Shared autonomous electric vehicle (SAEV) operations across the Austin,Texas network with charging infrastructure decisions

Shared autonomous vehicles, or SAVs, have attracted significant public and private interest because of their opportunity to simplify vehicle access, avoid parking costs, reduce fleet size, and, ultimately, save many travelers time and money. One way to extend these benefits is through an electric vehicle (EV) fleet. EVs are especially suited for this heavy usage due to their lower energy costs and reduced maintenance needs. As the price of EV batteries continues to fall, charging facilities become more convenient, and renewable energy sources grow in market share, EVs will become more economically and environmentally competitive with conventionally fueled vehicles. EVs are limited by their distance range and charge times, so these are important factors when considering operations of a large, electric SAV (SAEV) fleet.This study simulated performance characteristics of SAEV fleets serving travelers across the Austin, Texas 6-county region. The simulation works in sync with the agent-based simulator MATSim, with SAEV modeling as a new mode. Charging stations are placed, as needed, to serve all trips requested (under 75 km or 47 miles in length) over 30 days of initial model runs. Simulation of distinctive fleet sizes requiring different charge times and exhibiting different ranges, suggests that the number of station locations depends almost wholly on vehicle range. Reducing charge times does lower fleet response times (to trip requests), but increasing fleet size improves response times the most. Increasing range above 175 km (109 miles) does not appear to improve response times for this region and trips originating in the urban core are served the quickest. Unoccupied travel accounted for 19.6% of SAEV mileage on average, with driving to charging stations accounting for 31.5% of this empty-vehicle mileage. This study found that there appears to be a limit on how much response time can be improved through decreasing charge times or increasing vehicle range.     

A new approach for improving the performance of freight train timetabling of a single-track railway system

A new approach for improving the performance of freight train timetabling for single-track railways is proposed. Using the idea of a fixed-block signaling system, we develop a matrix representation to express the occupation of inter- and intra-station tracks by trains illustrating the train blocking time diagram in its entirety. Train departure times, dwell times, and unnecessary stopping are adjusted to reduce average train travel time and single train travel time. Conflicts between successive stations and within stations are identified and solved. A fuzzy logic system is further used to adjust the range of train departure times and checks are made to determine whether dwell times and time intervals can be adjusted for passenger and freight trains at congested stations to minimize train waiting times. By combining manual scheduling expertise with the fuzzy inference method, timetable efficiency is significantly improved and becomes more flexible.     

液化天然气(LNG)汽车日常检查方法及维护技术综述

天然气是一种优质的替代燃料,具有污染小、安全系数高、运行费用低等优点。天然气已经成为城市公共交通领域应用最为成功和广泛的车辆替代燃料技术,为推动交通运输行业的节能减排做出了显著的贡献。液化天然气汽车,作为天然气汽车的一种类型,与传统汽柴油车相比,液化天然气汽车安装了包括液化天然气气瓶、气管路及各种控制阀门和仪表在内的专用装置,在对液化天然气汽车进行日常检查时需要针对液化天然气汽车的专用装置进行重点检查。本文则针对液化天然气汽车的特点,对液化天然气汽车的正确使用方法、日常检查方法及维护技术要求、以及相关注意事项三个方面进行了解读,为指导液化天然气汽车进行日常检查与定期维护提供了技术参考。     

天然气车辆推广应用政策措施研究

天然气车辆因其能源结构调整效益和节能减排效益,在道路运输行业应用日趋广泛。但同时,其后期应用过程中的问题也随之显现,为引导道路运输企业更加科学合理的应用天然气汽车,本文通过对当前天然气车辆应用现状的分析,总结应用过程中的问题,对天然气车辆开展成本效益分析,最终提出适合天然气车辆推广应用的政策建议。     

管道内防腐层补口技术研究进展

管道内衬或内防腐涂层现场施焊时,焊缝两侧的防腐层会由于高温作用而破坏,使环焊缝处不能得到有效的保护,内补口技术也成为影响管道质量的主要瓶颈。文中介绍了国内外主要的内补口技术,如内补口机法、内衬保护套（管）焊接法、不锈钢接头法、机械连接法等,并指出各方法的特点。文中还就内补口技术的质量控制和研究方向等提出了建议。     

广西道路货物运输安全管理对策研究

文章介绍了当前广西道路货物运输行业的基本情况,阐述了广西道路货物运输安全生产存在的主要问题,并从行政管理和企业管理的角度,提出了切合广西道路货物运输行业实际的安全管理措施。     

The Alternative Motor Fuels Act, alternative-fuel vehicles, and greenhouse gas emissions

The corporate average fuel economy (CAFE) standard is the major policy tool to improve the fleet average miles per gallon of automobile manufacturers in the US. The Alternative Motor Fuels Act (AMFA) provides special treatment in calculating the fuel economy of alternative-fuel vehicles to give manufacturers CAFE incentives to produce more alternative-fuel vehicles. AMFA has as its goals an increase in the production of alternative-fuel vehicles and a decrease in gasoline consumption and greenhouse gas emissions. This paper examines theoretically the effects of the program set up under AMFA. It finds that, under some conditions, this program may actually increase the production of fuel-inefficient gasoline vehicles, gasoline consumption and greenhouse gas emissions.     

考虑电动自行车骑行的非机动车道最大纵坡探讨

现行规范要求非机动车道的最大纵坡不超过3.5%,但要在山地城市、跨江桥梁两岸慢行系统衔接等场景下满足该要求存在工程占地大、造价高、可实施性差等问题,考虑到电动自行车在非机动车出行方式中占比越来越高的实际情况,以《电动自行车安全技术规范》（GB 17761—2018）规定的国标电动自行车为研究对象对骑行过程进行受力分析,提出了可满足电动自行车骑行要求的非机动车道最大纵坡参考值,可供电动车骑行占比高的类似项目参考。     

深圳港区跨海货运通道方案比选研究

深圳市妈湾跨海通道蛇口—赤湾连接线工程是深圳港区跨海货运通道的一部分,建设条件复杂,为此开展多线位、多工法方案综合比选,以确定兼顾各方需求的优化方案。对工程的功能定位、港区货运需求、沿线用地情况及规划等因素进行了详细分析,对跨海货运快速路通道从选线、工法、设计方案逐层递进,进行多方案比选。综合考虑了地下空间充分利用、减少对港区用地占用,降低施工对疏港交通影响等因素,确定了项目的选线方案以及双管单层15 m盾构的工法和设计方案。成果为深圳市等港区货运通道的选线及设计方案提供借鉴。     

基于深度学习的智能车辆视觉里程计技术发展综述

针对基于模型的视觉里程计在光照条件恶劣的情况下存在鲁棒性差、回环检测准确率低、动态场景中精度不够、无法对场景进行语义理解等问题,利用深度学习可以弥补其不足。首先,简略介绍了基于模型的里程计的研究现状,然后对比了常用的智能车数据集,将基于深度学习的视觉里程计分为有监督学习、无监督学习和模型法与深度学习结合3种,从网络结构、输入和输出特征、鲁棒性等方面进行分析,最后,讨论了基于深度学习的智能车辆视觉里程计研究热点,从视觉里程计在动态场景的鲁棒性优化、多传感器融合、场景语义分割3个方面对智能车辆视觉里程计技术的发展趋势进行了展望。