路段平均行程时间估计方法

为了有效利用线圈检测数据,精确估计路段平均行程时间,提出了一种路段平均行程时间估计方法。将路段平均行程时间分为平均行驶时间、平均排队时间和平均通过路口时间三部分。考虑线圈埋设的特点,通过估计平均行驶速度得到平均行驶时间。用分段时齐Poisson过程描述车辆驶入路段过程和驶离过程,用Markov排队模型描述车辆排队过程,用生灭过程描述排队车辆数,得到车辆排队模型,计算了路段有、无初始排队的平均排队时间。基于选取与路口相关的饱和流率和平均车长,计算了平均通过路口时间。计算结果表明:平均行程时间估计值与实测值的误差小于12%,说明路段平均行程时间估计方法可行。     

基于约束卡尔曼滤波的短时交通流量组合预测模型

为了克服单一的交通流预测模型性能不稳定的问题,提出了基于约束卡尔曼滤波的短时交通流量组合预测模型。约束卡尔曼滤波组合预测模型以各单一预测模型的权重为状态变量,交通流量为观测变量,预测结果是单一预测模型的加权和,加权系数由约束卡尔曼滤波方程递推动态确定,最后通过广深高速公路上采集的交通流量数据对算法进行了验证。结果表明,在不同预测步长情况下,约束卡尔曼滤波组合预测模型要优于最佳的单一预测模型或与其持平,并且不受某一较差的预测模型影响,具有较高的鲁棒性。     

沈局重点工业污染源确定原则和评价方法的探讨

根据沈局污染源管理的需要,对沈局污染源进行了重新评价,制定了确定重点工业污染源的3项原则:依量原则;依害原则;依位原则.遵循3项原则,我们选择了新的污染源评价方法,即对等标污染负荷法进行了加权处理.处理后的评价方法能够较为全面地反映污染源的潜在污染能力,是确定重点工业污染源的直接依据.     

城市轨道交通各发展阶段的运行特征及在公交系统中的作用

城市轨道交通网络的形成分为三个发展阶段:成网前阶段,轨道骨架网阶段和稳定的轨道网络阶段.分析了不同发展阶段轨道交通运行特征指标的取值范围,采用客运量和客运周转量指标研究了轨道交通不同发展阶段在公共交通系统中所起的作用.     

广深线石龙地区准高速铁路开通前后铁路环境噪声冲击指数

广深线准高速铁路是我国第一条准高速铁路，其开通前后铁路环境噪声对居民影响程度必将产生变化。通过现场测试，采用铁路环境噪声冲击指数，分析评价准高速铁路对环境噪声的实际影响。结果表明，广深线准高速铁路开通后，石龙镇敏感地段铁路环境噪声冲击指数有所下降，高噪声居民下降率昼间约为２２．６％，夜间约为２３．２％。     

青海省道路气候区划研究

依据气候要素对道路工程建设的影响,根据全省气温、冻土以及潮湿系数的分布状况,结合青海省地形地貌特点,参照交通部部标准《公路自然区划标准》（JTJ003-86）,对青海省公路自然气候作了一、二级区划。一级区划将全省分为多年冻土区和季节冻土区,二级区划全省可分为11类气候类型区域。     

某海域松散砂层钻探成孔问题的探讨

针对某港口工程海域松散砂层钻探成孔中,多次出现的塌孔和涌砂致使取土困难不能进行原位测试的问题,在实际操作中经过反复试验,采用加重泥浆护壁和投放粘土球等办法,增加钻孔孔底的压力和孔壁的泥皮厚度,使层压水不易向钻孔内渗流,并通过适当控制钻进速度最终解决了钻探成孔问题,保证了工程地质勘察资料的准确性、真实性.     

Factors affecting public transportation usage rate: Geographically weighted regression

As the number of private vehicles grows worldwide, so does air pollution and traffic congestion, which typically constrain economic development. To achieve transportation sustainability and continued economic development, the dependency on private vehicles must be decreased by increasing public transportation usage. However, without knowing the key factors that affect public transportation usage, developing strategies that effectively improve public transportation usage is impossible. Therefore, this study respectively applies global and local regression models to identify the key factors of usage rates for 348 regions (township or districts) in Taiwan. The global regression model, the Tobit regression model (TRM), is used to estimate one set of parameters that are associated with explanatory variables and explain regional differences in usage rates, while the local regression model, geographically weighted regression (GWR), estimates parameters differently depending on spatial correlations among neighbouring regions. By referencing related studies, 32 potential explanatory variables in four categories, social-economic, land use, public transportation, and private transportation, are chosen. Model performance is compared in terms of mean absolute percentage error (MAPE) and spatial autocorrelation coefficient (Moran’ I). Estimation results show that the GWR model has better prediction accuracy and better accommodation of spatial autocorrelation. Seven variables are significantly tested, and most have parameters that differ across regions in Taiwan. Based on these findings, strategies are proposed that improve public transportation usage.     

Towards data-driven car-following models

Car following models have been studied with many diverse approaches for decades. Nowadays, technological advances have significantly improved our traffic data collection capabilities. Conventional car following models rely on mathematical formulas and are derived from traffic flow theory; a property that often makes them more restrictive. On the other hand, data-driven approaches are more flexible and allow the incorporation of additional information to the model; however, they may not provide as much insight into traffic flow theory as the traditional models. In this research, an innovative methodological framework based on a data-driven approach is proposed for the estimation of car-following models, suitable for incorporation into microscopic traffic simulation models. An existing technique, i.e. locally weighted regression (loess), is defined through an optimization problem and is employed in a novel way. The proposed methodology is demonstrated using data collected from a sequence of instrumented vehicles in Naples, Italy. Gipps’ model, one of the most extensively used car-following models, is calibrated against the same data and used as a reference benchmark. Optimization issues are raised in both cases. The obtained results suggest that data-driven car-following models could be a promising research direction.     

基于客户满意度的货运动车组开行方案研究

高铁货运逐渐兴起,本文对货运动车组开行方案优化进行了研究。针对高铁运输安全、快捷、运输成本较低等特性,提出以货运动车组及客货混编动车组为主,客运动车组捎带运输为补充的开行模式,结合运输需求、线路通过能力等限条件,以运输成本最小和货主满意度最大为目标,建立多目标综合优化模型,运用线性加权组合法,以lingo软件为依托进行求解。通过算例给出具体开行方案,并对同一运输区间各种运输方式的单位成本及用时进行对比分析。结果表明,相对于传统运输方式,高铁货运综合运输成本较低,用时更短,能够兼顾运输企业与货主的双重要求;所建模型优化效果明显,可为未来货运动车组的开行提供参考。