后悔视角下的应急疏散出行方式决策行为分析

鉴于应急疏散者出行决策的后悔规避心理,以政府公交车、自有私家车和借用私家车三类疏散交通方式选择问题为例,选取平均运行时间、运行时间不确定性、平均等待时间和预期感知服务水平等属性变量,分别构建了基于随机后悔最小化和效用最大化的多项Logit 模型.以应急疏散意向行为调查数据为基础,表明后悔模型的模型拟合度优于效用模型,对属性变量间关系的刻画更符合实际.进一步通过弹性计算和敏感性分析,揭示出平均运行时间这一属性最为敏感.研究表明,应急疏散者是后悔规避的,其决策行为试图将后悔感知最小化,而非预期效用最大化,从随机后悔视角描述疏散决策行为更为准确.     

基于Pareto蚁群算法的船舶风险规避路径优化

船舶在海上航行时,一直面临着海上运输风险的威胁,为了降低海上运输风险同时考虑船舶经济效益,本文建立了以运输风险最小和航行成本最小的双目标路径优化模型,实现船舶风险规避.运用栅格法构建环境模型,为相应的栅格路径赋予航行成本和运输风险,并设计了一种基于Pareto最优解集和NSGA小生境方法的多目标蚁群算法.以印度洋海域的2条航线为案例,以经典单目标蚁群算法为对比,验证了模型和算法的有效性.结果表明,该模型和算法在解决船舶风险规避路径优化问题上具有良好的效果,能为决策者制定船舶海上运输风险规避路径提供决策参考.     

城市道路平面信号交叉口行人安全分析

应用交通流及交通冲突理论,建立了道路平面信号交叉口行人安全的风险度模型,将其进一步界定为绿灯时间范围内穿越、红灯时间范围内穿越及基于中间岛的二次穿越3种方式,并以哈尔滨市嵩山路一汉水路十字交叉口为应用案例进行危险度计算。进而从一驾驶员驾驶行为、交叉口设计及行人特性角度探讨了交叉口行人过街的风险致因机理,提出了规避行人风险的相关建议。     

基于参考依赖法的出行者日常路径选择行为建模

为研究旅行时间不确定环境下出行者动态调节日常路径选择的行为特征,基于参考依赖行为理论建立了随机路网用户日常路径选择行为模型.引入了基于参考依赖的路径负效用,并设出行者每天的路径选择行为服从基于Logit模型的决策过程,给出了保持每日网络均衡的出行者日常旅行行为演化模型,分析了模型的稳定性,并利用相继平均法与Logit配流技术相结合的启发式算法进行求解.计算结果表明:路网的流量分布和出行者的参考点在一定的时间范围内将达到稳定状态,此时,传统的期望效用模型将低估总网络用户成本和高估由通行能力改善或拥挤收费政策所带来的收益,总量上达1%左右.     

Random regret minimization or random utility maximization: an exploratory analysis in the context of automobile fuel choice

Interest in alternative behavioural paradigms to random utility maximization (RUM) has existed ever since the dominance of the RUM formulation. One alternative is known as random regret minimization (RRM), which suggests that when choosing between alternatives, decision makers aim to minimize anticipated regret. Although the idea of regret is not new, its incorporation into the same discrete choice framework of RUM is very recent. This paper is the first to apply the RRM‐model framework to model choice amongst durable goods. Specifically, we estimate and compare the RRM and RUM models in a stated choice context of choosing amongst vehicles fuelled with petrol, diesel and hybrid (associated with specific levels of fuel efficiency and engine capacity). The RRM model is found to achieve a marginally better fit (using a non‐nested test of differences) than its equally parsimonious RUM counterpart. As a second contribution, we derive a formulation for regret‐based elasticities and compare utility‐based and regret‐based elasticities in the context of stated vehicle type choices. We find that in the context of our choice data, mean estimates of elasticities are different for many of the attributes and alternatives. Copyright © 2011 John Wiley & Sons, Ltd.     

New insights on random regret minimization models

This paper develops new methodological insights on Random Regret Minimization (RRM) models. It starts by showing that the classical RRM model is not scale-invariant, and that – as a result – the degree of regret minimization behavior imposed by the classical RRM model depends crucially on the sizes of the estimated taste parameters in combination with the distribution of attribute-values in the data. Motivated by this insight, this paper makes three methodological contributions: (1) it clarifies how the estimated taste parameters and the decision rule are related to one another; (2) it introduces the notion of “profundity of regret”, and presents a formal measure of this concept; and (3) it proposes two new family members of random regret minimization models: the μRRM model, and the Pure-RRM model. These new methodological insights are illustrated by re-analyzing 10 datasets which have been used to compare linear-additive RUM and classical RRM models in recently published papers. Our re-analyses reveal that the degree of regret minimizing behavior imposed by the classical RRM model is generally very limited. This insight explains the small differences in model fit that have previously been reported in the literature between the classical RRM model and the linear-additive RUM model. Furthermore, we find that on 4 out of 10 datasets the μRRM model improves model fit very substantially as compared to the RUM and the classical RRM model.     

密封拍卖和采购招标的比较静态分析

采用积分法对对称独立私人价值第一价格密封拍卖和招投标中的投标策略关于投标人的风险态度和信息分布的单调比较静态分析,其结果对风险中性、风险厌恶、风险追求以及混合风险的投标人均成立,是对Riley和Samulson的微分法所得结果的实质性推广.     

考虑风险规避和认知更新的日常择路行为演进

为了刻画出行者的日常择路行为,利用动力学系统方法和不动点理论,建立了一个集成风险规避和认知更新的演化模型,分析了演化过程的稳定性,并在一个简单网络上进行了验证。发现在模拟开始的前15 d内,出行时间预算、路径期望出行时间、实际出行时间以及路径流量都出现了较大的波动,但经过大约30 d的摸索以后,开始趋向于随机用户均衡状态。分析结果表明:模型所设计的择路演化过程类似于经典的相继平均算法的计算过程,可以确保收敛到稳定状态,并与初始状态参数的取值无关。     

Adaptation planning for climate-resilient urban infrastructures

This paper analyzes the interdependency across two critical infrastructures of transportation and motor fueling supply chains, and investigates how vulnerability to climatic extremes in a fueling infrastructure hampers the resilience of a transportation system. The proposed model features both a bi-stage mathematical program and an extension to an ‘α-reliable mean-excess’ regret model. The former aspect allows decision makers to optimize the pre-disaster asset prepositioning against the maximum post-disaster system resilience. The latter aspect of the proposed model devalues the impact of ‘low-probability, high-cost’ sub-scenarios upon model results. The model reveals the reliance of post-disaster urban mobility on the interdependent critical infrastructure of motor fueling supply chains. The results also suggest how investment in the fueling infrastructure’s vulnerable elements protects urban mobility while the transportation network is stressed or under attack.