Time-varying effects of influential factors on incident clearance time using a non-proportional hazard-based model

Incident clearance time is a major performance measure of the traffic emergency management. A clear understanding of the contributing factors and their effects on incident clearance time is essential for optimal incident management resource allocations. Most previous studies simply considered the average effects of the influential factors. Although the time-varying effects are also important for incident management agencies, they were not sufficiently investigated. To fill up the gap, this study develops a non-proportional hazard-based duration model for analyzing the time-varying effects of influential factors on incident clearance time. This study follows a systematic approach incorporating the following three procedures: proportionality test, model development/estimation, and effectiveness test. Applying the proposed model to the 2009 Washington State Incident Tracking System data, five factors were found to have significant but constant (or time independent) effects on the clearance time, which is similar to the findings from previous studies. However, our model also discovered thirteen variables that have significant time-varying impacts on clearance hazard. These factors cannot be identified through the conventional methods used in most previous studies. The influential factors are investigated from both macroscopic and microscopic perspectives. The population average effect evaluation provides the macroscopic insight and benefits long-term incident management, and the time-dependent pattern identification offers microscopic and time-sequential insight and benefits the specific incident clearance process.     

Forecasting demand for high speed rail

It is sometimes argued that standard state-of-practice logit-based models cannot forecast the demand for substantially reduced travel times, for instance due to High Speed Rail (HSR). The present paper investigates this issue by reviewing the literature on travel time elasticities for long distance rail travel and comparing these with elasticities observed when new HSR lines have opened. This paper also validates the Swedish long distance model, Sampers, and its forecast demand for a proposed new HSR, using aggregate data revealing how the air–rail modal split varies with the difference in generalized travel time between rail and air. The Sampers long distance model is also compared to a newly developed model applying Box–Cox transformations. The paper contributes to the empirical literature on long distance travel, long distance elasticities and HSR passenger demand forecasts. Results indicate that the Sampers model is indeed able to predict the demand for HSR reasonably well. The new non-linear model has even better model fit and also slightly higher elasticities.     

A finite mixture model of vehicle-to-vehicle and day-to-day variability of traffic network travel times

This study proposes an approach to modeling the effects of daily roadway conditions on travel time variability using a finite mixture model based on the Gamma–Gamma (GG) distribution. The GG distribution is a compound distribution derived from the product of two Gamma random variates, which represent vehicle-to-vehicle and day-to-day variability, respectively. It provides a systematic way of investigating different variability dimensions reflected in travel time data. To identify the underlying distribution of each type of variability, this study first decomposes a mixture of Gamma–Gamma models into two separate Gamma mixture modeling problems and estimates the respective parameters using the Expectation–Maximization (EM) algorithm. The proposed methodology is demonstrated using simulated vehicle trajectories produced under daily scenarios constructed from historical weather and accident data. The parameter estimation results suggest that day-to-day variability exhibits clear heterogeneity under different weather conditions: clear versus rainy or snowy days, whereas the same weather conditions have little impact on vehicle-to-vehicle variability. Next, a two-component Gamma–Gamma mixture model is specified. The results of the distribution fitting show that the mixture model provides better fits to travel delay observations than the standard (one-component) Gamma–Gamma model. The proposed method, the application of the compound Gamma distribution combined with a mixture modeling approach, provides a powerful and flexible tool to capture not only different types of variability—vehicle-to-vehicle and day-to-day variability—but also the unobserved heterogeneity within these variability types, thereby allowing the modeling of the underlying distributions of individual travel delays across different days with varying roadway disruption levels in a more effective and systematic way.     

高弹应力吸收带在旧水泥路面加铺改造中的应用

分析了目前旧水泥混凝土路面加铺改造中防止反射裂缝主要方法,对高弹性橡胶玛蹄脂材料应力吸收带的材料及指标、生产及施工工艺进行了介绍,通过实体工程应用对其使用效果进行了评价。     

膨胀加强带技术在翻车机房地下结构工程中的应用

大体积混凝土温度裂缝控制是地下混凝土结构工程施工中控制重点及难点。某码头工程翻车机房地下结构采用膨胀加强带代替闭合块(后浇带),同时使用Midas civil软件及一线通大体积混凝土测温系统对翻车机房漏斗梁混凝土温度及应力进行监测及分析,有效避免了混凝土温度裂缝的产生,同时缩短了工期,取得了较好的经济效益。     

基于SAPSO-BP网络模型的港口潮汐实时预报

为了提高港口码头潮汐预报的精度,提出一种自适应变异的粒子群优化算法SAPSO,将SAPSO优化算法与BP神经网络结合,用以潮汐水位的实时预报。SAPSO-BP网络模型运用自适应变异的PSO算法优化BP神经网络的网络参数,克服了传统BP神经网络所具有的对初始权值阈值敏感、容易陷入局部极小值的缺点,最后选用Isabel港口的实测潮汐值数据进行潮汐水位的实时预报仿真试验,用以验证SAPSO-BP预测模型的实用性和可靠性。     

贫混凝土透水基层的排水能力

为研究路面内部排水能力的计算方法、基层渗透系数取值方法以及路面内部退水时间,采用较大尺寸的试验板进行了透水基层排水能力的模拟试验研究,并通过研究变水头时不同坡度、不同孔隙率下的路面退水时间,建立了退水时间、退水百分比、孔隙率和坡度之间的回归公式。研究结果表明:在恒水头条件下,当基层底面水平时,基层的排水能力为达西定律计算值的1/2;当基层底面坡度为s时,基层的排水能力q可近似为q=kh1s;试件的垂直渗透系数和基层渗透系数的概念及数值不同,可以通过修正试件室内垂直渗透系数来获得路面基层渗透系数。建立的回归公式为最终确定路面内部的排水时间标准提供了依据。     

通勤出行公交候车时间的服务等级划分和度量

候车时间是决定通勤出行中公交系统吸引力的关键因素之一,合理的划分通勤出行公交候车时间服务等级有助于提高公交服务质量.通过乘客访谈,掌握乘客候车过程中的心理变化历程,按照乘客心理变化特征将其分为Ⅰ、Ⅱ、Ⅲ、Ⅳ等四个服务等级,构建隶属度函数,并基于隶属度最大原则划分每一等级的候车时间区间;通过SP 调查,应用非集计理论建立不同候车服务等级下候车时间价值模型,基于乘客的支付意愿,利用候车时间价值度量乘客感知候车时间,研究在不同服务等级下乘客感知候车时间的差异及随收入和候车服务等级的变化规律.结果表明,Ⅱ、Ⅲ和Ⅳ级服务水平乘客感知候车时间比Ⅰ级服务水平分别增加50%、近300%和540%;且在Ⅰ级和Ⅳ级服务水平下不同收入群体感知候车时间差异不明显;但Ⅱ级和Ⅲ级正好相反.从而为制定面向乘客的候车时间服务质量标准和优化运营调度方案、提高公交系统的吸引力提供科学依据.     

INNOVIA 300 跨座式单轨系统在芜湖单轨交通的折返性能分析

介绍 INNOVIA 300 跨座式单轨系统车辆、道岔和信号的主要特点和性能参数。针对芜湖单轨交通 2 号线万春湖路站至梦溪路站区段线路,建立多质点列车模型进行列车牵引仿真计算。在列车牵引仿真计算的基础上,根据站前、站后折返的作业流程进行基于通信的列车控制(communication based train control,CBTC)下的折返运行分析。结果表明,采用 R100 m 渡线和 R69 m 渡线的站前折返发车间隔分别为 128.7 s 和 132.6 s,站后折返发车间隔分别为 80.4 s 和 83.5 s;停站时间直接影响发车间隔,当停站时间不超过 65 s 时,站后折返时间均满足远期 2 min的发车间隔要求。利用 INNOVIA 300 跨座式单轨系统良好的折返性能可以有效提高系统运能,缩短乘客等待时间,并为线路的运营组织和优化奠定基础。