基于模型视图控制器框架的多功能车辆总线一致性测试的实现

一致性测试是通信设备实现互操作的必要条件.简单介绍了对列车通信网(TCN)进行一致性测试的重要性和模型视图控制器(MVC)框架.重点描写了对多功能车辆总线(MVB)进行一致性测试所包括的内容、测试的方案和步骤,以及如何在MVC这种优秀的框架中实现.在实验室搭建了测试平台,使用的是Duagon公司D114MVB网卡,测试结果真实有效.     

《电加热道岔融雪系统设备》TB/T3539-2018解读

解读《电加热道岔融雪系统设备》与《客运专线铁路信号产品暂行技术条件汇编——电加热道岔融雪系统设备》标准的共性部分,并从编制背景、标准定位、技术要求、试验方法和检验规则等方面,分析两者之间的差异性.     

基于有限元分析的城市客车扶手杆结构改进

基于国内城市客车扶手杆应用的现状,对顶置电池城市客车扶手结构进行了有限元分析,为扶手整体结构的强度、刚度设计提供理论依据。     

纯电动商用车电池热管理技术研究

能源危机和环境污染问题已成全球关注的焦点,新能源汽车顺势而为,纯电动汽车采用纯电驱动,更加节能、环保。随着纯电动汽车的发展,车辆的安全性、续航里程能力得到了关注,动力电池的性能很大程度上影响着整车性能,为了提升动力电池系统性能,避免热失控,研究高性能动力电池热管理系统至关重要。     

Using GPS-data to determine optimum electric vehicle ranges: A Michigan case study

Fuel-switching personal transportation from gasoline to electricity offers many advantages, including lower noise, zero local air pollution, and petroleum-independence. But alleviations of greenhouse gas (GHG) emissions are more nuanced, due to many factors, including the car’s battery range. We use GPS-based trip data to determine use type-specific, GHG-optimized ranges. The dataset comprises 412 cars and 384,869 individual trips in Ann Arbor, Michigan, USA. We use previously developed algorithms to determine driver types, such as using the car to commute or not. Calibrating an existing life cycle GHG model to a forecast, low-carbon grid for Ann Arbor, we find that the optimum range varies not only with the drive train architecture (plugin-hybrid versus battery-only) and charging technology (fast versus slow) but also with the driver type. Across the 108 scenarios we investigated, the range that yields lowest GHG varies from 65 km (55+ year old drivers, ultrafast charging, plugin-hybrid) to 158 km (16–34 year old drivers, overnight charging, battery-only). The optimum GHG reduction that electric cars offer – here conservatively measured versus gasoline-only hybrid cars – is fairly stable, between 29% (16–34 year old drivers, overnight charging, battery-only) and 46% (commuters, ultrafast charging, plugin-hybrid). The electrification of total distances is between 66% and 86%. However, if cars do not have the optimum range, these metrics drop substantially. We conclude that matching the range to drivers’ typical trip distances, charging technology, and drivetrain is a crucial pre-requisite for electric vehicles to achieve their highest potential to reduce GHG emissions in personal transportation.     

Electric vehicle charging station locations: Elastic demand,station congestion,and network equilibrium

Battery-only electric vehicles (BEVs) generally offer better air quality through lowered emissions, along with energy savings and security. The issue of long-duration battery charging makes charging-station placement and design key for BEV adoption rates. This work uses genetic algorithms to identify profit-maximizing station placement and design details, with applications that reflect the costs of installing, operating, and maintaining service equipment, including land acquisition. Fast electric vehicle charging stations (EVCSs) are placed across a congested city's network subject to stochastic demand for charging under a user-equilibrium traffic assignment. BEV users’ station choices consider endogenously determined travel times and on-site charging queues. The model allows for congested-travel and congested-station feedback into travelers’ route choices under elastic demand and BEV owners’ station choices, as well as charging price elasticity for BEV charging users.Boston-network results suggest that EVCSs should locate mostly along major highways, which may be a common finding for other metro settings. If 10% of current EV owners seek to charge en route, a user fee of $6 for a 30-min charging session is not enough for station profitability under a 5-year time horizon in this region. However, $10 per BEV charging delivers a 5-year profit of $0.82 million, and 11 cords across 3 stations are enough to accommodate a near-term charging demand in this Boston-area application. Shorter charging sessions, higher fees, and/or allowing for more cords per site also increase profits generally, everything else constant. Power-grid and station upgrades should keep pace with demand, to maximize profits over time, and avoid on-site congestion.     

宜昌市夜明珠路总体方案研究

结合宜昌BRT(快速公交系统)建设对宜昌市夜明珠路进行改造,受众多因素控制,道路改造难度较高。介绍了工程建设条件及总体设计方案。总体方案结合路线两侧现状地形、控制因素等不同特点,将路线分为沿河线段和合流线段两个典型路段,并进行方案比选,综合各方面因素,得出了推荐方案。     

Understanding the impacts of a combination of service improvement strategies on bus running time and passenger’s perception

Transit agencies implement many strategies in order to provide an attractive transportation service. This article aims to evaluate the impacts of implementing a combination of strategies, designed to improve the bus transit service, on running time and passenger satisfaction. These strategies include using smart card fare collection, introducing limited-stop bus service, implementing reserved bus lanes, using articulated buses, and implementing transit signal priority (TSP). This study uses stop-level data collected from the Société de transport de Montréal (STM)’s automatic vehicle location (AVL) and automatic passenger count (APC) systems, in Montréal, Canada. The combination of these strategies has lead to a 10.5% decline in running time along the limited stop service compared to the regular service. The regular route running time has increased by 1% on average compared to the initial time period. The study also shows that riders are generally satisfied with the service improvements. They tend to overestimate the savings associated with the implementation of this combination of strategies by 3.5-6.0 min and by 2.5-4.1 min for both the regular route and the limited stop service, respectively. This study helps transit planners and policy makers to better understand the effects of implementing a combination of strategies to improve running time and passenger’s perception of these changes in service.     

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.     

Analytical Approach to Evaluating Transit Signal Priority

Successful deployment of transit signal priority (TSP) systems requires thorough laboratory evaluation before field implementation. Traffic simulation is a powerful tool in this regard; however, it requires tremendous efforts toward network coding, data collection, and model calibration. Besides, simulation models tend to be project specific, and the models developed for one project are often discarded upon the completion of that project. In this paper, it is shown that the impacts of two fundamental TSP strategies (early green and extended green) can be evaluated using an analytical approach. The impacts of the above two strategies on both the prioritized and the nonprioritized approaches are illustrated using graphical as well as analytical methods. A simulation study is then conducted for comparison analyses, followed by a statistical approach for the test of generality.