迁居对家庭日常活动—出行行为交互机理的影响

居住地是家庭成员日常活动的主要起讫点,迁居会导致家庭日常生活空间重组,进而成为影响成员活动安排和出行模式的关键因素.本文以迁居家庭为研究对象,分别从个体和家庭两个层面分析迁居前后日常活动—出行行为的调整变化,以揭示家庭成员间的交互模式及其内在机理.研究表明:迁居对成员活动—出行行为的影响首先体现在通勤模式调整上,其次是在家务分担上.具体来说,当两个成员均受到职住错位影响时,双方在家务活动安排上交互强烈,家庭内部会通过替代或互助等互动模式来平衡通勤活动所受到的时空制约;当只有一方受到职住错位制约时,双方交互程度进一步降低,家务活动安排上表现出显著性别差异,男方较容易受通勤活动的影响而放弃承担家务活动;当双方均处于职住邻近状态时,双方交互程度最弱,家务活动常采用陪伴或联合等协作方式来完成.     

公交网络最优路径求解算法

求解公交网络最优路径是进行公共交通系统规划的一项关键技术.通过对多种现有算法的分析,利用公交换乘矩阵性质,本文提出了一种求解公交网络最优路径的标准算法.新算法考虑了公交换乘次数、换乘点选择以及出行总成本对求解最优路径的综合影响.通过建立换乘步行时间矩阵,并将过去求解最小换乘次数的换乘矩阵乘法运算变为相应的换乘步行时间矩阵和公交出行时间矩阵的加法运算,得到新算法.新算法可顺利实现在单一OD对、单起点多终点以及任意节点间求解最优路径的转化.文中给出了新算法的详细求解步骤,而且通过一个算例对新算法的有效性进行了验证.     

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.     

The dynamic shortest path problem with time-dependent stochastic disruptions

The dynamic shortest path problem with time-dependent stochastic disruptions consists of finding a route with a minimum expected travel time from an origin to a destination using both historical and real-time information. The problem is formulated as a discrete time finite horizon Markov decision process and it is solved by a hybrid Approximate Dynamic Programming (ADP) algorithm with a clustering approach using a deterministic lookahead policy and value function approximation. The algorithm is tested on a number of network configurations which represent different network sizes and disruption levels. Computational results reveal that the proposed hybrid ADP algorithm provides high quality solutions with a reduced computational effort.     

城市轨道交通新建线路对沿线住宅价格增值的时空效应

定量分析城市轨道交通对沿线土地价值的影响，是通过土地价值捕获将外部效益内部化， 解决中国新型城镇化过程中城市轨道交通财务可持续的关键问题。本文在开源数据环境下，获 取2011—2016年北京市二手房交易数据，采用特征价格模型(HPM)分析北京城市轨道交通新建 线路对沿线住宅价格增值的时空效应。研究表明：与全局常参数的多元线性回归模型(MLR)、空 间滞后模型(SLM)、空间误差模型(SEM)和空间杜宾模型(SDM)相比，局部变参数的地理加权回归 模型(GWR)拟合效果更优，可以更好地消除残差的空间效应，刻画轨道交通与土地价值关系的空 间异质性。城市轨道交通带来的可达性提升对沿线80%以上住宅小区的房价具有显著的正效 应，住宅价格增值比率随地铁站距离递远递减，且具有显著的空间异质性。轨道交通对住宅价格 的影响在空间上具有网络化效应，新建线路不仅会改变周边房价，对城轨网络其他位置的住宅也 具有影响。城市轨道交通对住宅价格的影响范围为1 km，在该范围内，住宅价格受到的增值效应 在 2011—2016 年基本稳定，约为 3%；受新线直接影响区域的住宅价格会产生相对更大的提升 (0.02%~0.22%)。