Research Article
CADAPPAR: An Integrated and Modular System for Crash Data Collection and Analytics
@INPROCEEDINGS{10.1007/978-3-032-15154-4_18, author={Awa Tiam and Ibrahima Gueye and Oumar Niang}, title={CADAPPAR: An Integrated and Modular System for Crash Data Collection and Analytics}, proceedings={Innovations and Interdisciplinary Solutions for Underserved Areas. 8th International Conference, InterSol 2025, Ouagadougou, Burkina Faso, July 3--4, 2025, Proceedings}, proceedings_a={INTERSOL}, year={2026}, month={11}, keywords={n-tier architecture road crash on-site data collection collaborative collection distributed database containerization offline-first}, doi={10.1007/978-3-032-15154-4_18} }- Awa Tiam
Ibrahima Gueye
Oumar Niang
Year: 2026
CADAPPAR: An Integrated and Modular System for Crash Data Collection and Analytics
INTERSOL
Springer
DOI: 10.1007/978-3-032-15154-4_18
Abstract
The present paper describes an integrated and modular computerized system for road crash data collection. Unlike the commonly used 2-tier architecture, this system leverages a 4-tier architecture including a mobile application for on-site data collection, a synchronization server, a distributed NoSQL database for data storage and a web application for data analysis and visualization. The system makes use of containerization to ensure scalability, ease of deployment and fault-tolerance. The offline-first approach ensures resiliency on connectivity restrained areas. The system incorporates a collaborative approach that allows multiple data collectors to contribute seamlessly. To evaluate its effectiveness, Proof of Concept tests, simulated scenarios, and resiliency assessments were conducted. The results highlight the system’s efficient offline-first synchronization and its adaptability to diverse deployment environments. Collaboration tests showed that enabling multiple collectors reduced data collection time by 50%. However, the evaluation also revealed key insights regarding real-world deployment challenges and the need for comprehensive performance benchmarking.