面向地铁突发事件的行车调度系统人误预测研究
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摘要
摘要：
近年来,随着地铁列车运行控制系统技术的发展,行车调度系统自动化程度越来越高,降低了由行车调度员失误引发事故的可能性,但与此同时,当地铁突发事件发生后,由于行车调度员处理不当而导致事故发生或结果恶化的问题日益凸显。行车调度系统是地铁安全运营的重要保障,行车调度员则是地铁突发事件下系统的最终保护和恢复机制,如何有效地避免和减少地铁突发事件中行车调度系统人误是各国地铁亟需解决的安全问题。为此,本文以国家高技术研究发展计划(863计划)“列车运行综合优化控制技术”为背景,从分析地铁行车调度系统人误致因出发,提出研究地铁突发事件下行车调度系统人误预测的两个关键方面——人误行为风险预测和人误诱发因素风险预测,提出基于场景的突发事件人误行为识别、预测指标定级和人误行为风险评价技术,同时提出考虑因果作用的人误诱发因素风险预测技术,并形成与提出的人误预测技术相匹配的规范化的行车调度系统人误数据采集和管理方法,为地铁行车调度系统人误预防与减少提供理论方法和技术支撑,具有重要的理论与实际应用价值。
首先,对行车调度员突发事件处理任务和行车调度系统人机交互特点进行了详细分析,在此基础上构建了地铁行车调度员人误机理模型,从人误行为和人误诱发因素两个方面对人误机理进行描述,先后基于层次任务分析法和m-SHEL模型建立了人误行为分析模型和人误诱发因素识别模型,形成了以多资源理论为基础的行车调度员人误模式分类框架和以Jae W. Kim研究为基础的行车调度系统人误诱发因素备选库,并以国内某地铁公司1997-2011年间的98份行车调度员突发事件处理的人误分析报告为对象,通过灰色关联分析、数据挖掘技术和评分者信度评估验证了人误模式分类框架、人误诱发因素分类、人误模式分析模型和人误诱发因素识别模型的合理性和正确性。
针对行车调度系统人误数据缺乏而引起的无法直接或间接计算、估计和预测人误率的问题,参考硬件故障模式、影响及危害性分析技术提出了以人误行为可能性、可恢复性和后果严重性为指标的人误行为风险预测模型,并结合行车调度系统人误模式分类框架确定了人误行为可能性等级划分标准；结合系统人误屏障的特点确定了人误行为可恢复性等级划分标准；结合地铁事故管理规定确定了人误行为后果严重性等级划分标准,形成了对人误行为风险进行评价和定级的度量图和风险度量矩阵。
考虑到人误行为风险预测模型定量化的需要,参考硬件故障模式发生概率等级的评分准则和系统人误屏障的失误率减少作用确定了人误行为可能性和可恢复性指标的定量化定级标准,研究了基于贝塔分布的定量化人误数据的采集和估计方法,解决了人误率数据采集难的问题,设计并开发了基于典型任务的ATS模拟实验系统,通过视线追踪技术对定量化人误数据进行采集,完成了对采集方法的验证,为行车调度系统定量化人误数据收集提供思路。
在建立的人误行为风险预测技术上研究突发事件场景人误行为风险的识别方法。根据行车调度员突发事件处理任务可模块化的特点,建立了基本任务模块库及相应的突发事件人误场景生成技术,以人误场景构建规则为对象,分析和确定了突发事件场景的失误后果严重性等级、可恢复性等级、可能性等级和风险等级的计算规则,以及关键人误行为识别技术,并通过对接触轨断电突发事件人误风险预测实例验证了方法的实用性。
在人误诱发情景风险预测方面,充分考虑人误诱发因素间因果关系,以及因果关系而导致的其对系统人误的综合和整体效应的变化情况,构建了以模糊认知图来进行描述的人误诱发情景图模型,引入模糊认知图在因素关系推理、权重计算方面的理论成果,形成人误诱发情景影响效应的评价技术,完成了对某地铁公司人误诱发情景风险预测的实例分析。为了保证构建的图模型的正确性,采用证据理论和不一致性判断方法对专家判断进行综合,同时引入小样本数据筛选技术提高了图模型构建的效率。
最后,根据建立的行车调度系统人误预测技术特点,对地铁行车调度系统人误数据需求和不确定性进行了详细分析,建立了规范化的人误数据采集方法。
ABSTRACT:
In recent years, with the development of the subway train operation control system technology, the degree of automation of traffic dispatching system is higher and higher, which reduces the probability of accident caused by the traffic dispatcher human error. But at the same time, the problem of accident or consequence deterioration leading by the traffic dispatcher human error in emergency is increasing. Traffic dispatching system is an important guarantee of the subway safety, while the traffic dispatcher is the system's ultimate protection and recovery mechanism under the emergency. How to effectively avoid and reduce the subway traffic dispatching system human error under emergency is an urgently security problem for the world subway companies. Therefore, relying on the National High Technology Research and Development Program863of 'Integrated Optimizing Control Technology for Train Operation', this thesis analyzes the subway traffic dispatching system human error causing, and proposes two key research aspects of human error behavior risk prediction and human error forcing factor prediction. Thereby, the scenario based emergency human error behavior identification technique, prediction indexes grading technique and human error risk assessment technique are constructed. At the same time, the human error forcing factor risk prediction technique based on the causal relations between factors is buit, and the normalized human error data collecting and management method based on the proposed human error prediction technique is also built to provide the theory method and technical support for subway traffic dispatching system human error prevention and reduction. Research on the problem of traffic dispatching system human error prediction has important theoretical and actual application value.
Firstly, a subway traffic dispatcher human error mechanism model is constructed based on the detail analysis of the emergency processing task and the human interaction features of the subway traffic dispatching system, which describes traffic dispatcher human error mechanism in two aspects of human error behavior and human error forcing factors. Then, a human error mode analysis model and a human error forcing factors identification model are built based on the hierarchical task analysis method and the m-SHEL model respectively, and the related traffic dispatcher human error mode classification framework and the human error forcing factors optional library are also developed by modifying the multi-resource theory and Jae W. Kim's human error forcing factors set respectively. Furthermore, the created models and classification framework's validation are tested and verified by grey correlation analysis, data mining and intra-rate reliability of98emergency human error analysis reports of1997-2011from a domestic subway company.
In order to solve the problem that the subway traffic system human error probability is difficult to calculate, estimate and predict both in direct and indirect way because of the lack of human error data, a human error behavior risk prediction model is proposed by referring to the failure mode, effects and criticality analysis technique, which estimates the human behavior risk based on the three aspects of human error behavior possibility, restorability and consequences seriousness. Thereby, the human error behavior possibility grading standard is determined according to the traffic dispatching system human error mode classification framework, while the human error behavior restorability grading standard is built by considering the human error barrier character, and the human error consequences seriousness grading standard is established with the subway accident management regulations. Also, the measurement figure of criticality matrix and risk assessment matrix are constructed for human error behavior risk calculating and grading.
Considering the requirement of human error behavior risk prediction model's quantification, the human error behavior possibility and restorability quantification grading standard are built referring to the failure mode probability grading standard and traffic dispatching system human error barrier's error probability reduction degree. As a support, the difficulty of quantification human error data collection is also solved by introducing Beta Distribution into human error data collection and estimation method. And an ATS experiment simulator based on the traffic dispatcher's typical tasks is designed and developed to collect the qualification human error data by the eye tracking technique. The experimental result verifies the rationality of the data collection method and provids the idea for the subway traffic dispatching system quantification data collection.
Based on the established human behavior risk prediction technique, the emergency scenario human error risk prediction method is studied. In this study, the emergency human error scenario generation technique is created according to the modularized characteristics of traffic dispatcher emergency processing task, and the emergency scenario human error behavior consequences seriousness, restoration, possibility, risk degree calculation rules and key human error behavior identification technique are analyzed and identified on the basis of the human error scenario building rules. Additionally, an example of the contact rail power off emergency human error risk prediction is used to verify the practical applicability of the structured emergency scenario human error risk prediction method.
In the aspect of human error forcing context risk prediction, the causal relations between human error forcing factors and the changes of the human error forcing context comprehensive and the overall effect caused by the causal relations are taken into full account. By the time, the human error forcing context graph model described by the fuzzy cognitive map is built. Consequently, the human error forcing context effect assessment technique is formed by introducing the fuzzy cognitive map's research achievement in causality reasoning and factors weight calculating, and is used to complete the example analysis of a subway company's traffic dispatching system human error forcing context risk prediction. In order to ensure the constructed graph model's validity, evidence theory and inconsistency judgment method are utilized to synthesize and evaluate the experts judgment. Meanwhile, a small sample data screening technique is introdued to improve the graph model construction efficiency.
Finally, the subway traffic dispatching system human error data requirements and uncertainty are analyzed in detail according to the structured human error prediction technique characteristics of traffic dispatching system. Then, a normalized human error data collecting method is established.
近年来,随着地铁列车运行控制系统技术的发展,行车调度系统自动化程度越来越高,降低了由行车调度员失误引发事故的可能性,但与此同时,当地铁突发事件发生后,由于行车调度员处理不当而导致事故发生或结果恶化的问题日益凸显。行车调度系统是地铁安全运营的重要保障,行车调度员则是地铁突发事件下系统的最终保护和恢复机制,如何有效地避免和减少地铁突发事件中行车调度系统人误是各国地铁亟需解决的安全问题。为此,本文以国家高技术研究发展计划(863计划)“列车运行综合优化控制技术”为背景,从分析地铁行车调度系统人误致因出发,提出研究地铁突发事件下行车调度系统人误预测的两个关键方面——人误行为风险预测和人误诱发因素风险预测,提出基于场景的突发事件人误行为识别、预测指标定级和人误行为风险评价技术,同时提出考虑因果作用的人误诱发因素风险预测技术,并形成与提出的人误预测技术相匹配的规范化的行车调度系统人误数据采集和管理方法,为地铁行车调度系统人误预防与减少提供理论方法和技术支撑,具有重要的理论与实际应用价值。
首先,对行车调度员突发事件处理任务和行车调度系统人机交互特点进行了详细分析,在此基础上构建了地铁行车调度员人误机理模型,从人误行为和人误诱发因素两个方面对人误机理进行描述,先后基于层次任务分析法和m-SHEL模型建立了人误行为分析模型和人误诱发因素识别模型,形成了以多资源理论为基础的行车调度员人误模式分类框架和以Jae W. Kim研究为基础的行车调度系统人误诱发因素备选库,并以国内某地铁公司1997-2011年间的98份行车调度员突发事件处理的人误分析报告为对象,通过灰色关联分析、数据挖掘技术和评分者信度评估验证了人误模式分类框架、人误诱发因素分类、人误模式分析模型和人误诱发因素识别模型的合理性和正确性。
针对行车调度系统人误数据缺乏而引起的无法直接或间接计算、估计和预测人误率的问题,参考硬件故障模式、影响及危害性分析技术提出了以人误行为可能性、可恢复性和后果严重性为指标的人误行为风险预测模型,并结合行车调度系统人误模式分类框架确定了人误行为可能性等级划分标准；结合系统人误屏障的特点确定了人误行为可恢复性等级划分标准；结合地铁事故管理规定确定了人误行为后果严重性等级划分标准,形成了对人误行为风险进行评价和定级的度量图和风险度量矩阵。
考虑到人误行为风险预测模型定量化的需要,参考硬件故障模式发生概率等级的评分准则和系统人误屏障的失误率减少作用确定了人误行为可能性和可恢复性指标的定量化定级标准,研究了基于贝塔分布的定量化人误数据的采集和估计方法,解决了人误率数据采集难的问题,设计并开发了基于典型任务的ATS模拟实验系统,通过视线追踪技术对定量化人误数据进行采集,完成了对采集方法的验证,为行车调度系统定量化人误数据收集提供思路。
在建立的人误行为风险预测技术上研究突发事件场景人误行为风险的识别方法。根据行车调度员突发事件处理任务可模块化的特点,建立了基本任务模块库及相应的突发事件人误场景生成技术,以人误场景构建规则为对象,分析和确定了突发事件场景的失误后果严重性等级、可恢复性等级、可能性等级和风险等级的计算规则,以及关键人误行为识别技术,并通过对接触轨断电突发事件人误风险预测实例验证了方法的实用性。
在人误诱发情景风险预测方面,充分考虑人误诱发因素间因果关系,以及因果关系而导致的其对系统人误的综合和整体效应的变化情况,构建了以模糊认知图来进行描述的人误诱发情景图模型,引入模糊认知图在因素关系推理、权重计算方面的理论成果,形成人误诱发情景影响效应的评价技术,完成了对某地铁公司人误诱发情景风险预测的实例分析。为了保证构建的图模型的正确性,采用证据理论和不一致性判断方法对专家判断进行综合,同时引入小样本数据筛选技术提高了图模型构建的效率。
最后,根据建立的行车调度系统人误预测技术特点,对地铁行车调度系统人误数据需求和不确定性进行了详细分析,建立了规范化的人误数据采集方法。
ABSTRACT:
In recent years, with the development of the subway train operation control system technology, the degree of automation of traffic dispatching system is higher and higher, which reduces the probability of accident caused by the traffic dispatcher human error. But at the same time, the problem of accident or consequence deterioration leading by the traffic dispatcher human error in emergency is increasing. Traffic dispatching system is an important guarantee of the subway safety, while the traffic dispatcher is the system's ultimate protection and recovery mechanism under the emergency. How to effectively avoid and reduce the subway traffic dispatching system human error under emergency is an urgently security problem for the world subway companies. Therefore, relying on the National High Technology Research and Development Program863of 'Integrated Optimizing Control Technology for Train Operation', this thesis analyzes the subway traffic dispatching system human error causing, and proposes two key research aspects of human error behavior risk prediction and human error forcing factor prediction. Thereby, the scenario based emergency human error behavior identification technique, prediction indexes grading technique and human error risk assessment technique are constructed. At the same time, the human error forcing factor risk prediction technique based on the causal relations between factors is buit, and the normalized human error data collecting and management method based on the proposed human error prediction technique is also built to provide the theory method and technical support for subway traffic dispatching system human error prevention and reduction. Research on the problem of traffic dispatching system human error prediction has important theoretical and actual application value.
Firstly, a subway traffic dispatcher human error mechanism model is constructed based on the detail analysis of the emergency processing task and the human interaction features of the subway traffic dispatching system, which describes traffic dispatcher human error mechanism in two aspects of human error behavior and human error forcing factors. Then, a human error mode analysis model and a human error forcing factors identification model are built based on the hierarchical task analysis method and the m-SHEL model respectively, and the related traffic dispatcher human error mode classification framework and the human error forcing factors optional library are also developed by modifying the multi-resource theory and Jae W. Kim's human error forcing factors set respectively. Furthermore, the created models and classification framework's validation are tested and verified by grey correlation analysis, data mining and intra-rate reliability of98emergency human error analysis reports of1997-2011from a domestic subway company.
In order to solve the problem that the subway traffic system human error probability is difficult to calculate, estimate and predict both in direct and indirect way because of the lack of human error data, a human error behavior risk prediction model is proposed by referring to the failure mode, effects and criticality analysis technique, which estimates the human behavior risk based on the three aspects of human error behavior possibility, restorability and consequences seriousness. Thereby, the human error behavior possibility grading standard is determined according to the traffic dispatching system human error mode classification framework, while the human error behavior restorability grading standard is built by considering the human error barrier character, and the human error consequences seriousness grading standard is established with the subway accident management regulations. Also, the measurement figure of criticality matrix and risk assessment matrix are constructed for human error behavior risk calculating and grading.
Considering the requirement of human error behavior risk prediction model's quantification, the human error behavior possibility and restorability quantification grading standard are built referring to the failure mode probability grading standard and traffic dispatching system human error barrier's error probability reduction degree. As a support, the difficulty of quantification human error data collection is also solved by introducing Beta Distribution into human error data collection and estimation method. And an ATS experiment simulator based on the traffic dispatcher's typical tasks is designed and developed to collect the qualification human error data by the eye tracking technique. The experimental result verifies the rationality of the data collection method and provids the idea for the subway traffic dispatching system quantification data collection.
Based on the established human behavior risk prediction technique, the emergency scenario human error risk prediction method is studied. In this study, the emergency human error scenario generation technique is created according to the modularized characteristics of traffic dispatcher emergency processing task, and the emergency scenario human error behavior consequences seriousness, restoration, possibility, risk degree calculation rules and key human error behavior identification technique are analyzed and identified on the basis of the human error scenario building rules. Additionally, an example of the contact rail power off emergency human error risk prediction is used to verify the practical applicability of the structured emergency scenario human error risk prediction method.
In the aspect of human error forcing context risk prediction, the causal relations between human error forcing factors and the changes of the human error forcing context comprehensive and the overall effect caused by the causal relations are taken into full account. By the time, the human error forcing context graph model described by the fuzzy cognitive map is built. Consequently, the human error forcing context effect assessment technique is formed by introducing the fuzzy cognitive map's research achievement in causality reasoning and factors weight calculating, and is used to complete the example analysis of a subway company's traffic dispatching system human error forcing context risk prediction. In order to ensure the constructed graph model's validity, evidence theory and inconsistency judgment method are utilized to synthesize and evaluate the experts judgment. Meanwhile, a small sample data screening technique is introdued to improve the graph model construction efficiency.
Finally, the subway traffic dispatching system human error data requirements and uncertainty are analyzed in detail according to the structured human error prediction technique characteristics of traffic dispatching system. Then, a normalized human error data collecting method is established.
引文
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