为解决大规模突发灾害给人民带来的生理与心理痛楚问题,考虑模糊需求情景下灾区道路受损、物资相对短缺、灾区需求紧迫度差异等因素,同时考虑灾民有限理性下物资竞争心理,运用前景理论刻画灾民对物资分配、运抵时间的综合感知,以灾区运...为解决大规模突发灾害给人民带来的生理与心理痛楚问题,考虑模糊需求情景下灾区道路受损、物资相对短缺、灾区需求紧迫度差异等因素,同时考虑灾民有限理性下物资竞争心理,运用前景理论刻画灾民对物资分配、运抵时间的综合感知,以灾区运输时间感知满意度最大、物资分配感知损失最小、运输成本最小为目标构建应急物资调度多目标优化模型,设计改进灰狼优化算法(Grey Wolf Optimizer,GWO)求解,引入混沌反向学习、差分进化、非线性收敛等策略实现对GWO算法的改进,并以2008年四川地震案例数据展开分析验证,依据模糊逻辑加权法选择合适的应急调度方案。研究表明,该模型可合理衡量有限理性下灾民综合感知,改进算法能够得出更加公平高效的调度方案,有效解决了灾后模糊需求情景下应急物资调度问题。展开更多
During the past two decades, several methodologies are endorsed to assess the compatibility of roadways for bicycle use under homogeneous traffic conditions. However, these methodologies cannot be adopted under hetero...During the past two decades, several methodologies are endorsed to assess the compatibility of roadways for bicycle use under homogeneous traffic conditions. However, these methodologies cannot be adopted under heterogeneous traffic where on-street bicyclists encounter a complex interaction with various types of vehicles and show divergent operational characteristics. Thus, the present study proposes an initial model suitable for urban road segments in mid-sized cities under such complex situations. For analysis purpose, various operational and physical factors along with user perception data sets （13,624 effective ratings in total） were collected from 74 road segments. Eight important road attributes affecting the bicycle service quality were identified using the most recent and most promising machine learning technique namely, random forest. The identified variables are namely, effective width of outside through lane, pavement condition index, traffic volume, traffic speed, roadside commercial activities, interruptions by unauthorized stoppages of intermittent public transits, vehicular ingress-egress to on-street parking area, and frequency of driveways carrying a high volume of traffic. Service prediction models were developed using ordered probit and ordered logit modeling structures which meet a confidence level of 95%. Prediction performances of developed models were assessed in terms of several statistical parameters and the ordered probit model outperformed the ordered logit model. Incorporating outputs of the probit model, a pre- dictive equation is presented that can identify under what level a segment is offering services for bicycle use. The service levels offered by roadways were classified into six categories varying from ＇excellent＇ to ＇worst＇ （A-F）.展开更多
文摘为解决大规模突发灾害给人民带来的生理与心理痛楚问题,考虑模糊需求情景下灾区道路受损、物资相对短缺、灾区需求紧迫度差异等因素,同时考虑灾民有限理性下物资竞争心理,运用前景理论刻画灾民对物资分配、运抵时间的综合感知,以灾区运输时间感知满意度最大、物资分配感知损失最小、运输成本最小为目标构建应急物资调度多目标优化模型,设计改进灰狼优化算法(Grey Wolf Optimizer,GWO)求解,引入混沌反向学习、差分进化、非线性收敛等策略实现对GWO算法的改进,并以2008年四川地震案例数据展开分析验证,依据模糊逻辑加权法选择合适的应急调度方案。研究表明,该模型可合理衡量有限理性下灾民综合感知,改进算法能够得出更加公平高效的调度方案,有效解决了灾后模糊需求情景下应急物资调度问题。
文摘During the past two decades, several methodologies are endorsed to assess the compatibility of roadways for bicycle use under homogeneous traffic conditions. However, these methodologies cannot be adopted under heterogeneous traffic where on-street bicyclists encounter a complex interaction with various types of vehicles and show divergent operational characteristics. Thus, the present study proposes an initial model suitable for urban road segments in mid-sized cities under such complex situations. For analysis purpose, various operational and physical factors along with user perception data sets （13,624 effective ratings in total） were collected from 74 road segments. Eight important road attributes affecting the bicycle service quality were identified using the most recent and most promising machine learning technique namely, random forest. The identified variables are namely, effective width of outside through lane, pavement condition index, traffic volume, traffic speed, roadside commercial activities, interruptions by unauthorized stoppages of intermittent public transits, vehicular ingress-egress to on-street parking area, and frequency of driveways carrying a high volume of traffic. Service prediction models were developed using ordered probit and ordered logit modeling structures which meet a confidence level of 95%. Prediction performances of developed models were assessed in terms of several statistical parameters and the ordered probit model outperformed the ordered logit model. Incorporating outputs of the probit model, a pre- dictive equation is presented that can identify under what level a segment is offering services for bicycle use. The service levels offered by roadways were classified into six categories varying from ＇excellent＇ to ＇worst＇ （A-F）.
