Measuring transport related CO2 emissions induced by online and brick-and-mortar retailing

We develop a method for empirically measuring the difference in transport related carbon footprint between traditional and online retailing (“e-tailing”) from entry point to a geographical area to consumer residence. The method only requires data on the locations of brick-and-mortar stores, online delivery points, and residences of the region’s population, and on the goods transportation networks in the studied region. Such data are readily available in most countries. The method has been evaluated using data from the Dalecarlia region in Sweden, and is shown to be robust to all assumptions made. In our empirical example, the results indicate that the average distance from consumer residence to a brick-and-mortar retailer is 48.54 km in the studied region, while the average distance to an online delivery point is 6.7 km. The results also indicate that e-tailing increases the average distance traveled from the regional entry point to the delivery point from 47.15 km for a brick-and-mortar store to 122.75 km for the online delivery points. However, as professional carriers transport the products in bulk to stores or online delivery points, which is more efficient than consumers’ transporting the products to their residences, the results indicate that consumers switching from traditional to e-tailing on average reduce their transport CO₂ footprints by 84% when buying standard consumer electronics products.     

The role of alternative fuel vehicles: Using behavioral and sensor data to model hierarchies in travel

Greater adoption and use of alternative fuel vehicles (AFVs) can be environmentally beneficial and reduce dependence on gasoline. The use of AFVs vis-à-vis conventional gasoline vehicles is not well understood, especially when it comes to travel choices and short-term driving decisions. Using data that contains a sufficiently large number of early AFV adopters (who have overcome obstacles to adoption), this study explores differences in use of AFVs and conventional gasoline vehicles (and hybrid vehicles). The study analyzes large-scale behavioral data integrated with sensor data from global positioning system devices, representing advances in large-scale data analytics. Specifically, it makes sense of data containing 54,043,889 s of speed observations, and 65,652 trips made by 2908 drivers in 5 regions of California. The study answers important research questions about AFV use patterns (e.g., trip frequency and daily vehicle miles traveled) and driving practices. Driving volatility, as one measure of driving practice, is used as a key metric in this study to capture acceleration, and vehicular jerk decisions that exceed certain thresholds during a trip. The results show that AFVs cannot be viewed as monolithic; there are important differences within AFV use, i.e., between plug-in hybrids, battery electric, or compressed natural gas vehicles. Multi-level models are particularly appropriate for analysis, given that the data are nested, i.e., multiple trips are made by different drivers who reside in various regions. Using such models, the study also found that driving volatility varies significantly between trips, driver groups, and regions in California. Some alternative fuel vehicles are associated with calmer driving compared with conventional vehicles. The implications of the results for safety, informed consumer choices and large-scale data analytics are discussed.     

Probabilistic assessment of aviation CO2 emission targets

Passenger demand for air transportation is expected to continue growing into the future. The increase in operations will undoubtedly lead to an escalation in harmful carbon dioxide emissions, an adverse effect that governing bodies have been striving to mitigate. The International Air Transport Association has set aggressive environmental targets for the global aviation industry. This paper investigates the achievability of those targets in the US using a top-down partial equilibrium model of the aviation system complemented with a previously developed fleet turnover procedure. Three ‘enablers’ are considered: aircraft technologies, operational improvements and sustainable biofuels. To account for sources of uncertainty, Monte Carlo simulations are conducted to run a multitude of scenarios. It was found that the likelihood of meeting all targets is extremely low (0.3%) for the expected demand growth rates in the US. Results show that biofuels have the most impact on system CO₂ emissions, responsible for an average 64% of the total savings by 2050 (with aircraft technologies and operational improvements responsible for 31% and 5%, respectively). However, this impact is associated with high uncertainty and very dependent on both biofuel type and availability.     

智能车经济性起步车速规划研究

智能车的车速决策影响燃油经济性。以起步阶段加速工况的燃油经济性为优化目标,建立了瞬态燃油消耗模型,并提出了基于该模型的经济性换挡规律制定方法;根据车辆纵向动力学方程,建立了基于前向欧拉离散方法的车速状态转移方程,以及确定相应的初末约束条件、边界条件;基于动态规划最优性原理,提出了智能车起步过程的经济性车速规划方法,建立了基于速度状态搜索策略;根据Matlab/Simulink和CarSim联合仿真,对比了典型驾驶员速度跟随模式的燃油消耗水平,结果表明,基于动态规划优化后的经济性车速及相应的挡位序列具有良好的节油特性,可为智能车经济驾驶的车速规划及挡位规划提供指导。     

某汽车漏油问题分析及改善技术研究

汽车油路系统中,油箱是汽油的存储容器,对汽车动力的输出起到了至关重要的作用。油箱主要由油箱壳体、汽油泵、油管等零部件组成,它既是功能件,又是安全件,需要满足国家汽车零部件强制性认证要求。特别是在汽车安全性方面,要求非常严格,不允许有汽油泄露问题发生。中国各大整车厂对汽车油路系统的控制也特别关注,汽车销售市场上很少发生汽油泄露的重大事故,但也会有零星车辆发生漏油问题,导致汽车召回。     

基于坐垫体压分布的驾驶员生理状态识别方法

提出一种基于坐垫体压分布识别长途驾驶过程中驾驶员生理状态的方法.开展长途驾驶模拟试验,采集9名被试者在驾驶过程中的坐垫体压分布数据,提取82维分类特征,再采用序列浮动前向选择算法从中选出55维最优特征子空间.试验结果表明,该方法能准确识别驾驶员生理状态,贝叶斯分类正确率可达93.37％.     

南宁市城市道路建设的影响因素分析

文章以南宁市“十二五”期间城市道路网建设规划为例,根据城市道路建设的影响因素类别,剖析并协调道路建设的重大影响因素,并通过指标测算确定了规划期内道路网建设的合理规模.     

基于Ecotect的T型交叉路口布灯方式仿真分析

为研究灯具布置对T型交叉口的照明影响,通过采用照明仿真软件对T型交叉口的交错式、对称式及中心对称式3种布灯方案进行仿真模拟分析,得到了不同布灯方式下的照明强度。结果表明:在相同灯具及条件下,交错式布灯方案最优,但道路终端灯光光强设置需偏大;对称式方案效果次之,交叉口中心位置照度低;中心对称式集中照明效果最好,但相比交错式及对称试其经济能耗最大。通过照明仿真模拟分析,有效验证了交错式布灯方案在T型交叉口照明设计中的优越性。     

Bi-objective routing problem with asymmetrical travel time distributions

Recent studies have confirmed that travelers consider travel time reliability in addition to average travel time when making route choice decisions. In this study, we develop a bi-objective routing model that seeks to simultaneously optimize the average travel time and travel time reliability. The semi-standard deviation (SSD) is chosen as the reliability measure because it reflects travelers' concerns over longer travel time better than the commonly used standard deviation. The Pareto-optimal solutions to the bi-objective model are found by using an improved strength Pareto evolutionary algorithm. Tests on a real-world urban network with field measured travel time data have demonstrated good performance of the algorithm in the aspects, such as computational efficiency, quick convergence, and closeness to the global Pareto-optimal. Overall, the bi-objective routing model generates reasonable path recommendations. The SSD-based model is sensitive to the asymmetry of travel time distribution and tends to avoid paths with excessively long delays. This would be particularly helpful to those users placing high values on travel time reliability.     

泥水盾构水平直管内石碴起动速度研究

泥水盾构环流系统是将开挖下的石碴从掘进界面输送出来的核心系统,石碴在环流管道内的流动特性是决定石碴能否顺利输送的关键。论文基于计算流体力学和离散单元法建立泥水盾构环流系统水平直管内石碴运动模型,研究石碴形状系数、等容粒径和数量对石碴起动速度的影响。仿真研究结果表明： 在石碴等容粒径相等情况下,不同形状的石碴起动速度大小顺序为： 椭球体>扁平状>近球形;当石碴等容粒径为38.5、42.5、53.3、56.2、58 mm时,石碴起动速度分别为0.48、1.06、1.26、2.36、4.7 m/s,随等容粒径的增大呈指数增大;随数量的增多呈对数增长,多颗粒起动速度随数量的增多增幅较为平缓,最大增幅为21%。