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Reducing traffic volumes and CO2-emissions from freight transport has proven difficult in many countries. Although the increasing suburbanization of warehouses is seen as a relevant land use trend, comprehensive analyses of their impact remain scarce. This study uses real data in modeling transport, costs, environmental and modal effects from warehouse relocations around Oslo and Trondheim (Norway). Results indicate that for Oslo, traffic performance (ton-km), CO2-emissions, and transport costs increase following warehouse suburbanization. For Trondheim, transport performance and CO2-emissions increase less, while transport costs decrease marginally. We conclude that specific case characteristics (geography and trade patterns) are important in determining the strength and direction of effects, and expect that common concomitant developments (warehouse centralization and consolidation) would lead to more pronounced results. Our findings confirm some, but challenge other, findings from the relatively scarcely literature available. Finally, the study's more general insights and observations can help advance similar analyses beyond Norway. 相似文献
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Driving volatility captures the extent of speed variations when a vehicle is being driven. Extreme longitudinal variations signify hard acceleration or braking. Warnings and alerts given to drivers can reduce such volatility potentially improving safety, energy use, and emissions. This study develops a fundamental understanding of instantaneous driving decisions, needed for hazard anticipation and notification systems, and distinguishes normal from anomalous driving. In this study, driving task is divided into distinct yet unobserved regimes. The research issue is to characterize and quantify these regimes in typical driving cycles and the associated volatility of each regime, explore when the regimes change and the key correlates associated with each regime. Using Basic Safety Message (BSM) data from the Safety Pilot Model Deployment in Ann Arbor, Michigan, two- and three-regime Dynamic Markov switching models are estimated for several trips undertaken on various roadway types. While thousands of instrumented vehicles with vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication systems are being tested, nearly 1.4 million records of BSMs, from 184 trips undertaken by 71 instrumented vehicles are analyzed in this study. Then even more detailed analysis of 43 randomly chosen trips (N = 714,340 BSM records) that were undertaken on various roadway types is conducted. The results indicate that acceleration and deceleration are two distinct regimes, and as compared to acceleration, drivers decelerate at higher rates, and braking is significantly more volatile than acceleration. Different correlations of the two regimes with instantaneous driving contexts are explored. With a more generic three-regime model specification, the results reveal high-rate acceleration, high-rate deceleration, and cruise/constant as the three distinct regimes that characterize a typical driving cycle. Moreover, given in a high-rate regime, drivers’ on-average tend to decelerate at a higher rate than their rate of acceleration. Importantly, compared to cruise/constant regime, drivers’ instantaneous driving decisions are more volatile both in “high-rate” acceleration as well as “high-rate” deceleration regime. The study contributes to analyzing volatility in short-term driving decisions, and how changes in driving regimes can be mapped to a combination of local traffic states surrounding the vehicle. 相似文献
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This study determines the optimal electric driving range of plug-in hybrid electric vehicles (PHEVs) that minimizes the daily cost borne by the society when using this technology. An optimization framework is developed and applied to datasets representing the US market. Results indicate that the optimal range is 16 miles with an average social cost of $3.19 per day when exclusively charging at home, compared to $3.27 per day of driving a conventional vehicle. The optimal range is found to be sensitive to the cost of battery packs and the price of gasoline. When workplace charging is available, the optimal electric driving range surprisingly increases from 16 to 22 miles, as larger batteries would allow drivers to better take advantage of the charging opportunities to achieve longer electrified travel distances, yielding social cost savings. If workplace charging is available, the optimal density is to deploy a workplace charger for every 3.66 vehicles. Moreover, the diversification of the battery size, i.e., introducing a pair and triple of electric driving ranges to the market, could further decrease the average societal cost per PHEV by 7.45% and 11.5% respectively. 相似文献
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为了总结面向智能车辆的现役道路设施行驶适应性,即现役道路基础设施承载智能车辆行驶的适宜程度,阐述自主智能驾驶定义与驾驶自动化等级分类,在此基础上剖析不同等级间的人机功能差异,并分别从感知层、感知-决策层、决策-控制层探讨与道路设计要素相关联的人机功能差异,通过归纳总结智能车辆与道路几何要素、路面性能及其他道路要素(如道路标线)的相互作用机制研究,从道路工程角度及其他道路要素方面回顾该领域的研究现状,指出存在的问题和未来发展方向。研究结果表明:相比传统车辆,配置高等级自动驾驶系统的智能车辆对现役道路设施行驶适应性最高,主动安全系统次之,而驾驶辅助及有条件自动驾驶系统适应性不足。而目前研究主要问题包括:难以归纳、标定不同驾驶自动化等级间的人机功能差异及其对于道路设计参数的需求设计值;测试道路场景条件过于理想,考虑的驾驶自动化等级单一,试验规模和样本有限;道路几何、路面性能以及道路标志、标线等道路要素与智能车辆间的相互作用机制研究不足,缺乏与不同道路场景相匹配的智能车辆驾驶特征数据的获取手段。因此建议:重视并推动与道路设计要素相关联的关键人机功能差异指标信息共享;联合高保真且可交互的道路场景、高精度感知传感器物理模型、车辆动力学模型及微观交通流模型,利用测试场景自动化生成、极限工况场景搜寻与泛化等技术开展智能驾驶虚拟测试,突破现有研究的深度和广度;探索反映不同等级智能车辆的道路行驶适应性特征指标与评价标准,精准、有效地评估预测复杂道路场景及不利道路条件下的行驶适应性。 相似文献
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分析了电阻点焊原理,对相同的点焊搭接方式,分别采用3种工况进行焊接试验。3种工况分别采用相同焊接参数对搭接接触面涂导电密封胶和不涂导电密封胶的试件进行焊接;优化焊接参数后,对搭接接触面涂导电密封胶的试验件进行焊接。对3种工况下完成的焊接试件进行拉伸力检测试验、金相熔核尺寸检测和缺陷检测试验,对熔核直径、拉伸力、熔核内部缺陷的试验结果进行分析。试验结果表明:导电密封胶对电阻点焊的焊接性能存在影响;通过适当优化焊接参数,可以提高涂导电密封胶试件的焊接性能,焊接质量能够满足产品焊接要求。 相似文献
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This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study’s results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models. 相似文献