基于单双号限行措施的北京市交通需求管理策略分析

北京市交通管理局于2007年8月17号至20号在全市行政区域道路内实施了一项特殊的交通需求管理策略：机动车“单双号”交通保障措施。根据规定,在测试期间实行机动车单日单号行驶（8月17和19）、双日双号行驶（8月18和20）。为了研究该策略的有效性,对限行期间交通流变化数据进行了分析,通过对比限行前后交通流参数统计规律的变化特点得到如下结论：①作为一种强有力的交通需求管理手段,单双号限行期间每天停驶的机动车接近50％,因此城市交通拥堵得到缓解;②即使采取严格的限行措施,在数据采集点某些特殊时段的交通压力依然接近或超过阻塞密度,其他交通需求管理策略需同步配合实施才能够为2008年奥林匹克运动会提供高质量的交通服务。     

热熔喷涂型道路标线涂料在原太高速公路上的应用

介绍了热熔喷涂型道路标线涂料在原太高速公路养护中的应用情况,测试了其单位面积用量和反光性能,热熔喷涂型道路标线涂料较好地解决了高速公路边线路用性能和耐久性的关系,在实际施工和应用中取得了良好效果。     

公路隧道照明模糊控制研究

论述隧道照明亮度的计算方法,建立隧道照明模糊控制模型,讨论照明控制参数的取值方法,提出白天与夜间的照明控制界限。通过模拟分析,验证模糊预测的可行性。     

LKJ基础数据中信号机公里标与距离不一致问题分析

列车运行监控装置(LKJ)基础数据中,信号机公里标与信号机间距离是控制列车安全运行的基础,当提报的LKJ基础数据中信号机公里标与距离不一致时,会出现LKJ临时限速控制异常的情况。本文针对此类情况进行原因分析,并提出解决方法。     

施工船舶虚拟警戒标系统的设计与实现

针对施工船舶的安全作业需求,保障施工船舶的安全作业水域不受他船非法入侵,确保施工安全,设计并构建一套施工船舶虚拟警戒标系统。采用嵌入式计算、无线通信、船舶自动识别系统(Automatic Identification System,AIS)等技术对施工船舶虚拟警戒标系统进行关键性设计,在对其系统功能进行验证之后,在长江武汉段选取“吉洲”号船舶进行实船测试。测试结果表明:该虚拟警戒标系统能够实现船舶施工水域的划定、播发和显示等功能,并能对施工船舶进行远程跟踪和管理。研究成果对保障水上施工作业安全、减少和预防水上交通事故、保持港口与航道正常运行有较强的现实意义,对航运安全保障技术的发展有一定贡献。     

内河多功能航标供电系统研究

多功能航标对保障水上交通安全具有重要的作用,针对当前多功能航标存在的供电不足的问题,提出了建立风光互补的多功能航标供电系统。通过计算航标用电设备的功耗,对供电系统中的蓄电池模块、太阳能发电单元以及风力发电单元进行了设计。依据太阳辐射和风速的历史数据,利用M atlab计算并分析了太阳能电池和风力发电机的发电情况,采用逐月能量平衡法,对供电系统的可靠性进行了分析。研究结果表明,风光互补的多功能航标供电系统可以保障多功能航标的连续稳定供电。     

标定瞬时单位线法既有桥梁水害预报

以瞬时单位线法为基础,提出一种解决既有桥梁水害预报的水痕标定瞬时单位线法,利用历史水痕高程和对应的降雨量系列,完成瞬时单位线标定;将预报洪水雨量系列通过标定瞬时单位线对洪峰水位和洪峰滞时进行预报.在整个过程中,通过建立水位流量函数完成洪峰流量与洪峰水位之间的转换;对单位线法得到的洪水过程线的高峰流量段进行四次多项式拟合,提高预报洪峰数据精度.实例应用表明:水痕标定瞬时单位线法理论合理,应用方便,精度高,可以实现对实时水害提前预报,在保证行车安全和抗洪抢险等方面具有尤其重要的意义,预计在既有桥梁水害预报方面具有很好的应用前景.     

内河航标遥控遥测管理初探

文章结合航标遥控遥测技术在贵梧Ⅱ级航道工建设中的应用实践,分析了航标遥控遥测应用管理遇到的主要问题,并提出建立有效的运行管理制度、加强人才的培养和引进、做好系统软硬件的维护管理、加强实时监控的技术管理和深化应用管理等充分发挥航标遥控遥测技术效能的有效措施。     

A splitting rate model of traffic re-routeing and traffic control

The paper presents an idealised dynamical model of day-to-day or within-day re-routeing using splitting rates at nodes, or node-exit flows, rather than route-flows. It is shown that under certain conditions the dynamical model gives rise to a sequence of link flow vectors which converges to a set of approximate Wardrop equilibria. A special dynamical signal green-time re-allocation model is added; the combination is also shown (in outline) to converge to the set of approximate consistent equilibria under certain conditions. Finally the paper uses model network results to illustrate a method of designing fixed time signal timings to meet different scenarios.     

Latent temporal preferences: An application to airline travel

An essential element of demand modeling in the airline industry is the representation of time of day demand—the demand for a given itinerary as a function of its departure or arrival times. It is an important datum that drives successful scheduling and fleet decisions. There are two key components to this problem: the distribution of the time of day demand and how preferred travel time influences itinerary choice. This paper focuses on estimating the time of day distribution. Our objective is to estimate it in a manner that is not confounded with air travel supply; is a function of the characteristics of the traveler, the trip, and the market; and accounts for potential measurement errors in self-reported travel time preferences. We employ a stated preference dataset collected by intercepting people who were booking continental US trips via an internet booking service. Respondents reported preferred travel times as well as choices from a hypothetical set of itineraries. We parameterize the time of day distribution as a mixture of normal distributions (due to the strong peaking nature of travel time preferences) and allow the mixing function to vary by individual characteristics and trip attributes. We estimate the time of day distribution and the itinerary choice model jointly in a manner that accounts for measurement error in the self-reported travel time preferences. We find that the mixture of normal distributions fits the time of day distribution well and is behaviorally intuitive. The strongest covariates of travel time preferences are party size and time zone change. The methodology employed to treat self-reported travel time preferences as potentially having error contributes to the broader transportation time of day demand literature, which either assumes that the desired travel times are known with certainty or that they are unknown. We find that the error in self-reported travel time preferences is statistically significant and impacts the inferred time of day demand distribution.