成都市道路建设改革与研究

~~成都市道路建设改革与研究@钟强文$成都市市政工程设计研究院!四川成都610015~~~~     

异形变截面多室曲线箱梁桥的试验研究及三维有限元分析

在对某异形变截面多室曲线箱梁桥试验研究的基础上,构造可以综合考虑拉压、弯扭、畸变、剪力滞及其耦合作用的三维虚拟层合单元法,并对该异形桥梁进行空间有限元分析。理论分析和试验研究表明,利用此法对这种复杂的异形桥梁结构进行空间分析,具有模型单元少、计算效率和精度高的特点,完全可以满足实际工程的需要,是一种简单方便的实用计算方法。最后,综合考虑在一期恒载和纵向预应力筋作用下其应力的空间分布情况。研究成果可供工程设计和施工参考。     

某高速公路13m跨简支桥检测试验研究

采用原位等效静荷载试验方法,检测和验证13m跨简支钢筋混凝土单块板,铰缝处理前后结构的受力性能,铰缝处理前后单板挠度分别为1/307、1/786,单板经铰缝处理满足了正常使用要求。     

北京城市交通发展战略研究

本文综合分析了北京城市交通现状，运用生产力布局的理论对北京城市交通拥堵的原因进行分析，探索了国内外大城市交通发展的最新趋势，根据发展“首都经济”的要求．提出解决北京城市交通拥堵问题，必须采取标本兼治，并提出北京城市交通发展战略研究的有关建议。.     

海湾大桥北引道沥青混凝土罩面设计

结合广东省汕汾高速公路汕头海湾大桥北引道路面加铺工程,分析软基路段过渡性路面的病害,提出沥青混凝土路面罩面结构设计和施工中须注意的问题.     

风积沙的毛细性及其盐胀的室内试验研究

通过毛细水上升高度试验，得出了风积沙在最佳含水量、不同压实度条件下的毛细水上升高度数据；通过室内盐胀试验得到了不同含盐量风积沙的盐胀量，并分析了含盐量及温度与盐胀量的关系。     

Does crowding affect the path choice of metro passengers?

This paper investigates crowding effect on the path choice of metro passengers. We show people reroute not only to avoid the delay from crowding but also to evade crowding itself. More specifically, a logit model fits best when it uses the transit delay from crowding as well as the passenger load of a connection in addition to the conventional explanatory variables. Also, we demonstrate that crowding decreases the overall welfare of metro passengers. The model is tested on the real path choice data acquired by the recent algorithm by Hong et al. (2015) known to detect the real path choice from Smart Card data in more than 90% of the cases.     

Local joint flexibility of tubular X-joints stiffened with external ring or external plates

In the present paper, the Local Joint Flexibility (LJF) of the ring-stiffened X-joints and plate-stiffened X-joints under compressive load is investigated. In the first phase, a finite element (FE) model was generated and verified with the results of available experimental tests and equations. In the next phase, a set number of 234 FE models were created to evaluate the role of the external ring size (β_r and τ_r), the external plate size (β_p and τ_p), and the connection geometry (γ, τ, and β) on the LJF factor (f_LJF). In these FE models, the weld connecting the chord and brace members was generated. The results indicated that the f_LJF of a plate stiffened joint can be down to 76% of the f_LJF of the corresponding un-stiffened joint. Also, the effect of the ring size on the f_LJF was more than the effect of the plate size on the f_LJF, because of the stiffener position. Despite the notable effect of the ring and the plate on the f_LJF, there is not any study or formula on tubular connections stiffened with ring or plate. Therefore, the FE results were used to propose two parametric formulas for determining the f_LJF in X-joints with external ring or external plate under brace compressive load. Moreover, the derived formulas were checked based on the UK DoE acceptance criteria.     

Human-like autonomous car-following model with deep reinforcement learning

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.