Urban Underground Logistics Systems (UULSs): A Hybrid Approach to Risk Assessment
Urban underground logistics systems (UULSs) are emerging as a promising solution to address increasing congestion, reduce surface traffic, and optimize last-mile delivery in densely built-up areas. By transporting goods through subterranean tunnels, these systems can significantly enhance urban mobility and environmental sustainability. However, operating UULSs in complex urban environments presents unique challenges and risks, including structural stability, utility interference, safety hazards, and system reliability. Accurate risk assessment is critical to ensure safe, efficient, and resilient operations, particularly in areas with high population density and intricate infrastructure networks. Traditional risk assessment methods often struggle to capture the inherent uncertainties and interdependencies of such systems, which may lead to incomplete or overly simplistic analyses.
To address these challenges, this study proposes a hybrid methodology that combines fuzzy logic, Bayesian networks, and machine learning techniques for comprehensive risk assessment of UULS operations in built-up areas. Fuzzy logic allows the modeling of ambiguous and subjective expert judgments, while Bayesian networks provide a probabilistic framework to quantify causal relationships among risk factors. Machine learning algorithms complement these approaches by identifying complex patterns and predicting potential failures from operational data. By integrating these techniques, the proposed framework offers a robust and adaptive tool for evaluating, prioritizing, and mitigating risks, ultimately supporting the safe deployment and sustainable operation of urban underground logistics systems.
Hybrid Risk Assessment Framework for Urban Underground Logistics Systems
This flowchart outlines a hybrid methodology for the comprehensive risk assessment of Urban Underground Logistics Systems (UULSs).
Flowchart Steps
1. Input Data & Expert Knowledge
- Operational data from UULS
- Expert judgments on risks (structural, safety, utility conflicts)
2. Fuzzy Logic Layer
- Handles uncertainty in expert opinions
- Converts qualitative assessments into quantitative risk scores
3. Bayesian Network Layer
- Models causal relationships between risk factors
- Calculates probabilistic risk propagation and interdependencies
4. Machine Learning Layer
- Learns patterns from historical and real-time operational data
- Predicts potential failures and risk escalation
5. Risk Evaluation & Prioritization
- Combines outputs from all three layers
- Generates a ranked list of critical risks
6. Decision Support & Mitigation Strategies
- Provides actionable insights for planners and operators
- Supports risk mitigation, resource allocation, and safe UULS operations
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