Increasing the capacity of signalized intersections with separate left turn phases

A separate turn phase is often used on the approach leg to an intersections with heavy left turns. This wastes capacity on the approach because some of its lanes cannot discharge during its green phases. The paper shows that the problem can be eliminated by reorganizing traffic on all the lanes upstream of an intersection using a mid-block pre-signal. If drivers behave deterministically, the capacity that can be achieved is the same as if there were no left turns. However, if the reorganization is too drastic, it may be counterintuitive to drivers. This can be remedied by reorganizing traffic on just some of the available lanes. It is shown that such partial reorganization still increases capacity significantly, even if drivers behave randomly and only one lane is reorganized. The paper shows how to optimize the design of a pre-signal system for a generic intersection. It also identifies both, the potential benefits of the proposed system for a broad class of intersections, and the domain of application where the benefits are most significant.     

Estimating traffic volumes for signalized intersections using connected vehicle data

Recently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT). At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimate traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilot Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9–12%, based on benchmark data manually collected and data from loop detectors. Considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future.     

分离式立交地面层交叉口一体化设计方法研究

分离式立交是解决城市拥堵的有效手段,但立交桥地面层的平面交叉口设计仍缺乏足够重视,现有的组织方式、控制方案无法有效应对立交桥下特有的交通环境.文中分析立交地面层交叉口特有的交通环境,提出一体化组织方法及信号控制模型,运用VISSIM仿真软件分析评价一体化组织方法的适用范围,为实践中可否采用一体化组织方法的决策提供参考....     

车载网络安全的系统设计研究及关键技术开发

本文介绍了车载网络安全的系统设计研究及关键技术开发,以应对黑客通过网联攻击车辆,建立智能网联汽车车内网络信息安全交互模型;分析“外部网络环境”与“车内ECU群体”之间进行“信息资源”交互过程中的安全风险,研究其对车内网络信息安全的破坏机理;利用基于密码学理论研究车内网络安全通信协议的设计方法,解决车内网络在智能网联汽车环境下面临的信息安全问题。     

排阵式交叉口几何设计与信号控制协同鲁棒优化

针对排阵式交叉口在实际交通波动环境中存在车辆滞留排序区,运行效率稳定性难以保障的问题,提出了鲁棒优化方法,平衡交叉口运行的效率和稳定性。在分析排阵式交叉口运行特性的基础上,指出了其运行效率波动性与交叉口几何设计、信号控制、交通需求、饱和流率和运行车速这5个因素有关。确定了将交通需求、饱和流率和运行车速这3个客观波动因素作为模型的输入参数,将几何设计和信号控制这2个可受设计人员控制的要素作为模型的优化控制变量进行协同优化的模型框架。在此基础上,以交叉口车均延误条件风险值最小为目标,考虑了各流向车道数、信号相位相序、排序区车辆清空等方面的约束条件,构建了基于情景的鲁棒优化模型,并建立了遗传算法对模型进行求解。通过案例分析,对鲁棒优化模型的置信水平取值和算法准确性进行了分析,证明了算法可以使目标函数收敛到最小值,并基于蒙特卡洛模拟对优化效益进行了检验。研究发现,所建立的几何设计与信号控制协同鲁棒优化模型可实现在交通需求和供给的波动下,对排阵式交叉口的车道功能、排序区长度以及主、预信号控制进行协同优化。相较于确定性的设计方法,在平均延误层面基本维持原有水平,但对延误标准差和最大值有着较为明显的改善,案例中分别减少了48%和23%。     

A signal-based fault detection and classification method for heavy haul wagons

This paper proposes a signal-based fault detection and isolation (FDI) system for heavy haul wagons considering the special requirements of low cost and robustness. The sensor network of the proposed system consists of just two accelerometers mounted on the front left and rear right of the carbody. Seven fault indicators (FIs) are proposed based on the cross-correlation analyses of the sensor-collected acceleration signals. Bolster spring fault conditions are focused on in this paper, including two different levels (small faults and moderate faults) and two locations (faults in the left and right bolster springs of the first bogie). A fully detailed dynamic model of a typical 40t axle load heavy haul wagon is developed to evaluate the deterioration of dynamic behaviour under proposed fault conditions and demonstrate the detectability of the proposed FDI method. Even though the fault conditions considered in this paper did not deteriorate the wagon dynamic behaviour dramatically, the proposed FIs show great sensitivity to the bolster spring faults. The most effective and efficient FIs are chosen for fault detection and classification. Analysis results indicate that it is possible to detect changes in bolster stiffness of ±25% and identify the fault location.     

A technical note on evaluating the effectiveness of bus rapid transit with transit signal priority

Transit Signal Priority (TSP) and Bus Rapid Transit (BRT) are innovative Intelligent Transportation System (ITS) tools that can reduce travel times for buses. Combining TSP and BRT can significantly improve bus travel, but can negatively impact network traffic operations. Although TSP has been implemented worldwide, few previous studies holistically examined the effects of using various conditional and unconditional TSP strategies with or without a BRT system. This research simulates multiple TSP and BRT combination scenarios to understand their impact on traffic operations, including crossing street traffic. A test bed along International Drive (I-Drive) in Orlando, Florida, was chosen as the simulation area. Field data collected for this test bed, which included traffic volumes, bus travel times, and traffic signal control data, were used to develop, calibrate, and validate the simulation model. Results showed that BRT with Conditional TSP 3 minutes behind significantly improved travel times, average speed, and average total delay per vehicle for the main through movements compared with no BRT or TSP, with only minor effects on crossing street delays. BRT with Unconditional TSP resulted in significant crossing street delays, especially at major intersections with high traffic demand, indicating that this scenario is impractical for implementation. The simulation suggests that BRT and TSP will be most effective when used in areas where crossing street volumes are low. However, it is unknown how these ITS tools affect pedestrian traffic. Using optimization methods can determine the best strategy to balance transit and pedestrian traffic.     

城市交通干线信号协调控制方案过渡调整策略

介绍了国内外当前交通信号协调控制方案过渡方法的研究现状,分析比较了现有的干线协调过渡方法.针对当前过渡调整方法对于交通信号控制机如何实现协调方案的快速调整涉及较少,未考虑交通信号机实现过渡调整过程中的实际需求等不足,以当日零点为协调控制方案的起始点,针对交叉口过渡信号周期以及周期调整步幅的取值范围,提出了1种基于周期的交通信号协调控制过渡调整策略,并进行了算例分析.结果表明:该策略能够在1~2个周期内快速调整完毕,减少了信号配时的突变所引发路面交通的剧烈波动,实现了交通流运行的平稳和连续.文中所介绍的算法可以实现不同控制方式之间的切换,也可以用于城市区域协调控制中,具有较广的适用范围和实用价值.     

EEMD和SVM在发动机故障诊断中的应用

针对发动机缸盖振动信号的非线性非平稳特征,提出一种总体平均经验模态分解(EEMD)和支持向量机相结合的信号分析及故障诊断方法,该方法利用EEMD算法以及IMF序列和原始振动信号之间的相关系数,有效放大故障诊断特征向量的差异。对原始振动信号进行EEMD分解,得到各阶特征模态函数(IMF),求各阶IMF分量对应于原始信号的相关系数并组成故障分类特征向量。分别将IMF相关系数法和IMF能量分布法得到的特征向量作为输入,建立BP神经网络和支持向量机,判断发动机工作状态和故障类型。分析表明,对IMF求相关系数的方法简便易行,能有效放大不同工况下特征向量的差异,结合支持向量机能够对既定机型的配气机构和点火系常见故障进行准确识别。