systems expose users to fragmented information: static timetables, live vehicle maps, and separate weather or POI searches. This fragmentation creates decision fatigue, especially in unfamiliar cities.
Cognitive City Navigator investigates how a semantic abstraction layer—built on top of raw transport and geospatial data—can reduce this cognitive load by answering higher-level questions like: “What is the best decision to make right now, given my intent and the city’s current state?”
System Architecture
The system is designed as a modular pipeline with three core components:
1. Semantic Representation Layer (Knowledge Graph)
At the core lies a Neo4j knowledge graph that models the city as interconnected entities rather than isolated records. Examples of modeled relationships include:
- Routes operating on stops
- Walkable proximity between stops
- Points of interest located near transit nodes
This graph structure enables relational queries such as smart transfers, proximity-based recommendations, and multi-hop reasoning across the transport network.
2. Neural Retrieval & Inference
Unstructured user intent is processed using sentence-level embeddings to capture semantic meaning beyond keyword matching. These embeddings are used to:
- Match user intent to relevant routes, areas, or POIs
- Rank candidate decisions based on semantic similarity
- Bridge natural language queries with structured graph data
This hybrid approach combines neural flexibility with graph precision.
3. Context-Aware Application Layer
The application layer integrates static transport data (GTFS), real-time vehicle feeds (GTFS-RT), and contextual signals such as weather. A lightweight inference pipeline dynamically injects graph-derived context into a language model, allowing the system to generate structured, situation-aware recommendations rather than free-form text.
Key Features
- Semantic City Graph: A labeled property graph encoding transport topology, walkability, and spatial proximity.
- Context-Aware Recommendations: Suggestions adapt to real-time conditions such as weather, live vehicle positions, and user location.
- Hybrid Reasoning Pipeline: Neural embeddings for intent understanding combined with symbolic graph traversal for constraint satisfaction.
- Real-Time Responsiveness: Live vehicle data is continuously ingested and filtered to reflect the city’s current state.
Technical Stack
Design Takeaways
- Knowledge graphs provide interpretability and structure that pure neural systems lack.
- Neural embeddings excel at translating ambiguous human intent into machine-usable signals.
- Combining symbolic and neural approaches enables more robust urban reasoning.
- Cities are better modeled as systems of relationships, not flat datasets.
Future Directions
- Graph-based learning (GNNs) for predictive congestion modeling.
- Deeper multimodal integration (bikes, scooters, pedestrian flows).
- More explicit uncertainty modeling in recommendations.