Otchere, Isaac Ramsaw
ORCID: https://orcid.org/0009-0003-2984-9729
(2025)
Leveraging Graph Databases for Multimodal Travel Data
Integration and Analysis.
Masters thesis, Concordia University.
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Abstract
Recent advances in communication, mobility, and sensing technologies have enabled the collection of detailed travel data through smartphones. These advances offer the potential to capture not only traditional trip characteristics such as mode, purpose, and timing, but also real-time contextual feedback from travellers. However, most existing systems focus on post-trip, text-only feedback, overlooking live events that can significantly influence travel time, distance, and user experience.
This research develops and applies a real-time, multimodal feedback framework integrated into the BDMobility app, a bespoke smartphone application designed to link trip records with video, audio, images, text, and documents.
The framework records detailed contextual metadata covering who, when, how, what, and where feedback originates and associates it directly with ongoing trips. The framework’s benchmark assessment demonstrated the significant computational advantage of the graph database (Neo4j) compared to the relational database (PostgreSQL and SQL) with increasing data sizes. Neo4j achieved up to a 90–95% reduction in query latency, maintaining response times below 150 ms at 50,000 records, whereas SQL exceeded 18,000 ms and PostgreSQL averaged around 9,500 ms under equivalent loads. In terms of indexing and scalability, Neo4j processed operations up to 85% faster, with latency stabilizing at under 10 ms, compared to PostgreSQL (60–120ms) and SQL (80–140 ms). CPU utilization further reinforced this trend. PostgreSQL performed
adequately during insertions and retrievals, however, its efficiency decreased significantly at peak utilization with CPU usage exceeding 300% with increasing record sizes. Neo4j consistently used 60–70% less CPU and 40% less memory than SQL during complex graph traversals. Neo4j’s storage footprint decreased by 80% and demonstrated horizontal scalability, validating its suitability for real-time, multimodal, and feedback-enhanced urban transportation analytics.
These findings reiterate that the graph-based system offers enhanced computational performance and establishes a structural and energy-efficient basis for future human-centered, data-intensive transportation system capable of integrating structured and unstructured data at scale through capturing comprehensive travel experiences in large-scale, real-world mobility studies.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Otchere, Isaac Ramsaw |
| Institution: | Concordia University |
| Degree Name: | M.A. Sc. |
| Program: | Quality Systems Engineering |
| Date: | 29 October 2025 |
| Thesis Supervisor(s): | Patterson, Zachary and Farooq, Bilal |
| ID Code: | 996913 |
| Deposited By: | Isaac Ramsaw Otchere |
| Deposited On: | 29 Jun 2026 14:52 |
| Last Modified: | 29 Jun 2026 14:52 |
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