The role of alternative fuel vehicles: Using behavioral and sensor data to model hierarchies in travel

Greater adoption and use of alternative fuel vehicles (AFVs) can be environmentally beneficial and reduce dependence on gasoline. The use of AFVs vis-à-vis conventional gasoline vehicles is not well understood, especially when it comes to travel choices and short-term driving decisions. Using data that contains a sufficiently large number of early AFV adopters (who have overcome obstacles to adoption), this study explores differences in use of AFVs and conventional gasoline vehicles (and hybrid vehicles). The study analyzes large-scale behavioral data integrated with sensor data from global positioning system devices, representing advances in large-scale data analytics. Specifically, it makes sense of data containing 54,043,889 s of speed observations, and 65,652 trips made by 2908 drivers in 5 regions of California. The study answers important research questions about AFV use patterns (e.g., trip frequency and daily vehicle miles traveled) and driving practices. Driving volatility, as one measure of driving practice, is used as a key metric in this study to capture acceleration, and vehicular jerk decisions that exceed certain thresholds during a trip. The results show that AFVs cannot be viewed as monolithic; there are important differences within AFV use, i.e., between plug-in hybrids, battery electric, or compressed natural gas vehicles. Multi-level models are particularly appropriate for analysis, given that the data are nested, i.e., multiple trips are made by different drivers who reside in various regions. Using such models, the study also found that driving volatility varies significantly between trips, driver groups, and regions in California. Some alternative fuel vehicles are associated with calmer driving compared with conventional vehicles. The implications of the results for safety, informed consumer choices and large-scale data analytics are discussed.     

A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data

There are many systems to evaluate driving style based on smartphone sensors without enough awareness from the context. To cover this gap, we propose a new system namely CADSE system to consider the effects of traffic levels and car types on driving evaluation. CADSE system includes three subsystems to calibrate smartphone, to classify the maneuvers, and to evaluate driving styles. For each maneuver, the smartphone sensors data are gathered in three successive time intervals referred as pre-maneuver, in-maneuver, and post-maneuver times. Then, we extract some important mathematical and experimental features from these data. Afterwards, we propose an ensemble learning method on these features to classify the maneuvers. This ensemble method includes decision tree, support vector machine, multi-layer perceptron, and k-nearest neighbors. Finally, we develop a rule-based fuzzy inference system to integrate the outputs of these algorithms and to recognize dangerous and safe maneuvers. CADSE saves this result in driver’s profile to consider more for dangerous driving recognition. The experimental results show that accuracy, precision, recall, and F-measure of CADSE system are greater than 94%, 92%, 92%, and 93%, respectively that prove the system efficiency.     

Crash prediction with behavioral and physiological features for advanced vehicle collision avoidance system

Real-time crash prediction is the key component of the Vehicle Collision Avoidance System (VCAS) and other driver assistance systems. The further improvements of predictability requires the systemic estimation of crash risks in the driver-vehicle-environment loop. Therefore, this study designed and validated a prediction method based on the supervised learning model with added behavioral and physiological features. The data samples were extracted from 130 drivers’ simulator driving, and included various features generated from synchronized recording of vehicle dynamics, distance metrics, driving behaviors, fixations and physiological measures. In order to identify the optimal configuration of proposed method, the Discriminant Analysis (DA) with different features and models (i.e. linear or quadratic) was tested to classify the crash samples and non-crash samples. The results demonstrated the significant improvements of accuracy and specificity with added visual and physiological features. The different models also showed significant effects on the characteristics of sensitivity and specificity. These results supported the effectiveness of crash prediction by quantifying drivers’ risky states as inputs. More importantly, such an approach also provides opportunities to integrate the driver state monitoring into other vehicle-mounted systems at the software level.     

新能源汽车续驶里程焦虑及维修技术研究

续驶里程焦虑是新能源汽车售后市场抱怨最多的问题。文章从售后角度分析了导致续驶里程焦虑的原因及维修排查技术,提出了一些建议性措施。旨在为售后相关人员提供一定的理论指导,从而推动售后市场更好的发展。     

有限水深中破损船舶的运动与波浪载荷研究

由于有限水深中船舶搁浅和触礁等严重破损事故频发,为了减少事故的发生,对有限水深中船舶破损后的运动及波浪载荷的研究显得十分必要。文章基于三维势流理论,引入有限水深自由面Green函数,在频域内使用奇点分布法对一艘首部破损进水的散货船在有限水深中的运动与波浪载荷展开了计算,并根据劳氏船级社规范做了短期预报。短期预报结果表明,该散货船破损进水后,船体所受垂向和水平波浪弯矩均比破损前有明显增加,且在较浅水深中变化更为显著。     

波浪中母船干扰下潜水员梯的运动及水动力特性分析

潜水员梯是通过销轴连接在母船舷侧为潜水员执行潜水作业时上下船的平台,为保证在波浪中使用时的安全性,对其波浪作用力、运动响应及销轴处的动作用载荷设计有较高要求。为研究母船干扰下潜水员梯的动态响应特性,采用基于深水格林函数为积分内核的边界元方法,先计算母船及潜水梯这一整体的运动响应及其平均湿表面的辐射和绕射压力分布,依据刚体运动合成原理求解得到潜水梯重心处的运动。再以潜水员梯为研究对象,采用压力积分方法获得潜水员梯的波浪力作用,并将其运动、波浪力和销轴处的作用力联立构成动力学平衡方程,逆向求解出销轴处的动作用力。基于上述思路,系统研究了波长、航速及潜水员梯与母船相对位置变化时潜水员梯的运动、波浪作用力及销轴处作用力的变化规律,为潜水员梯的布置、销轴的选型及潜水作业时母船的操纵可提供指导意义。     

基于解释结构模型的绿色供应链驱动研究

随着环境压力的增大,绿色供应链日益成为一个重要的理念。本文考虑了目前业内专家认定的绿色供应链驱动因素,通过分析驱动因素的自相关性和相互之间的可达性,建立了绿色供应链的解释结构模型,并通过MICMAC象限图具体分析各因素的驱动层次,以期为绿色供应链的实施提供科学依据。     

正确使用ABS确保行车安全

通过分析ABS的工作原理,提出了使用ABS的注意事项,并将发动机制动与ABS制动合理结合,提高整车制动效能;分析了车辆弯道行驶状况,提出以安全、稳定车速通过弯道,不可过于依赖ABS,尽量避免制动,防止车辆侧滑.     

汽车跑偏测试系统中的相机标定技术研究

欲保证基于摄影测量的汽车行驶跑偏在线自动测试系统的测试精度,需对测试用CCD相机进行精确标定.CCD相机标定常用的算法有空间后方交会法和直接线性变换(DLT)法等2种.通过对2种相机标定方法基本原理的分析,结合汽车行驶跑偏在线自动测试系统的特点,从相机标定精度和稳定性出发,探讨了相机标定算法的选用原则及控制点类型对标定...     

轨道式集装箱龙门起重机(RMG)在青岛前湾招商局国际集装箱码头工程中的应用

结合青岛前湾招商局国际集装箱码头工程设计施工和运行情况,分析总结了轨道式集装箱龙门起重机(RMG)堆场工艺布置特点、设备选型及配备数量的确定、RMG重箱堆场的设计与施工中的亮点和RMG装卸工艺模式在我国集装箱码头中的应用前景.实践表明,RMG作为集装箱码头的装卸模式,实现了低碳环保、节能高效.