A review on motorcycle and rider modelling for steering control

The paper is a review of the state of knowledge and understanding of steering control in motorcycles and of the existing rider models. Motorcycles are well known to have specific instability characteristics, which can detrimentally affect the rider's control, and as such a suitable review of these characteristics is covered in the first instance. Next, early models which mostly treat riding as a regulatory task are considered. A rider applies control based on sensory information available to him/her, predominantly from visual perception of a target path. The review therefore extends to cover also the knowledge and research findings into aspects of road preview control. Here, some more emphasis is placed on recent applications of optimal control and model predictive control to the riding task and the motorcycle–rider interaction. The review concludes with some open questions which naturally present a scope for further study.     

基于层次策略的路径规划算法对比研究

为比较路径规划中基于层次策略的分层算法的计算效率和规划结果的合理性,选取基于预计算的分层算法和基于道路等级的分层分区算法这2类典型的分层路径规划算法,通过对2类算法基本原理的分析,并引入新分区算法和＂虚拟边＂等方法改进现有基于道路等级的分层分区算法以适应实际路网下的路径规划。选取广东省路网数据进行大规模测试,通过寻找＂最短路＂和＂最快路＂进行算法效率和路径规划结果的比较分析。测试结果表明改进的基于道路等级的分层分区算法计算效率更高,规划结果更符合出行偏好。     

功效系数法在水泥稳定材料配合比设计中的应用

水泥稳定材料的配合比设计方法主要考虑技术指标,对经济指标关注很少。对满足强度要求的多个配合比设计方案,将价格也作为考察指标之一,运用功效系数法加以评价和优化,获得性价比最佳的配合比设计方案,既能保证质量,又可节约工程成本,并总结出一种多指标权衡的配合比设计评价和优化方法。     

舰载机机库调运作业路径

航母舰载机机库调运作业空间狭窄、调运设施特殊等特点决定了航母舰载机调运作业需要合理的路线规划,否则会造成作业环境拥塞、调运作业无序混乱.本文以俄航母舰载机机库内调运作业为研究原型,创建了舰载机调运作业交通网络,通过对交通网络分析,给出了2种舰载机机库调运作业路径选择模型,可为不同布列下舰载机调运作业路线的规划提供决策参考.     

船体纵骨典型节点疲劳裂纹扩展寿命评估

基于有限元软件ABAQUS,结合虚拟裂纹闭合法、裂纹扩展判据及子结构技术,应用脚本语言Python开发出模拟疲劳裂纹扩展的程序(FCG-System)。对含初始裂纹的油船纵骨典型节点在侧面压力作用下进行疲劳裂纹扩展数值模拟,并探讨了软趾、背肘板及防倾肘板对疲劳裂纹扩展路径和寿命的影响。结果表明,增设软趾、背肘板或防倾肘板都会使裂纹扩展路径曲率增大,且软趾、防倾肘板可使裂纹扩展寿命增大,背肘板可使裂纹扩展寿命减小。     

Explaining “peak car” with economic variables

Many western countries have seen a plateau and subsequent decrease of car travel during the 21st century. What has generated particular interest and debate is the statement that the development cannot be explained by changes in traditional explanatory factors such as GDP and fuel prices. Instead, it has been argued, the observed trends are indications of substantial changes in lifestyles, preferences and attitudes to car travel; what we are experiencing is not just a temporary plateau, but a true “peak car”. However, this study shows that the traditional variables GDP and fuel price are in fact sufficient to explain the observed trends in car traffic in all the countries included in our study: the United States, France, the United Kingdom, Sweden and (to a large extent) Australia and Germany. We argue that the importance of the fuel price increases in the early 2000s has been underappreciated in the studies that shaped the later debate. Results also indicate that GDP elasticities tend to decrease with rising GDP, and that fuel price elasticities tend to increase at high price levels and during periods of rapid price increases.     

Constraint reformulation and a Lagrangian relaxation-based solution algorithm for a least expected time path problem

Using a sample-based representation scheme to capture spatial and temporal travel time correlations, this article constructs an integer programming model for finding the a priori least expected time paths. We explicitly consider the non-anticipativity constraint associated with the a priori path in a time-dependent and stochastic network, and propose a number of reformulations to establish linear inequalities that can be easily dualized by a Lagrangian relaxation solution approach. The relaxed model is further decomposed into two sub-problems, which can be solved directly by using a modified label-correcting algorithm and a simple single-value linear programming method. Several solution algorithms, including a sub-gradient method, a branch and bound method, and heuristics with additional constraints on Lagrangian multipliers, are proposed to improve solution quality and find approximate optimal solutions. The numerical experiments investigate the quality and computational efficiency of the proposed solution approach.     

Collective public-transport tickets and anticipated majority choice: A model of student tickets

In Germany, many universities have student tickets that are bargained for between student representatives and public transport companies, approved by referendum, and mandatory for all students. They allow the use of public transport at no additional cost. I analyze such a scenario in a theoretical model as an example of a flat-rate ticket for public transport which is implemented by majority decision. The mandatory character of the ticket reduces transaction costs like marketing and ticket inspection, reducing the ticket price and thus the students’ commuting expenses. However, there is a countervailing effect. Students face and rationally expect zero marginal monetary commuting costs, so that new students choose a place of residence which is relatively far from the university. This in turn raises the equilibrium ticket price. It may even be the case that students would be better off if such a ticket had never existed. Nonetheless, they always vote for it in referenda, because accepting the high price is optimal given their place of residence. After laying out the model, I analyze an optimal policy, which consists, for example, of subsidizing student dorms at an efficient distance to the city center.     

Meeting points in ridesharing: A privacy-preserving approach

Nowadays, problems of congestion in urban areas due to the massive usage of cars, last-minute travel needs and progress in information and communication technologies have fostered the rise of new transportation modes such as ridesharing. In a ridesharing service, a car owner shares empty seats of his car with other travelers. Recent ridesharing approaches help to identify interesting meeting points to improve the efficiency of the ridesharing service (i.e., the best pick-up and drop-off points so that the travel cost is competitive for both driver and rider). In particular, ridesharing services, such as Blablacar or Carma, have become a good mobility alternative for users in their daily life. However, this success has come at the cost of user privacy. Indeed in current’s ridesharing services, users are not in control of their own data and have to trust the ridesharing operators with the management of their data.In this paper, we aim at developing a privacy-preserving service to compute meeting points in ridesharing, such that each user remains in control of his location data. More precisely, we propose a decentralized architecture that provides strong security and privacy guarantees without sacrificing the usability of ridesharing services. In particular, our approach protects the privacy of location data of users. Following the privacy-by-design principle, we have integrated existing privacy enhancing technologies and multimodal shortest path algorithms to privately compute mutually interesting meeting points for both drivers and riders in ridesharing. In addition, we have built a prototype implementation of the proposed approach. The experiments, conducted on a real transportation network, have demonstrated that it is possible to reach a trade-off in which both the privacy and utility levels are satisfactory.