The design of electric vehicle charging network

The promotion of Electric Vehicles (EVs) has become a key measure of the governments in their attempt to reduce greenhouse gas emissions. However, range anxiety is a big barrier for drivers to choose EVs over traditional vehicles. Installing more charging stations in appropriate locations can relieve EV drivers’ range anxiety. To determine the locations of public charging stations, we propose two optimization models for two different charging modes - fast and slow charging, which aim at minimizing the total cost while satisfying certain coverage goal. Instead of using discrete points, we use geometric objects to represent charging demands. Importantly, to resolve the partial coverage problem (PCP) for networks, we extend the polygon overlay method to split the demands on the road network. After applying the models to Greater Toronto and Hamilton Area (GTHA) and to Downtown Toronto, we show that the proposed models are practical and effective in determining the locations of charging stations. Moreover, they can eliminate PCP and provide much more accurate results than the complementary partial coverage method (CP).     

How might potential future plug-in electric vehicle buyers differ from current “Pioneer” owners?

The entry of various plug-in electric vehicles (PEVs) into the passenger vehicle sector provides novel opportunities to learn about the potential for future PEV markets. However, early PEV buyers (or “Pioneers”) can substantially differ from present conventional vehicle owners that have interest in purchasing PEVs in the future (or the “Potential Early Mainstream buyers”). To compare the characteristics, preferences, and motivations of Pioneers and Potential Early Mainstream buyers, we draw data from the Canadian Plug-in Electric Vehicle Study, a three-part mixed-mode survey with samples of PEV owners (n = 94) and conventional new vehicle buyers (n = 1754). We identify several significant differences in household characteristics, including income, education, and recharge access. In terms of preferences, Pioneers express extremely high valuation of PEVs and prefer pure battery electric vehicle (BEV) designs over plug-in hybrid electric (PHEV) designs. In contrast, Potential Early Mainstream respondents prefer PHEVs. Both Pioneer and Potential Early Mainstream respondents are similarly cautious about controlled charging programs, but Pioneers place five times as much value on using electricity generated from renewable sources than the Potential Early Mainstream. Pioneers also tend to have different motivations, including significantly higher levels of environmental concern, and higher engagement in environment- and technology-oriented lifestyles. Policymakers, automakers, and electric utilities that anticipate a transition to electric mobility ought to consider how potential future PEV buyers may differ in their vehicle preferences, usage and motivations relative to current PEV owners.     

A novel dynamic segmentation model for identification and prioritization of black spots based on the pattern of potential for safety improvement

Road segmentation is one of the most important steps in identification of high accident-proneness segments of a road. Based on the ratio of the Potential to Safety Improvement (PSI) along the road, the objective of the paper is to propose a novel dynamic road segmentation model. According to the fundamental model assumption, the determined segments must have the same pattern of PSI. Experimental results obtained from implementation of the proposed method took four Performance Measures (PMs) into consideration; namely, Crash Frequency, Crash Rate, Equivalent Property Damage Only, and Expected Average Crash Frequency with Empirical Bayes adjustment into the accident data obtained from Highway 37 located between two cities in Iran. Results indicated the low sensitivity of the method to PMs. In comparison with the real high accident-proneness segments, identified High Crash Road Segments (HCRS) obtained from the model, demonstrated the potential of the method to recognize the position and length of high accident-proneness segments accurately. Based on the road repair and maintenance costs limitation index for safety improvement, in an attempt to compare the proposed method of road segmentation with conventional ones, results demonstrated the efficient performance of the proposed method. So as to identify 20 percent HCRS located on a read, the proposed method showed an improvement of 38 and 57 percent in comparison with the best and worst outcomes derived from conventional road segmentation methods.     

高速铁路列车制动曲线计算精确度与效率分析

在高速铁路列车控制系统中,车载设备依据行车许可、线路数据和列车制动参数计算目标距离连续速度控制模式曲线,对列车位置和速度进行实时监控,保证列车安全、高效运行.在不同速度下,高速铁路列车具有不同的制动能力.在现有的高速列车控制系统中,对速度进行有限数量分段,分段内采用固定减速度,以较少速度分段计算速度监控曲线.如何对列车...     

复杂背景下多目标图像分割方法研究

真实路况中的运动车辆图像进行图像分割时,图像中往往存在多个车辆车牌信息,且这些车牌信息具有尺度不一,位置随机等特点,加之光照及复杂背景的影响,如何兼顾多个车辆车牌的分割效果是车辆检测和跟踪领域亟待解决的问题.为了解决这类工程应用中的问题,需要在尺度空间下对多目标图像进行分析.因本文在前期多尺度分割模型的基础上引入视觉注意机制,利用不变性特征实现多目标的定位及最优分割尺度的选取.经大量实验测试结果表明,该算法较好地实现了图像中多个车牌图像的分割并且具有较好的分割效果.     

北京市居民出行方式人群分类

为识别出行者选择不同出行方式的差异,将多重对应分析法应用到行为领域进行出行人群分类。根据北京市居民日常出行问卷调查,利用结构方程模型选择出对出行方式影响比较显著的变量,分析出行人属性、出行属性、出行偏好和出行方式之间的定量关系。根据选择出来的指标把出行人群划分成5个类别,分析每类群体所倾向的出行方式并针对不同群体提出相应的措施,以引导居民出行。     

遥感图像中港口水域的检测方法

通过对港口水域自动检测问题的深入研究,提出一种基于熵的感兴趣区域自动提取算法.将水域和陆地背景分离,并对分割后的二值图像进行标记,最后提取出水域.     

Will autonomous vehicles change residential location and vehicle ownership? Glimpses from Georgia

Many studies have begun investigating possible transportation landscapes in the autonomous vehicle (AV) era, but empirical results on longer-term decisions are limited. We address this gap using data collected from a survey designed and implemented for Georgia residents in 2017–2018. Focusing on a hypothetical all-AV future, this section of the survey included questions regarding advantages/disadvantages of AVs, short-term mode choice impacts, medium-term impacts on activity patterns, and long-term behavioral changes – specifically, whether/how AVs will influence individuals to change residential location and the number of cars in the household. We hypothesize that AVs could act in concert with attitudinal preferences to stimulate changes in these long-term decisions, and that some medium-term activity changes triggered by AVs could motivate people to relocate their residence or shed household vehicles. We applied exploratory factor analysis to measure the perceived likelihood that AVs would prompt various medium-term changes. We then included some of those measures, among other variables, in a cross-nested logit (CNL) model of the choice of the residential location/vehicle ownership bundle. Although more than half of respondents expected “no change” in their bundle, we found that younger, lower income, pro-suburban, and pro-non-car-mode individuals were more likely to anticipate changing their selections. In addition, some expected medium-term impacts of AVs influenced changes in these longer-term choices. We further applied the CNL model to two population segments (Atlanta and non-Atlanta-region residents). We found notable improvement in goodness of fit and different effects of factors across segments, signifying the existence of geography-related taste heterogeneity.     

Segmentation of complex objects’ sonar images using parameter-fixed MRF model

1 Introduction1 Imaging sonar has been widely used in the case of mine finding, underwater seeking and sound vision system of AUV as an important method. Therefore the object recognition based on sonar image, especially complex object, is one of the impor…     

HIERARCHICAL CONTENT BASED IMAGE RETRIEVAL

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