Relationship between number of passing events and operating parameters in mixed bicycle traffic

This paper investigates the passing events between electric bicycles and conventional bicycles and explores the relationships between passing events and traffic parameters in bicycle facilities.Three exclusive bicycle paths in Nanjing, China,were observed with cameras.Then,the field data including vehicle number,velocity characteristics and passing event features were analyzed in detail.Data analysis and fitting reveal that the speed difference has little impact on the passing event number,as does the bicycle ratio.The Gaussian function can better describe the relationship between the passing event number and bicycle volume （density）.The valid use level of bicycle path width influences the inflexion of the passing events-density fitting curve.The conclusions can be applied for estimating the passing events in mixed bicycle flows and for choosing a suitable width of separate bicycle path.

A Parameter-Detection Algorithm for Moving Ships

In traffic-monitoring systems, numerous vision-based approaches have been used to detect vehicle parameters. However, few of these approaches have been used in waterway transport because of the complexity created by factors such as rippling water and lack of calibration object. In this paper, we present an approach to detecting the parameters of a moving ship in an inland river. This approach involves interactive calibration without a calibration reference. We detect a moving ship using an optimized visual foreground detection algorithm that eliminates false detection in dynamic water scenarios, and we detect ship length, width, speed, and flow. We trialed our parameter-detection technique in the Beijing-Hangzhou Grand Canal and found that detection accuracy was greater than 90% for all parameters.

新胡同空间的参数化设计初探——以北京前门地区为例

随着社会的进步,北京高速城市化与旧城保护的要求两者之间的矛盾越来越突出,旧城街区中一些典型传统空间在城市快速发展中已经逐渐消失。该文在介绍与运用参数化设计的基础上,从前门地区整体的道路网格和建筑庭院空间两个方面着手,在延续胡同纹理和保护传统四合院的前提下,通过对旧城街区内一些典型传统空间特征的模拟与计算,探索既能延续传统的场所精神又能满足未来多样化需求的空间,从而兼顾旧城保护与现代城市功能的需要。

Travel time prediction model of freeway based on gradient boosting decision tree

To investigate the travel time prediction method of the freeway, a model based on the gradient boosting decision tree (GBDT) is proposed. Eleven variables (namely, travel time in current period T i , traffic flow in current period Q i , speed in current period V i , density in current period K i , the number of vehicles in current period N i , occupancy in current period R i , traffic state parameter in current period X i , travel time in previous time period T i -1 , etc.) are selected to predict the travel time for 10 min ahead in the proposed model. Data obtained from VISSIM simulation is used to train and test the model. The results demonstrate that the prediction error of the GBDT model is smaller than those of the back propagation (BP) neural network model and the support vector machine (SVM) model. Travel time in current period T i is the most important variable among all variables in the GBDT model. The GBDT model can produce more accurate prediction results and mine the hidden nonlinear relationships deeply between variables and the predicted travel time.

依据轨迹数据的船舶交通密度计算方法

研究基于轨迹数据的船舶交通密度计算方法,利用精准的船舶交通密度计算结果提升海上交通规划水平。利用AIS设备采集船舶航行轨迹数据,利用均匀参数化方法对所采集的航行轨迹数据重采样处理。将通过重采样处理获取的航行轨迹数据,划分为静止轨迹数据点以及移动轨迹数据点,依据数据点间的欧式距离,以及船舶航行方向、航行速度的相似性,选取基于密度的DBSCAN聚类算法完成轨迹数据聚类。依据船舶航行轨迹数据聚类结果,选取多维密度方法,通过更新船舶经过总数、船舶经过总时间等参数,计算船舶交通密度。实验结果表明,该方法可以依据船舶航行轨迹数据,精准计算船舶交通密度,为海上交通规划提供有效支撑。

基于CATIA的车辆相对于路边石通过性的参数化分析

文章首先对公司周边不同路段的路边石高度进行了统计,然后根据统计结果利用CATIA软件制作出可快速推算不同车型通过各类路边石的最小离地间隙的简易参数化几何模型,此简易几何模型可为新开发车型的通过性参数提供参考,在提高车辆通过性以及相对于其他车型竞争力的同时,还能为用户进出位于路边石上的临时停车位提供便利。

基于Web的交通事故远程仿真系统

结合开发交通事故防范的数字化公共平台,根据基于Web的交通事故模拟分析系统的技术特征,设计了基于Web的交通事故模拟分析系统的框架.基于商业软件,提出适用于模拟法的交通事故场景自动生成技术,并初步建立了包括车辆、人体在内的全三维参数化仿真模型数据库.通过实现远程交通事故模拟与分析,为交通事故的快速分析和处理提供有力的支持.

基于语义分割的多类别地面交通标志识别

无人驾驶汽车的外界环境感知系统中对地面交通标志的识别模型少,地面交通标志误分割和分割不完整,对此提出了轻量级的多类别地面交通标志分割模型。主干网络由多分支结构的重参数Vgg模块(RepVGG Block)组成,增加模型的特征表达能力,避免模型训练时的梯度消失。设计了具有倒残差结构的条形特征提取单元(IDAB Module)用于条形特征的提取,并将模块进行了串联组合,对每个单元中的卷积设置不同的空洞率,以此扩大模块中特征图的感受野,充分利用上下文信息;同时利用非对称卷积更好地提取交通标志的长条形状特征。采用地面交通标志数据集训练和测试地面交通标志分割模型,利用具有多分支结构的主干网络进行训练,测试时通过结构重参数化方法,将多分支结构融合为单路结构,以加快模型的推理速度。实验结果表明,以2 300×600大小的图像为输入,模型的测试精度mIoU最高可达到79.7%,并且有效改善了地面交通标志分割不完整和误分割的问题。