A Domain Ontology For Safety of Road Users – SafeOn: Overview & Design
Road traffic incidents are a matter of global concern. The accident data plays a significant role in prevention and accident management and getting deeper insights for designing better policies and guidelines. Effective management of the collected accident information should ensure the effective utilization of the information so that multiple outputs can be created and used in various types of safety analysis. We are developing SafeOn – a domain ontology for road user safety ontology. SafeOn is developed for India and uses a bottom-up approach in Protégé and OWL. Our proposed Safety Ontology (SafeOn) organizes accident for detailed analysis of various aspects of road safety. It offers features of interoperability to integrate with crash data of other departments like healthcare, and road design for better insights, policy decisions, and accident prevention plans. We instantiated SafeOn with the accident case of Cyrus Mistry. We integrated the accident information with SafeOn and generated the knowledge graph. This graph is capable of speedy analysis of road accidents and can efficiently answer all the questions related to that accident. SafeOn is useful for road safety departments to design better policies, resolve problems of missing information in hit-and-run crashes, and ensure the saving life of road users.
Authors
- Alok Nikhil Jha
- Geetam Tiwari (ORCID: https://orcid.org/0000-0002-9302-1243)
- Niladri Chatterjee (ORCID: https://orcid.org/0000-0003-3832-4003)
- Rahul Gupta (ORCID: https://orcid.org/0000-0002-2024-3478)
Institutions
- Indraprastha Institute of Information Technology Delhi (IN)
- Goldman Sachs (United States) (US)
- Indian Institute of Technology Delhi (IN)
Publication Details
- Journal
- Transportation research procedia
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.trpro.2024.12.164
- Citations
- 3
- Primary Topic
- Human-Automation Interaction and Safety
- Type
- article
- Field-Weighted Citation Impact
- 7.32