Field implementation feasibility study of cumulative travel‐time responsive (CTR) traffic signal control algorithm

The cumulative travel‐time responsive (CTR) algorithm determines optimal green split for the next time interval by identifying the maximum cumulative travel time (CTT) estimated under the connected vehicle environment. This paper enhanced the CTR algorithm and evaluated its performance to verify a feasibility of field implementation in a near future. Standard Kalman filter (SKF) and adaptive Kalman filter (AKF) were applied to estimate CTT for each phase in the CTR algorithm. In addition, traffic demand, market penetration rate (MPR), and data availability were considered to evaluate the CTR algorithm's performance. An intersection in the Northern Virginia connected vehicle test bed is selected for a case study and evaluated within vissim and hardware in the loop simulations. As expected, the CTR algorithm's performance depends on MPR because the information collected from connected vehicle is a key enabling factor of the CTR algorithm. However, this paper found that the MPR requirement of the CTR algorithm could be addressed (i) when the data are collected from both connected vehicle and the infrastructure sensors and (ii) when the AKF is adopted. The minimum required MPRs to outperform the actuated traffic signal control were empirically found for each prediction technique (i.e., 30% for the SKF and 20% for the AKF) and data availability. Even without the infrastructure sensors, the CTR algorithm could be implemented at an intersection with high traffic demand and 50–60% MPR. The findings of this study are expected to contribute to the field implementation of the CTR algorithm to improve the traffic network performance. Copyright © 2017 John Wiley & Sons, Ltd.     

Travel mode choices in small cities of China: A case study of Changting

The existing literature on urban transportation planning in China focuses primarily on large cities and neglects small cities. This paper aims to fill part of the knowledge gap by examining travel mode choice in Changting, a small city that has been experiencing fast spatial expansion and growing transportation problems. Using survey data collected from 1470 respondents on weekdays and weekends, the study investigates the relationship between mode choice and individuals’ socio-economic characteristics, trip characteristics, attitudes, and home and workplace built environments. While more than 35 percent of survey respondents are car owners, walk, bicycle, e-bike, and motorcycle still account for over 85 percent of trips made during peak hours. E-bike and motorcycle are the dominant means of travel on weekdays, but many people shift to walking and cycling on weekends, making non-motorized and semi-motorized travel especially important for non-commuting trips. Results of multinomial logistic regression show that: (1) job-housing balance might exert different effects on mode choice in different types of urban areas; (2) negative attitude towards e-bike and motorcycle is associated with more walking and cycling; and (3) land use diversity of workplace is related to commuting mode choice on weekdays, while land use diversities of both residential and activity places do not significantly affect mode choice on weekends. Our findings imply that planning and design for small cities needs to differentiate land use and transportation strategies in various types of areas, and to launch outreach programs to shift people’s mode choice from motorized travel to walking and cycling.     

市郊铁路投融资模式选择策略研究

未来大规模的市郊铁路建设将给地方政府带来融资难题。针对市郊铁路的投融资困境,结合项目特点,基于全生命周期理念建立了由项目总体融资模式、初始融资模式和再融资模式三个选择层面构成的市郊铁路投融资模式选择体系。分析了多种投融资模式在市郊铁路建设和运营中的应用。     

面向动静态混合环境的智能车运动规划方法

针对存在动态和静态障碍物环境中的智能车运动规划问题，本文提出一种基于三维搜索的方法。该方法首先在笛卡尔坐标系中增加时间维度以建立时间-空间栅格，构建不同车速下的车辆运动基元；将智能车转化为多个圆心组成的线段，采用膨胀静态障碍物的方法进行不规则障碍物碰撞检测，运用时间间隔法将动态障碍物碰撞检测简化为线段交叉性检测；构建包含最大速度和加速度约束的速度启发式函数，用于引导搜索树在时空空间中快速到达目标位置和速度；最后基于启发式方法和局部运动规划方法在时空栅格中进行搜索，获取融合时间信息和“停止-等待”等决策信息的运动轨迹。实验结果表明：本文运动规划方法在动静态混合环境中能够引导智能车安全行驶，相比速度障碍方法，安全行驶的平均成功率提升23%；相比混合A*方法，平均成功率提升19%，行驶耗时缩短21%。     

道路工程SOM-RBF神经网络估价模型研究

研究从道路工程全生命周期造价的角度,运用自适应(SOM)和径向基(RBF)神经网络的模型与全生命显著性造价(WLCS)结合的预测模型对已完工程造价信息进行数据挖掘,估算工程造价。建立WLCS模型并分析计算。运用自组织SOM网络对WLCS模型分析结果进行特征抽取,验证其对类似工程的确定。在此基础上,运用基于自组织RBF网络对投资估算进行预测。结果表明,WLCS应用可以减少造价估算的工作量,SOM网络可以对道路工程的显著性成本项目(CSIs)造价数据进行特征提取,WLCS的SOM-RBF网络模型减少了估价计算量,并提高了估价精度。本研究成果适用于道路工程造价管理研究和应用领域。     

网络环境下科技查新及其存在的问题研究

本文针对网络环境下科技查新工作,对科技查新与信息检索、信息分析、竞争情报之间的关系进行了辨析,剖析了影响科技查新的主要因素,并从提高查新人员的整体素质、制定查新工作统一标准、拓展文献检索手段、加强查新员与委托人之间的交流等方面提出了相应的提高科技查新质量的措施。     

公路工程造价投资控制现状与对策

分析了现阶段公路工程建设造价投资控制现状及存在的问题,结合国外先进投资理念,针对决策阶段、建设阶段、运营阶段造价控制提出对策.根据国家交通运输“十二五”发展规划,提出了全寿命成本理念及交通建设节约资源、有效利用土地资源的新理念.     

花场湾自然条件及海床演变分析

通过对海南花场湾海域潮汐、潮流、泥沙、波浪以及底质沉积物等自然条件的分析,对该海区的水动力、泥沙环境及海床演变规律进行了研究,论证了花场湾海域略有冲刷的基本特征。     

高速列车研制中环境保护的几个问题

针对高速列车对环境危害最严重的噪声，振动和电磁干扰问题作了阐述，并提出了解决办法。     

捞雷机器人土力学特性研究

介绍了一种沿半埋鱼雷体表面爬行的捞雷机器人作业装置构成和原理，建立了捞雷机器人在水下泥土环境中作业的力学模型，对该模型和钻进状态进行了较为详细的分析。