An improved cellular automaton with axis information for microscopic traffic simulation

Cellular Automaton (CA), an efficient dynamic modeling method that is widely used in traffic engineering, is newly introduced for traffic load modeling. This modeling method significantly addresses the modest traffic loads for long-span bridges. It does, however, require improvement to calculate precise load effects. This paper proposed an improved cellular automaton with axis information, defined as the Multi-axle Single-cell Cellular Automaton (MSCA), for the precise micro-simulation of random traffic loads on bridges. Four main ingredients of lattice, cells’ states, neighborhoods and transition rules are redefined in MSCA to generate microscopic vehicle sequences with detailed vehicle axle positions, user-defined cell sizes and time steps. The simulation methodology of MSCA is then proposed. Finally, MSCA is carefully calibrated and validated using site-specific WIM data. The results indicate: (1) the relative errors (REs) for the traffic parameters, such as volumes, speeds, weights, and headways, from MSCA are basically no more than ±10% of those of WIM data; (2) the load effects of three typical influence lines (ILs) with varied lengths of 50, 200 and 1000 m are also confidently comparable, both of which validate the rationality and precision of MSCA. Furthermore, the accurate vehicle parameters and gaps generated from MSCA can be applied not only for precise traffic loading on infrastructures but also for the accurate estimation of vehicle dynamics and safety. Hence, wide application of MSCA can potentially be expected.     

Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data

Reliable travel behavior data is a prerequisite for transportation planning process. In large tourism dependent cities, tourists are the most dynamic population group whose size and travel choices remain unknown to planners. Traditional travel surveys generally observe resident travel behavior and rarely target tourists. Ubiquitous uses of social media platforms in smartphones have created a tremendous opportunity to gather digital traces of tourists at a large scale. In this paper, we present a framework on how to use location-based data from social media to gather and analyze travel behavior of tourists. We have collected data of about 67,000 users from Twitter using its search interface for Florida. We first propose several filtering steps to create a reliable sample from the collected Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The accuracy of the proposed classifier has been compared against the state-of-the-art classification methods. Finally, different clustering methods have been used to find the spatial patterns of destination choices of tourists. Promising results have been found from the output clusters as they reveal most popular tourist spots as well as some of the emerging tourist attractions in Florida. Performance of the proposed clustering techniques has been assessed using internal clustering validation indices. We have analyzed temporal patterns of tourist and resident activities to validate the classification of the users in two separate groups of tourists and residents. Proposed filtering, identification, and clustering techniques will be significantly useful for building individual-level tourist travel demand models from social media data.     

铁路桥梁浅基冲刷防护方案探讨

桥梁基础长期处于水环境中,在水流冲刷作用下,河床下切导致基础埋置深度减小,容易使基础产生病害进而影响结构强度和稳定性,同时降低地基基础承载力。通过分析河床冲刷对基础承载力的影响,结合铁路桥梁基础加固防护工程实践,探讨基础加固防护方案的合理性与可行性,对有效预防和整治因基础冲刷而产生的桥梁病害具有重要意义。     

Modeling time-of-day car use behavior: A Bayesian network approach

In this research, a Bayesian network (BN) approach is proposed to model the car use behavior of drivers by time of day and to analyze its relationship with driver and car characteristics. The proposed BN model can be categorized as a tree-augmented naive (TAN) Bayesian network. A latent class variable is included in this model to describe the unobserved heterogeneity of drivers. Both the structure and the parameters are learned from the dataset, which is extracted from GPS data collected in Toyota City, Japan. Based on inferences and evidence sensitivity analysis using the estimated TAN model, the effects of each single observed characteristic on car use measures are tested and found to be significant. The features of each category of the latent class are also analyzed. By testing the effect of each car use measure on every other measure, it is found that the correlations between car use measures are significant and should be considered in modeling car use behavior.     

马鞍山长江公路大桥钢塔线形控制技术

马鞍山长江公路大桥左汊主桥为（360＋2×1080＋360） m的三塔两跨悬索桥，中塔采用钢－混叠合、塔梁固结门式结构，下塔柱为预应力钢筋混凝土结构，上塔柱为钢结构，钢塔共分21个节段，首节采用浮吊安装，标准节段长6 m ，最大起吊重达235 t ，采用塔吊进行安装。为确保钢塔线形满足要求，对影响钢塔安装精度因素进行分析，形成以控制钢塔制造质量为核心、钢塔首节段安装精度为基础的线形控制流程，对钢塔节段进行工厂制造控制和现场安装控制。工厂制造控制包括零部件加工、块体制作、节段组拼、端面机加工、预拼装；现场安装控制包括首节段安装、标准节段安装、横梁与钢塔的连接。实践表明，该桥采用以控制钢塔制造精度为核心的钢塔线形控制技术进行钢塔架设施工，施工过程中钢塔制造精度和安装精度满足要求，实现了钢塔线形控制的目的。     

桥面铺装复合梁的有限元分析

采用三维有限元法,分析了复合梁试件模型和实体桥面铺装模型中沥青混凝土铺装层表面的受力特性及其变化趋势。计算结果表明,复合梁模型和实体桥面铺装模型中的沥青铺装层表面的受力状态相同,变化趋势一致,采用试验可控性较好的复合梁试验能够模拟不同结构组合的桥面铺装层受力特性,为大跨径桥梁桥面铺装层结构设计、材料选择提供依据。     

嘉绍大桥4.1 m超大直径钢护筒施工关键技术

以杭州湾水域嘉绍大桥桩基准4.1 m超大直径钢护筒施工为例,介绍超大直径钢护筒的制作、运输、沉放施工工艺以及振动锤选型、导向框设计、钢护筒加强设计等,探讨强涌潮水域复杂水文条件下桥梁桩基超大直径钢护筒施工技术及控制要点,可供类似工程参考借鉴。     

京津客运专线预制箱梁架设施工技术

结合国内首条客运专线——京津城际轨道交通工程中32m简支箱梁的架设要求及运架作业情况,详细介绍了架设900t/32m箱梁的工艺流程及运输、吊装和架设的关键施工技术,对同类工程施工有借鉴意义。     

多分类转导支持向量机

为了完成分类学习,传统的支持向量机基于带标记信息的经验数据归纳出一个通用的决策函数。而转导支持向量机则不同,它考虑包含测试集在内的所有数据信息并致力于最小化测试样本的分类错误数。在已有的2类分类方法的基础上构造了直接求解多类分类问题的的转导支持向量机。