客运枢纽"人群迷路区"的快速识别与优化 ——以北京南站为例

为缓解大型客运枢纽因诱导服务不到位而产生的人流拥堵问题，采用室内WiFi 定位技术和Mysql获取行人交通流分布热力图，通过百度地图坐标拾取系统识别人流聚集区的具体位置。通过定义迷路人群的交通特征参数，提出“人群迷路区”概念；应用视频识别追踪技术，分析行人速度与加速度的方差，于人流密集区运用视频识别技术寻找“人群迷路区”。最后以北京南站为例进行实地调研，利用调研数据识别北京南站的“人群迷路区”。基于人群速度与加速度方差，“人群迷路区”被分为四类：单纯人流聚集区、因标识不足而产生的人群迷路区、因标识过于复杂而产生的人群迷路区、因商业设施或障碍物遮挡而产生的人群迷路区。同时提出加大商业活动监管力度、简化部分引导标识、增加其他交通方式换乘区域标识等优化方法，可使人群行进效率提高约25%。     

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.     

点汇聚系统的航空器污染物减排效应与机理

为研究点汇聚系统的环境效益及减排机理，采用考虑气象条件修正后的航空器性能、燃油 流量及污染物计算模型，设计了理想条件下非高峰时刻与实际运行的高峰时刻两种场景，对比分 析了航空器在点汇聚系统与标准进场程序中污染物(即HC、CO、NOX、SOX和PM)的排放情况，并 从飞行时间、燃油消耗与排放指数3个方面分析了点汇聚系统的减排机理、识别了减排关键因素。 研究发现：在非高峰时刻，点汇聚系统与标准进场程序的污染物排放总量分别为5.79 kg与7.17 kg， 点汇聚系统较标准进场程序共减少约19.25%污染物排放，对NOX、SOX和PM减排效果显著；在高 峰时刻，点汇聚系统与标准进场程序的污染物排放总量分别为290.01 kg与406.69 kg，点汇聚系 统较标准进场程序共减少28.69%污染物排放，其中NOX减排比例最高可达48.32%。结果表明： 无论是非高峰时刻还是高峰时刻，点汇聚系统都具有良好的环境效益，可有效减少污染物的排放 总量，且对NOX减排效果最佳；较短的飞行时间、较低的燃油流量是点汇聚系统体现减排优势的 关键驱动因素。     

Demi-flexible operating policies to promote the performance of public transit in low-demand areas

The efforts of providing attractive transport service to residents in sparse communities have previously focused on operating flexible transit services. This paper identifies a new category of transit policies, called demi-flexible operating policies, to fill the gap between flexible transit services and conventional fixed-route systems. The passenger cost function is defined as the performance measure of transit systems and the analytic work is performed based on a real-world flag-stop transit service, in which we compare its system performance with another two comparable systems, the fixed-route and flex-route services, at expected and unexpected demand levels in order to be closer to reality. In addition, the dynamic-station policy is introduced to assist the flex-route service to better deal with unexpectedly high demand. Experiments demonstrate the unique advantages of demi-flexible operating policies in providing affordable, efficient, and reliable transport service in low-demand operating environments and this work is helpful to optimize the unifying framework for designing public transit in suburban and rural areas.     

小管径垂直管水下排气完全携带点实验及分析

船用柴油机采用水下排气方式具有降低排气噪声、改善舱外空气质量等优点，在大型豪华游轮等特殊船舶中具有广泛应用前景。为防止海水由排气管倒灌入柴油机造成事故，需弄清水下排气管内水倒流发生的条件。对大管径垂直管防止水倒灌临界条件即完全携带点的已有认识较为统一，而对小管径垂直管仍未有清晰的认识。在管径25~100 mm范围对垂直管的完全携带点进行了实验，探究了小管径垂直管的完全携带点预报方法。实验结果表明在小管径范围，垂直管完全携带点对应的临界表观气速随管径增大而升高，但气相Wallis数随管径增大而减小，说明Wallis数过度关联了管径对临界表观气速的影响。基于气相Kutateladze数和无量纲气相黏性，对小管径垂直管提出了新的完全携带点预报模型和相应实验关联式。     

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.     

Analysis of air traffic control operational impact on aircraft vertical profiles supported by machine learning

The Air Traffic Management system is under a paradigm shift led by NextGen and SESAR. The new trajectory-based Concept of Operations is supported by performance-based trajectory predictors as major enablers. Currently, the performance of ground-based trajectory predictors is affected by diverse factors such as weather, lack of integration of operational information or aircraft performance uncertainty.Trajectory predictors could be enhanced by learning from historical data. Nowadays, data from the Air Traffic Management system may be exploited to understand to what extent Air Traffic Control actions impact on the vertical profile of flight trajectories.This paper analyses the impact of diverse operational factors on the vertical profile of flight trajectories. Firstly, Multilevel Linear Models are adopted to conduct a prior identification of these factors. Then, the information is exploited by trajectory predictors, where two types are used: point-mass trajectory predictors enhanced by learning the thrust law depending on those factors; and trajectory predictors based on Artificial Neural Networks.Air Traffic Control vertical operational procedures do not constitute a main factor impacting on the vertical profile of flight trajectories, once the top of descent is established. Additionally, airspace flows and the flight level at the trajectory top of descent are relevant features to be considered when learning from historical data, enhancing the overall performance of the trajectory predictors for the descent phase.     

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。     

需求响应的集装箱班列时刻表优化及 Benders分解算法

充分考虑集装箱货物与集装箱班列在时间和数量方面的匹配关系，以集装箱货物送达目的站的总延误最小为目标，构建集装箱班列时刻表优化的线性混合整数规划模型.采 用Benders分解算法将原问题分解为确定集装箱货物与班列匹配方案的主问题，以及优化班列时刻表的子问题，通过求解子问题的对偶模型不断产生主问题的割平面.为克服割平面有效性较低的缺点，设计改进策略，使每次迭代产生多个割平面同时添加至主问题中.最后，通过算例验证模型和算法的有效性.结果表明：改进策略的运用有效提高了算法的计算效率，利用模型及算法得到的班列时刻表与集装箱货物的数量、时间分布之间具有较好的匹配性，制定的班列时刻表很好地响应了用户需求.     

基于有限混合零截尾事故预测模型的事故多发段判别方法

为了准确判别事故多发段,有针对性地提出安全应对措施以提升道路交通的安全水平,针对零值缺失交通事故数据并考虑其异质性特点,在单零截尾负二项(ZTNB)模型的基础上建立有限混合零截尾事故预测模型(FMZTNB)。应用R软件对单零截尾负二项模型中的参数进行估计,采用马尔科夫链蒙特卡洛算法(MCMC)对FMZTNB预测模型参数进行求解,并采用Gelman-Rubin收敛统计量对抽样结果进行检查。选择事故风险水平分别为低、中和高的9个路段,分别用2种模型对交通事故次数进行预测。综合观测到的事故次数和相应的事故预测模型结果,采用经验贝叶斯方法对事故相对多发段进行判别。最后采用事故次数一致性检验、判别点段一致性检验和排序一致性检验3种检验方式对判别结果对比分析。结果表明:基于事故率的事故相对多发段判别方法存在较大的不一致性,基于零截尾负二项预测模型的路段事故相对多发判别结果明显优于基于传统负二项预测模型的结果。整体上,基于有限混合零截尾事故预测模型的事故相对多发路段的判别结果高于基于单零截尾负二项分布模型的判别结果。