CO2 emissions and expansion of railway,road, airline and in-land waterway networks over the 1985–2013 period in China: A time series analysis

China, the world’s largest CO₂ emitter, is continuing its long-term strategy to use transportation investments as a tool for development. With the expectation that transportation will contribute 30–40% of the total CO₂ emissions in China in the near future, there is an imminent need to identify how the development of different transportation modes may have different long-term effects on CO₂ emissions. Using time series data over the period of 1985–2013, this paper applies the combined autoregressive distributed lag (ARDL) and vector error correction model (VECM) approach to identify short- and long-run causal relationships between CO₂ emissions and mode-specific transportation development, including railway, road, airline, and inland waterway. We find that China’s domestic expansions of road, airline, and waterway infrastructure lead to long-run increases in CO₂ emissions. Among them, waterway has the strongest positive impact on CO₂ emissions, followed by road. Despite a short-run, positive impact on CO₂ emissions, railway expansion leads to long-run decreases in CO₂ emissions. The results are especially encouraging for the central government of China given its long-standing and on-going efforts to expand railway infrastructure at the national level. Looking forward, it is recommended that China continues its national investments in railway infrastructure to achieve both environment and economy goals.     

Controlled time of arrival windows for already initiated energy-neutral continuous descent operations

Continuous descent operations with controlled times of arrival at one or several metering fixes could enable environmentally friendly procedures without compromising terminal airspace capacity. This paper focuses on controlled time of arrival updates once the descent has been already initiated, assessing the feasible time window (and associated fuel consumption) of continuous descent operations requiring neither thrust nor speed-brake usage along the whole descent (i.e. only elevator control is used to achieve different metering times). Based on previous works, an optimal control problem is formulated and numerically solved. The earliest and latest times of arrival at the initial approach fix have been computed for the descent of an Airbus A320 under different scenarios, considering the potential altitudes and distances to go when receiving the controlled time of arrival update. The effects of the aircraft mass, initial speed, longitudinal wind and position of the initial approach fix on the time window have been also investigated. Results show that time windows about three minutes could be achieved for certain conditions, and that there is a trade-off between robustness facing controlled time of arrival updates during the descent and fuel consumption. Interestingly, minimum fuel trajectories almost correspond to those of minimum time.     

浅谈北京地铁十号线万柳站折返能力

北京地铁十号线万柳站的折返作业间隔时间为117s,折返能力紧张。本文论述了两种提高折返能力的改进措施：调整岔线结构和改变折返方式。     

旅客列车票额自动分配系统的设计和实现

基于地区中心数据库中保存的各车站售票存根和旅客未能获得的票额数,利用时间序列预测方法进行客流预测。对所有列车分成三类:1 列车的开行线路没变,停靠站也不变,编组发生改变。2 列车的开行线路没变,只是增加或者减少了停靠站。3 列车的开行线路变了,或者是新增加的一趟临客。对不同类列车分别求其相对应的沿途各站分席别客流量,按各车站客流量比例分配票额。系统分为地区中心和车站两个子系统。     

小波变换域的时间扩展信道的信号检测

时间扩展信道（TSD）是信道畸变的一种常见形式，它的最佳似然比检测器是副本相关积分器（RCI）。本文从连续小波变换的理论出发，推导了TSD信道小波变换域的最佳检测器（WDRCI），从理论上分析了它与副本相关积分器的等价性，用仿真实验给出了模拟的结果，验证了其有效性，并进一步说明了其性能优于小波域的副本相关器（WDRC）。     

浮式抗冰平台设计理念及水动力特性研究

钻探极地地区近海石油和天然气资源对移动式离岸钻井平台的水动力性能、抗冰性能和作业性能有着极大的要求。本文主要介绍了浮式抗冰平台的设计方案,并结合当今最新一款半潜式抗冰平台的设计理念,分别从频域和时域分析的角度与传统的半潜式平台作对比,分析得出其具有优越的水动力性能,垂荡、横摇、纵摇运动响应都优于传统的半潜平台,为下一步抗冰性能分析及其关键技术的研究做基础,同时也给半潜式平台带来新的设计理念,为浮式抗冰平台的设计建造提供合理思路。     

提高GPS定位精度的新方法

提出了一种提高ＧＰＳ定位精度的新方法。用该方法可以准确地对ＧＰＳ定位数据进行建模和一步预测，在此基础上通过自适应滤能够提高ＧＰＳ定位精度３－５倍。定点实时数据处理证明了此方法的有效性。     

新奥法施工隧道掘进作业接尘量调查

新奥法施工隧道掘进作业粉尘时间加权平均浓度为１４．４９ｍｇ／ｍ■＾３。各工班中以炮后时间加权平均浓度最高（４１．８８ｍｇ／ｍ■＾３），装碴和打眼分别为２２．８４ｍｇ／ｍ■＾３和６．４４ｍｇ／ｍ■＾３。掘进作业全过程时间加权接尘量为１２２５９．５７ｍｇ／（ｍ■＾３．ｍｉｎ）。其中装碴接尘量最高７１７．７６ｍｇ／（ｍ■＾３．ｍｉｎ）占掘进全过程总接尘量的５８．５０％。各产尘工序产尘量构成比也以装碴最高     

Local online kernel ridge regression for forecasting of urban travel times

Accurate and reliable forecasting of traffic variables is one of the primary functions of Intelligent Transportation Systems. Reliable systems that are able to forecast traffic conditions accurately, multiple time steps into the future, are required for advanced traveller information systems. However, traffic forecasting is a difficult task because of the nonlinear and nonstationary properties of traffic series. Traditional linear models are incapable of modelling such properties, and typically perform poorly, particularly when conditions differ from the norm. Machine learning approaches such as artificial neural networks, nonparametric regression and kernel methods (KMs) have often been shown to outperform linear models in the literature. A bottleneck of the latter approach is that the information pertaining to all previous traffic states must be contained within the kernel, but the computational complexity of KMs usually scales cubically with the number of data points in the kernel. In this paper, a novel kernel-based machine learning (ML) algorithm is developed, namely the local online kernel ridge regression (LOKRR) model. Exploiting the observation that traffic data exhibits strong cyclic patterns characterised by rush hour traffic, LOKRR makes use of local kernels with varying parameters that are defined around each time point. This approach has 3 advantages over the standard single kernel approach: (1) It allows parameters to vary by time of day, capturing the time varying distribution of traffic data; (2) It allows smaller kernels to be defined that contain only the relevant traffic patterns, and; (3) It is online, allowing new traffic data to be incorporated as it arrives. The model is applied to the forecasting of travel times on London’s road network, and is found to outperform three benchmark models in forecasting up to 1 h ahead.     

Dynamics of connected vehicle systems with delayed acceleration feedback

In this paper, acceleration-based connected cruise control (CCC) is proposed to increase roadway traffic mobility. CCC is designed to be able to use acceleration signals received from multiple vehicles ahead through wireless vehicle-to-vehicle (V2V) communication. We consider various connectivity structures in heterogeneous platoons comprised of human-driven and CCC vehicles. We show that inserting a few CCC vehicles with appropriately designed gains and delays into the flow, one can stabilize otherwise string unstable vehicle platoons. Exploiting the flexibility of ad-hoc connectivity, CCC can be applied in a large variety of traffic scenarios. Moreover, using acceleration feedback in a selective manner, CCC provides robust performance and remains scalable for large systems of connected vehicles. Our conclusions are verified by simulations at the nonlinear level.