Multi-dimensional Road Safety Evaluation Model
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Solution Overview
Problem
Current road safety analysis models fail to comprehensively consider the correlation between macro and micro levels, and assume constant safety risk exposure, which is not accurate as it changes with motor vehicle annual average daily traffic.
Innovation Solution
A method and system for evaluating road safety by constructing safety evaluation models for sub-regions, using historical traffic data to quantify safety risk exposure, and building road and region safety quantification sub-models to account for various influencing factors, including population density, road network density, and traffic data, to perform a comprehensive safety evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If road safety analysis models are constructed at only one dimension (macro or micro level), then the model construction is simpler, but the analysis results have deviation and lack comprehensiveness
Solution Approach 1:
The patent divides the road safety analysis system into two distinct levels: macro-level analysis (region safety quantification sub-model) and micro-level analysis (road safety quantification sub-model). Each level operates independently with its own model structure and parameters, allowing comprehensive multi-dimensional analysis while maintaining manageable complexity at each segment. The macro model evaluates regional safety based on aggregate traffic data, while the micro model assesses individual road safety based on specific road characteristics.
Solution Approach 2:
The patent implements a nested model structure where the micro-level road safety models are embedded within the macro-level regional safety model. The region safety quantification sub-model incorporates and aggregates results from multiple road safety quantification sub-models within the same region. This nested architecture allows the system to simultaneously perform detailed road-level analysis and comprehensive regional analysis, with results flowing from micro to macro levels, thereby achieving both precision and comprehensiveness without proportionally increasing overall system complexity.
2Measurement precision
If constant safety risk exposure is assumed, then the analysis model is simpler, but the evaluation results are less accurate as safety risk exposure actually changes with motor vehicle annual average daily traffic
Solution Approach 1:
The patent transforms the static assumption of constant safety risk exposure into a dynamic model where safety risk exposure varies with motor vehicle annual average daily traffic. The model incorporates traffic volume as a variable parameter that directly influences safety risk exposure calculations. By making safety risk exposure dynamic rather than constant, the model accurately reflects real-world conditions where changing traffic volumes affect safety outcomes, thereby improving evaluation accuracy while accepting the necessary increase in model complexity.
Data Source
AI summary
The present invention discloses a method and system for evaluating road safety based on multi-dimensional influencing factors, and relates to the field of road safety technologies. Based on historical traffic data and corresponding safety influencing factors, safety evaluation models in different dimensions are respectively constructed, and road safety risk exposure is classified flexibly. The safety evaluation models in macro and micro dimensions are linked by using a constraint function, and influence mechanisms of the safety influencing factors are determined respectively. Specifically, a safety evaluation model is constructed and obtained for each sub-region in a limited region range. The safety evaluation model is applied to obtain influencing factors of safety of each traffic road in the sub-region, and safety evaluation is performed on the sub-region. Through the technical solutions of the present invention, an accurate, comprehensive, objective method for evaluating road safety that reflects authentic influence data is provided, which has a wider application scope.