未来之城的崛起

近日,交通运输部、河北省政府联合发布《支撑雄安新区交通运输高质量发展标准体系》,旨在以高水平标准体系助力创造"雄安质量",加快建设交通强国先行区. 雄安,在2017 年为全国所知的城市.     

京津冀轨道交通网将越织越密

今年政府工作报告提出"扎实推动京津冀协同发展""高标准、高质量建设雄安新区".全国人大代表、北京铁路局集团公司董事长赵春雷表示,"十四五"期间,京津冀轨道交通网将越织越密,一批新线、新站将加快建设.     

雄安站开出京雄城际首列"复兴号"

2020年12月27日上午10点18分,C2702次复兴号列车从北京至雄安新区城际铁路(以下简称京雄城际)最大的新建车站——雄安站首发.20分钟后即10点38分,C2701次复兴号列车从北京西站首发.这两趟列车的首发,标志着京雄城际全线开通运营.其中,北京西至雄安的最短运行时间为50分钟,北京西至大兴机场最短运行时间为29分钟,大兴机场至雄安站最短运行时间为19分钟.     

船舶污染事故溢油量测算方法比较

本文论述了船舶污染事故溢油量的两大类测算方法,分别是适用于风险评价的溢油量预测方法和适用于已发生的船舶污染事故溢油量的估算方法,分析比较了经验公式法、MARPOL方法、基于CFD的数值模型计算法、基于监视监测的估算法和基于事故船舶油量检测估算方法的基本假设、原理及其主要应用领域。本文可为船舶污染事故溢油量的快速准确估算提供方法选择依据,为改进估算方法提供建议。     

交通运输继续发挥先行官作用

2021年,交通运输将继续为国家重大战略实施当好先行官.一是京津冀交通一体化暨雄安新区综合交通运输体系建设.二是长江经济带综合立体交通走廊建设.三是粤港澳大湾区交通运输发展.四是长三角交通运输更高质量一体化发展.五是黄河流域交通运输生态保护和高质量发展.六是成渝地区双城经济圈交通运输发展.     

基于客运量的氢燃料公交车节能减碳计算方法研究

在“双碳”背景下,氢能源作为零污染零排放的典型代表,其关键技术日益成熟,氢燃料公交车为公交企业带来的益处也逐渐显现。目前,公交车辆节能减碳的计算方法并没有统一的标准,同行业之间公布的节能量和减碳量也参差不齐,缺乏科学合理的计算方法。本文参照现有节能量的计算方法,建立了一套适用于城市公交企业实际运营情况的,即基于客运量的节能减碳计算方法,并以北京公交氢燃料公交车为例,验证了该方法的可行性,对企业分析节能减碳效果,评价节能减碳管理水平具有较好的参考意义。     

交通运输行业碳交易机制分析

采用"自上而下"的能源消耗碳排放量的测算方法,对我国交通运输仓储和邮政业能源消费量和碳排放量进行了科学测算。并基于行业能耗特点,从基本定位、实现路径、市场机制等方面分析了交通运输行业碳交易机制的实现方式,提出了交通运输行业碳交易实现机制的总体发展建议。     

基于NSGA-Ⅱ算法的需求响应式公交多目标线网优化模型

需求响应式公交服务作为新形势下提出的一种多元化的客运服务模式，不仅解决了大多数通勤者舒适便捷的通勤需求，而且有效提高了道路使用效率，缓解了早晚高峰时段的道路交通压力。本文针对需求响应式公交线网优化，以运营企业成本最小和乘客出行时间最短作为优化目标，构建需求响应式公交多目标线网优化模型，采用NSGA-Ⅱ算法进行求解。以雄安新区运行的需求响应式公交为实例，基于运行两个月的实际数据对模型参数进行标定，进一步增加了模型的精度，验证了模型的可行性与有效性。     

基于全生命周期分析的高速公路减碳技术碳足迹核算研究

为了推动绿色公路建设,识别绿色低碳材料、工艺与技术,本文采用全生命周期碳排放测算方法,对延黄高速公路使用的绿色建造技术进行碳排放测算,采用帕累托法则分析各绿色建造技术建筑材料、机械设备的碳排放数据,结果表明：桥梁上部结构由钢桥变为混凝土预制的减碳率达到55.78%,隧道工程优化为路堑工程的减碳率达44.66%,拱形骨架护坡优化为CBS边坡防护的减碳率达21.36%;钢材和混凝土是公路建设材料的主要碳排放来源,是碳减排重点控制材料,应该通过优化设计、改进施工工艺或使用低碳排放的同类替换材料等方法降低原材料碳排放;小型装载机、凿毛机是混凝土预制桥梁机械设备的主要碳排放来源,30装载机是CBS边坡防护建设中的主要碳排放来源。     

贯彻GB1589-2016对高速公路货车的影响

本文首先通过分析14个含有轴型字段省份的高速公路计重数据,测算出GB 1589-2016实施后的货物溢出量及所需增加的货车数量,并客观分析了对货物品种的影响。其次针对以轴数计重的省份,举例测算了GB1589-2016实施后的货运形势。最后本文结合测算数据全面分析GB1589-2016实施所存在得利好与负面影响。     

Carbon and energy footprints of electric delivery trucks: A hybrid multi-regional input-output life cycle assessment

Due to frequent stop-and-go operation and long idling periods when driving in congested urban areas, the electrification of commercial delivery trucks has become an interesting topic nationwide. In this study, environmental impacts of various alternative delivery trucks including battery electric, diesel, diesel-electric hybrid, and compressed natural gas trucks are analyzed. A novel life cycle assessment method, an environmentally-extended multi-region input-output analysis, is utilized to calculate energy and carbon footprints throughout the supply chain of alternative delivery trucks. The uncertainties due to fuel consumption or other key parameter variations in real life, data ranges are taken into consideration using a Monte Carlo simulation. Furthermore, variations in regional electricity mix greenhouse gas emission are also considered to present a region-specific assessment for each vehicle type. According to the analysis results, although the battery electric delivery trucks have zero tailpipe emission, electric trucks are not expected to have lower environmental impacts compared to other alternatives. On average, the electric trucks have slightly more greenhouse emissions and energy consumption than those of other trucks. The regional analysis also indicates that the percentage of cleaner power sources in the electricity mix plays an important role in the life cycle greenhouse gas emission impacts of electric trucks.     

Impacts of replacement of engine powered vehicles by electric vehicles on energy consumption and CO2 emissions

This paper evaluates the impacts on energy consumption and carbon dioxide (CO₂) emissions from the introduction of electric vehicles into a smart grid, as a case study. The AVL Cruise software was used to simulate two vehicles, one electric and the other engine-powered, both operating under the New European Driving Cycle (NEDC), in order to calculate carbon dioxide (CO₂) emissions, fuel consumption and energy efficiency. Available carbon dioxide data from electric power generation in Brazil were used for comparison with the simulated results. In addition, scenarios of gradual introduction of electric vehicles in a taxi fleet operating with a smart grid system in Sete Lagoas city, MG, Brazil, were made to evaluate their impacts. The results demonstrate that CO₂ emissions from the electric vehicle fleet can be from 10 to 26 times lower than that of the engine-powered vehicle fleet. In addition, the scenarios indicate that even with high factors of CO₂ emissions from energy generation, significant reductions of annual emissions are obtained with the introduction of electric vehicles in the fleet.     

Comparison of energy demands of drone-based and ground-based parcel delivery services

Drones are one of the most intensively studied technologies in logistics in recent years. They combine technological features matching current trends in transport industry and society like autonomy, flexibility, and agility. Among the various concepts for using drones in logistics, parcel delivery is one of the most popular application scenarios. Companies like Amazon test drones particularly for last-mile delivery intending to achieve both reducing total cost and increasing customer satisfaction by fast deliveries. As drones are electric vehicles, they are also often claimed to be an eco-friendly mean of transportation.In this paper an energy consumption model for drones is proposed to describe the energy demand for drone deliveries depending on environmental conditions and the flight pattern. The model is used to simulate the energy demand of a stationary parcel delivery system which serves a set customers from a depot. The energy consumed by drones is compared to the energy demand of Diesel trucks and electric trucks serving the same customers from the same depot.The results indicate that switching to a solely drone-based parcel delivery system is not worthwhile from an energetic perspective in most scenarios. A stationary drone-based parcel delivery system requires more energy than a truck-based parcel delivery system particularly in urban areas where customer density is high and truck tours are comparatively short. In rather rural settings with long distances between customers, a drone-based parcel delivery system creates an energy demand comparable to a parcel delivery system with electric trucks provided environmental conditions are moderate.     

Life cycle emissions and cost model for urban light duty vehicles

The growth of vehicle sales and use internationally requires the consumption of significant quantities of energy and materials, and contributes to the deterioration of air-quality and climate conditions. Advanced propulsion systems and electric drive vehicles have substantially different characteristics and impacts. They require life cycle assessments and detailed comparisons with gasoline powered vehicles which, in turn, should lead to critical updates of traditional models and assumptions. For a comprehensive comparison of advanced and traditional light duty vehicles, a model is developed that integrates external costs, including emissions and time losses, with societal and consumer life cycle costs. Life cycle emissions and time losses are converted into costs for seven urban light duty vehicles. The results, which are based on vehicle technology characteristics and transportation impacts on environment, facilitate vehicle comparisons and support policy making in transportation. Substantially, more sustainable urban transportation can be achieved in the short-term by promoting policies that increase vehicle occupancy; in the intermediate-term by increasing the share of hybrid vehicles in the car market and in the long-term by the widespread use of electric vehicles. A sensitivity-analysis of life cost results revealed that vehicle costs change significantly for different geographical areas depending on vehicle taxation, pricing of gasoline, electric power and pollution. Current practices in carbon and air quality pricing favor oil and coal based technologies. However, increasing the cost of electricity from coal and other fossil fuels would increase the variable cost for electric vehicles, and tend to favor the variable cost of hybrid vehicles.     

Fuel price elasticities in the U.S. combination trucking sector

This paper estimates fuel price elasticities of combination trucking operations in the United States between 1970 and 2012. We evaluate trucking operations in terms of vehicle miles traveled and fuel consumption for combination trucks. Our explanatory variables include measures of economic activity, energy prices, and indicator variables that account for important regulatory shifts and changes in data collection and reporting in national transportation datasets. Our results suggest that fuel price elasticities in the United States’ trucking sector have shifted from an elastic environment in the 1970s to a relatively inelastic environment today. We discuss the importance of these results for policymakers in light of new policies that aim to limit energy consumption and reduce greenhouse gas emissions from heavy-duty vehicles.     

Modelling energy consumption of electric freight vehicles in urban pickup/delivery operations: analysis and estimation on a real-world dataset

Electric Freight Vehicles (EFVs) are a promising and increasingly popular alternative to conventional trucks in urban pickup/delivery operations. A key concerned research topic is to develop trip-based Tank-to-Wheel (TTW) analyses/models for EFVs energy consumption: notably, there are just a few studies in this area. Leveraging an earlier research on passenger electric vehicles, this paper aims at filling this gap by proposing a microscopic backward highly-resolved power-based EFVs energy consumption model (EFVs-ECM). The model is estimated and validated against real-world data, collected on a fleet of five EFVs in the city centre of Rome, for a total of 144 observed trips between subsequent pickup/delivery stops. Different model specifications are tested and contrasted, with promising results, in line with previous findings on electric passenger vehicles.     

Electric vehicles’ energy consumption measurement and estimation

Use of electric vehicles (EVs) has been viewed by many as a way to significantly reduce oil dependence, operate vehicles more efficiently, and reduce carbon emissions. Due to the potential benefits of EVs, the federal and local governments have allocated considerable funding and taken a number of legislative and regulatory steps to promote EV deployment and adoption. With this momentum, it is not difficult to see that in the near future EVs could gain a significant market penetration, particularly in densely populated urban areas with systemic air quality problems. We will soon face one of the biggest challenges: how to improve efficiency for EV transportation system? This research takes the first step in tackling this challenge by addressing a fundamental issue, i.e. how to measure and estimate EVs’ energy consumption. In detail, this paper first presents a system which can collect in-use EV data and vehicle driving data. This system then has been installed in an EV conversion vehicle built in this research as a test vehicle. Approximately 5 months of EV data have been collected and these data have been used to analyze both EV performance and driver behaviors. The analysis shows that the EV is more efficient when driving on in-city routes than driving on freeway routes. Further investigation of this particular EV driver’s route choice behavior indicates that the EV user tries to balance the trade-off between travel time and energy consumption. Although more data are needed in order to generalize this finding, this observation could be important and might bring changes to the traffic assignment for future transportation system with a significant share of EVs. Additionally, this research analyzes the relationships among the EV’s power, the vehicle’s velocity, acceleration, and the roadway grade. Based on the analysis results, this paper further proposes an analytical EV power estimation model. The evaluation results using the test vehicle show that the proposed model can successfully estimate EV’s instantaneous power and trip energy consumption. Future research will focus on applying the proposed EV power estimation model to improve EVs’ energy efficiency.     

Dynamic charging infrastructure deployment for plug-in hybrid electric trucks

Inspired by the rapid development of charging-while-driving (CWD) technology, plans are ongoing in government agencies worldwide for the development of electrified road freight transportation systems through the deployment of dynamic charging lanes. This en route method for the charging of plug-in hybrid electric trucks is expected to supplement the more conventional charging technique, thus enabling significant reduction in fossil fuel consumption and pollutant emission from road freight transportation. In this study, we investigated the optimal deployment of dynamic charging lanes for plug-in hybrid electric trucks. First, we developed a multi-class multi-criteria user equilibrium model of the route choice behaviors of truck and passenger car drivers and the resultant equilibrium flow distributions. Considering that the developed user equilibrium model may have non-unique flow distributions, a robust deployment of dynamic charging lanes that optimizes the system performance under the worst-case flow distributions was targeted. The problem was formulated as a generalized semi-infinite min-max program, and a heuristic algorithm for solving it was proposed. This paper includes numerical examples that were used to demonstrate the application of the developed models and solution algorithms.     

Forward power-train energy management modeling for assessing benefits of integrating predictive traffic data into plug-in-hybrid electric vehicles

In this paper, a forward power-train plug-in hybrid electric vehicle model with an energy management system and a cycle optimization algorithm is evaluated for energy efficiency. Using wirelessly communicated predictive traffic data for vehicles in a roadway network, as envisioned in intelligent transportation systems, traffic prediction cycles are optimized using a cycle optimization strategy. This resulted in a 56-86% fuel efficiency improvements for conventional vehicles. When combined with the plug-in hybrid electric vehicle power management system, about 115% energy efficiency improvements were achieved. Further improvements in the overall energy efficiency of the network were achieved with increased penetration rates of the intelligent transportation assisted enabled plug-in hybrid electric vehicles.     

Analysis of the impact of hybrid vehicles on energy systems in Japan

This study examines the impact of using hybrid vehicles for passenger transportation on carbon emissions in the Japanese energy system. A partial equilibrium model of the energy sector has been developed to forecast changes in the energy system out to the year 2040. The model can account for changes in technology capacities, fuels, and consumption in response to policy initiatives, such as taxes. We find that hybrid vehicles are more efficient in reducing carbon dioxide emission than conventional vehicles. Hybrid vehicles have a great impact on reducing carbon emissions when BTU taxes are imposed, which in turn has the advantage of encouraging a more diverse set of technologies and fuels.