施工场景下基于YOLOv3的安全帽佩戴状态检测

针对现有安全帽检测研究中采用的两阶段检测法存在检测效率偏低,累积误差对精度影响较大的问题,提出一种对安全帽的单阶段检测法。将安全帽和工人头部视为一个整体,将检测目标分为2类,即佩戴安全帽的头部和未佩戴安全帽的头部,同时对2类目标进行检测,避免了冗余的计算步骤及累积误差的影响。同时,针对施工场景安全帽佩戴状态检测特点,对YOLOv3的网络结构、损失函数及先验框尺寸进行改进,提出YOLOv3-C模型。研究结果表明:改进后的YOLOv3-C模型的检测性能大幅提升,在本文建立的样本集中模型的mAP达到93.84%,对安全帽检测平均精度达到97.01%,对工人头部检测平均精度达到90.67%,同时YOLOv3-C对本文的检测场景表现出良好的鲁棒性。     

某地铁列车无人自动折返卡顿故障分析及解决方案

针对某城市地铁车辆在试运行期间频繁发生的列车无人自动折返时的卡顿故障进行了分析研究。简要介绍了列车无人自动折返功能,并从列车网络系统、牵引系统和车辆电路3个角度深入分析故障原因,提出了解决方案。按此方案整改后故障得以完全消除。     

大城市城市轨道交通线网规划关键技术探讨

目前,城市轨道交通线网规划的技术已相对成熟,指导了我国超大、特大城市的城市轨道交通建设。随着城市化进程的不断加快,人口在100万至500万的大城市已成为城市轨道交通规划和建设的重要参与者。大城市在空间、人口、经济、交通出行特征等方面与超大、特大城市存在较大的差异,使得目前城市轨道交通线网规划的技术对大城市的适用性存在一定的局限。通过分析大城市与城市轨道交通的互动关系,提出大城市城市轨道交通线网规划的需求。基于现有的城市轨道交通线网编制技术流程,结合大城市的需求,从前提研究、方案研究、实施规划三个层面,对城市轨道交通线网规划编制中服务范围分析、线网规模确定、近期建设方案制定等技术内容提出优化建议,以适应大城市城市轨道交通线网规划的编制需求。     

Transport network criticality metrics: a comparative analysis and a guideline for selection

ABSTRACT

Transport network criticality analysis aims at ranking transport infrastructure elements based on their contribution to the performance of the overall infrastructure network. Despite the wide variety of transport network criticality metrics, little guidance is available on selecting metrics that are fit for the specific purpose of a study. To address this gap, this study reviews, evaluates and compares seventeen criticality metrics. First, we conceptually evaluate these metrics in terms of the functionality of the transport system that the metrics try to represent (either maintaining connectivity, reducing travel cost, or improving accessibility), the underlying ethical principles (either utilitarianism or egalitarianism), and the spatial aggregation considered by the metrics (either network-wide or localised). Next, we empirically compare the metrics by calculating them for eight transport networks. We define the empirical similarity between two metrics as the degree to which they yield similar rankings of infrastructure elements. Pairs of metrics that have high empirical similarity highlight the same set of transport infrastructure elements as critical. We find that empirical similarity is partly dependent on the network’s topology. We also observe that metrics that are conceptually similar do not necessarily have high empirical similarity. Based on the insights from the conceptual and empirical comparison, we propose a five-step guideline for transport authorities and analysts to identify the set of criticality metrics to use which best aligns with the nature of their policy questions.     

FXD3-J型动力集中动车组动力车网络控制系统

文章详细介绍了FXD3-J型动力集中动车组动力车网络控制系统的结构与组成,对网络设备、系统控制功能、显示界面、冗余特性等进行了阐述。运用考核试验表明,该网络控制系统性能稳定、可靠。     

基于全以太网的列车网络控制系统在美国地铁列车上的应用

从列车网络控制系统的拓扑结构、通信协议架构、冗余措施及功能描述等几个方面对基于全以太网的列车网络控制系统在美国地铁列车上的应用进行了详细分析与试验验证。在装车调试之前进行系统集成测试可尽早地发现列车网络控制系统或子系统在硬件和软件方面存在的各种问题,大大节省装车调试所需要的人力和时间。     

A new logit‐artificial neural network ensemble for mode choice modeling: a case study for border transport

Logit model is one of the statistical techniques commonly used for mode choice modeling, while artificial neural network (ANN) is a very popular type of artificial intelligence technique used for mode choice modeling. Ensemble learning has evolved to be very effective approach to enhance the performance for many applications through integration of different models. In spite of this advantage, the use of ANN‐based ensembles in mode choice modeling is under explored. The focus of this study is to investigate the use of aforementioned techniques for different number of transportation modes and predictor variables. This study proposes a logit‐ANN ensemble for mode choice modeling and investigates its efficiency in different situations. Travel between Khobar‐Dammam metropolitan area of Saudi Arabia and Kingdom of Bahrain is selected for mode choice modeling. The travel on this route can be performed mainly by air travel or private vehicle through King Fahd causeway. The results show that the proposed ensemble gives consistently better accuracies than single models for multinomial choice problems irrespective of number of input variables. Copyright © 2015 John Wiley & Sons, Ltd.     

A modified reinforcement learning algorithm for solving coordinated signalized networks

This study proposes Reinforcement Learning (RL) based algorithm for finding optimum signal timings in Coordinated Signalized Networks (CSN) for fixed set of link flows. For this purpose, MOdified REinforcement Learning algorithm with TRANSYT-7F (MORELTRANS) model is proposed by way of combining RL algorithm and TRANSYT-7F. The modified RL differs from other RL algorithms since it takes advantage of the best solution obtained from the previous learning episode by generating a sub-environment at each learning episode as the same size of original environment. On the other hand, TRANSYT-7F traffic model is used in order to determine network performance index, namely disutility index. Numerical application is conducted on medium sized coordinated signalized road network. Results indicated that the MORELTRANS produced slightly better results than the GA in signal timing optimization in terms of objective function value while it outperformed than the HC. In order to show the capability of the proposed model for heavy demand condition, two cases in which link flows are increased by 20% and 50% with respect to the base case are considered. It is found that the MORELTRANS is able to reach good solutions for signal timing optimization even if demand became increased.     

Prioritizing road extension projects with interdependent benefits under time constraint

Since transportation projects are costly and resources are limited, prioritizing or sequencing the projects is imperative. This study was inspired by a client who asked: “I have tens of approved road extension projects, but my financial resources are limited. I cannot construct all the projects simultaneously, so can you help me prioritize my projects?” To address this question, the benefits and costs of all the possible scenarios must be known. However, the impacts (or benefit) of road extension projects are highly interdependent, and in sizable cases cannot be specified thoroughly. We demonstrate that the problem is analogous to the Traveling Salesman Problem (TSP). Dynamic change in travel demand during construction is another aspect of the complexity of the problem. The literature is yet to provide efficient methods for large cases. To this end, we developed a heuristic methodology in which the variation of travel demand during the construction period is considered. We introduce a geometrical objective function for which a solution-finding policy based on “gradient maximization” is developed. To address the projects’ interdependency, a special neural network (NN) model was devised. We developed a search engine hybridized of Ant Colony and Genetic Algorithm to seek a solution to the TSP-like problem on the NN based on gradient maximization. The algorithm was calibrated and applied to real data from the city of Winnipeg, Canada, as well as two cases based on Sioux-Falls. The results were reliable and identification of the optimum solution was achievable within acceptable computational time.     

Cellpath: Fusion of cellular and traffic sensor data for route flow estimation via convex optimization

A new convex optimization framework is developed for the route flow estimation problem from the fusion of vehicle count and cellular network data. The issue of highly underdetermined link flow based methods in transportation networks is investigated, then solved using the proposed concept of cellpaths for cellular network data. With this data-driven approach, our proposed approach is versatile: it is compatible with other data sources, and it is model agnostic and thus compatible with user equilibrium, system-optimum, Stackelberg concepts, and other models. Using a dimensionality reduction scheme, we design a projected gradient algorithm suitable for the proposed route flow estimation problem. The algorithm solves a block isotonic regression problem in the projection step in linear time. The accuracy, computational efficiency, and versatility of the proposed approach are validated on the I-210 corridor near Los Angeles, where we achieve 90% route flow accuracy with 1033 traffic sensors and 1000 cellular towers covering a large network of highways and arterials with more than 20,000 links. In contrast to long-term land use planning applications, we demonstrate the first system to our knowledge that can produce route-level flow estimates suitable for short time horizon prediction and control applications in traffic management. Our system is open source and available for validation and extension.