Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China

Short-term forecasting of high-speed rail (HSR) passenger flow provides daily ridership estimates that account for day-to-day demand variations in the near future (e.g., next week, next month). It is one of the most critical tasks in high-speed passenger rail planning, operational decision-making and dynamic operation adjustment. An accurate short-term HSR demand prediction provides a basis for effective rail revenue management. In this paper, a hybrid short-term demand forecasting approach is developed by combining the ensemble empirical mode decomposition (EEMD) and grey support vector machine (GSVM) models. There are three steps in this hybrid forecasting approach: (i) decompose short-term passenger flow data with noises into a number of intrinsic mode functions (IMFs) and a trend term; (ii) predict each IMF using GSVM calibrated by the particle swarm optimization (PSO); (iii) reconstruct the refined IMF components to produce the final predicted daily HSR passenger flow, where the PSO is also applied to achieve the optimal refactoring combination. This innovative hybrid approach is demonstrated with three typical origin–destination pairs along the Wuhan-Guangzhou HSR in China. Mean absolute percentage errors of the EEMD-GSVM predictions using testing sets are 6.7%, 5.1% and 6.5%, respectively, which are much lower than those of two existing forecasting approaches (support vector machine and autoregressive integrated moving average). Application results indicate that the proposed hybrid forecasting approach performs well in terms of prediction accuracy and is especially suitable for short-term HSR passenger flow forecasting.     

皮口至炮台高速公路工程地质特征

结合皮炮高速公路路线勘察地质资料，对线路区内的区域工程地质条件、工程地质分区等工程地质特征进行了概略性介绍。     

Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas

In today’s world of volatile fuel prices and climate concerns, there is little study on the relationship between vehicle ownership patterns and attitudes toward vehicle cost (including fuel prices and feebates) and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin’s personal-fleet evolution.Opinion survey results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are price, type/class, and fuel economy. Most (56%) respondents also indicated that they would consider purchasing a Plug-in Hybrid Electric Vehicle (PHEV) if it were to cost $6000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle’s emissions, more strongly than they respond to information on fuel cost savings.Twenty five-year simulations of Austin’s household vehicle fleet suggest that, under all scenarios modeled, Austin’s vehicle usage levels (measured in total vehicle miles traveled or VMT) are predicted to increase overall, along with average vehicle ownership levels (both per household and per capita). Under a feebate, HEVs, PHEVs and Smart Cars are estimated to represent 25% of the fleet’s VMT by simulation year 25; this scenario is predicted to raise total regional VMT slightly (just 2.32%, by simulation year 25), relative to the trend scenario, while reducing CO₂ emissions only slightly (by 5.62%, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 24% and CO₂ emissions by 30% (relative to trend).Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross-over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin’s current household fleet). Feebate-policy receipts are forecasted to exceed rebates in each simulation year.In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have added effects on energy dependence and greenhouse gas emissions.     

大型机场地面集疏运需求预测方法研究

机场地面集疏运系统是机场发展的重要支撑，交通需求预测是进行机场集疏运交通规划的前提。笔者以四阶段预测的基本原理为基础，结合机场集疏运需求特点，对预测的整体思路、方法、步骤和内容等进行详细分析，优化调整出行分布、方式划分预测模型，并以南京禄口国际机场为实例进行了预测。研究成果对国内同类大型机场的集疏运需求预测具有借鉴和参考价值。     

Development and evaluation of a statistically reliable traffic counting program

This paper reviews the existing procedures to compute the seasonal factors, growth factors, and the number of automatic traffic recorder stations (ATRs) for developing a statistically reliable traffic counting program. The review is based on, (i) theoretical evaluation of the procedures, (ii) analysis of the results obtained from applying these procedures to extensive real life data, and (iii) experience of past researchers with these methods. The paper primarily deals with existing statistical procedures for determining seasonal factors, since it is believed that these factors play the most important role in estimating Annual Average Daily Traffic (AADTs) from short counts. The procedure of cluster analysis (which is most commonly used to determine seasonal factors) is critically reviewed. The shortcomings of this method motivated the development of a different statistical procedure, based on regression analysis, to determine seasonal factors. It is shown that this newly developed method gives better results than the cluster analysis techniques. Furthermore, separate sections are dedicated to a thorough analysis of determination of number of ATR stations and computation of growth factors.     

软土地基桥涵设计方法探析

软土地基是桥梁建设经常遇到的地基问题.针对软土地基的特点及桥梁在软土地基上的设计和建设的原则进行探讨,以期能为我国桥梁建设提供一些有意义的参考.     

基于GRNN的GSM-R场强覆盖预测算法

在比较Hata模型修正方法和GRNN算法场强覆盖预测效果的基础上,仿真分析了训练集构成和平滑因子选择对GRNN算法预测效果的影响,给出了训练集构成和平滑因子选择的方法;提出了电波传播环境相似系数来表征GRNN模型在不同环境下的适用性。仿真结果表明,两种传播环境的相似系数越大,由一种环境下测试数据确定的GRNN在另一环境下的预测精度越高。     

汽车硅油风扇离合器性能试验方法研究和测试台研制

通过对汽车硅油风扇离合器工况的模拟和高效测试方法的研究,研制测试其各项技术参数的数控自动测试台。实现对产品性能的自动、高效、准确的判定。     

高桥墩桩基础的优化设计及因素分析

在高桥墩桩基屈曲能量法分析的基础上,提出兼顾结构稳定、强度和经济性的优化模型,并应用优化算法中非线性序列二次规划法,进行优化设计探讨,同时给出优化设计算法流程图,编制了相应的优化计算程序;为了解桥墩高度、轴向荷载、地基比例系数和混凝土弹性模量对于高桥墩桩基屈曲的影响,结合优化设计模型进行了因素分析,探讨了参数变量对目标函数最优值的影响规律和程度,比较结果说明,在进行高桥墩桩基的屈曲受力分析时,高桥墩的大变形影响不容忽视;而一般在工程设计中,可考虑将桩周土体和混凝土弹性模量的增强作用作为设计的安全储备。     

填料性能对预应力锚拉式桩板墙影响的数值分析

预应力锚拉式桩板墙是一种得到广泛应用的新型支挡结构,本文通过建立耦合分析数值模型对桩板墙的受力特征进行了有限元分析。通过分析了解填料的力学参数(变形模量、摩擦角、粘聚力)对桩板墙位移、弯矩、土压力以及锚固端接触应力和自由段轴力的变化。揭示填料力学性能对预应力锚拉式桩板墙的影响,为类似工程的设计提供参考。