Safe Systems for Road Safety: A Human Factors Perspective
A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Social Science".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 26
Special Issue Editors
Interests: transportation safety and operations; data-driven analyses; predictive modeling
Interests: highway safety; active transportation; context-sensitive solutions; naturalistic driving; human factors
Interests: road safety; driver behavior; statistical modeling; before–after studies; countermeasure evaluation
Special Issue Information
Dear Colleagues,
The Safe Systems approach to road safety focuses on designing a transportation system that anticipates and accommodates human error, rather than solely relying on individual drivers to be perfectly safe. This approach recognizes that humans make mistakes and are vulnerable to injury, and, therefore, the system itself should be designed to minimize the risk of crashes and reduce the severity of injuries when crashes do occur.
This Special Issue seeks contributions that establish a scholarly foundation for a Safe System approach for road safety from a human factors perspective. This Special Issue aims to provide an opportunity for transportation researchers and practitioners to share their theoretical and practical knowledge and findings in the field, with the goal to examine the entire transportation system and identify the systemic factors that contribute to road safety. Contributions should focus on the following areas:
- Human factors and behavior;
- Crash data and predictive analyses;
- Road infrastructure design;
- Speed management strategies;
- Vehicle safety technology;
- Traffic incident management;
- Emergency response systems;
- System integration and responsibility sharing;
- Equity and accessibility.
Dr. Meghna Chakraborty
Dr. Anshu Bamney
Dr. Nischal Gupta
Dr. Amrita Goswamy
Guest Editors
Manuscript Submission Information
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Keywords
- safe systems approach
- road safety
- human factors
- data-driven analyses
- predictive modeling
- vulnerable road users
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