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.     

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.     

浅析船舶电机绝缘电阻

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人机工程学在移动电站设计中的应用

通过介绍人机工程在移动电站设计中的应用,在移动电站引入人机工程的相关知识和经验。     

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航空地面电源车主要用于飞机地面启动和通电检查等场合。介绍了我部新装备的某新型航空地面电源车的特性,对使用过程中曾出现的故障进行了分析,结合电源车的特点总结出了维护建议。     

汽车电动助力转向系统

对目前汽车配置的助力转向系统做了简要比较,指出了机械液压和电子液压助力的缺点,介绍了电动助力转向系统的构成、工作原理、以及主要设计参数和控制特性。此系统的优点是节约能源,提高操作稳定性,是将来动力转向的发展趋势。     

基于模型匹配控制的PHEV动态协调控制方法

在分析发动机与电动机的动态响应特性基础上,设计基于模型匹配控制的整车动力系统动态协调控制方法,并在所建立的并联式混合动力汽车(PHEV)的前向仿真模型上对该方法进行研究,证明该方法能有效控制2个动力源的动力耦合过程,具有较高的转矩控制精度和很好的动态响应特性,并利用所建立的硬件在环仿真试验台对该方法进行试验验证。     

油田埋地管道防腐层破损点检测及影响因素

简要阐述了埋地管道防腐层破损点的检测原理,并通过现场检测实例,总结出了影响防腐层破损点检测的几个主要因素,同时提供了解决方法,以提高防腐层破损点的检测准确率.     

混合动力汽车发动机起动/停机控制

介绍了采用继电器对发动机管理系统ECU的工作进行控制,同时针对发动机管理系统原有的减速断油模式进行了重新标定,实现了发动机高转速起动功能,且降低了停机时的转速,避免了对发动机ECU频繁的通/断电操作及因频繁起动造成的发动机燃油消耗率和排放性能的不良影响。     

内燃机电站行业国际标准制修订动态及ISO8528-1部分修订内容简介

简要介绍内燃机电站行业国际标准的制修订动态及ISO8528-1的部分修订内容,并提出贯彻实施意见。