智能交通网络防止抢灯的无线预警系统——基于zigbee道路交叉口防抢行预警系统

为了解决一些驾驶人员在通过交通路口时冲灯抢行的行为,提出了一种智能交通网络防止抢灯的无线预警系统,介绍了利用ZigBee实现车辆和交通信号灯之间通信,从而构成智能交通网络,避免抢灯行为,并阐述该系统结构及构成。     

基于VISSIM的北土城西路交叉口交通组织优化

信控交叉口的渠化和信号配时直接影响交通的效率和安全。因此,如何对路口进行合理的渠化和信号配时使得车辆与行人安全、高效地通过信控交叉口具有重要意义。文章基于对北京北土城西路口相关交通数据的实地调查,分析了该路口存在的交通问题,并针对该路口的交通特性,使用交通仿真软件VISSIM对该信号交叉口进行了仿真研究,从路口渠化、信号配时等角度提出了三种优化设计方案。     

高速公路交织区汇入行为预测研究

构建高速公路交织区汇入决策模型，准确预测车辆汇入行为可以有效地减少汇入风险，缓解因不恰当汇入行为导致的高速公路拥堵。本文提出一种基于CART分类树的车辆汇入行为预测模型，基于NGSIM车辆轨迹数据并综合选取14个影响汇入行为的特征变量对模型进行验证与分析。由预测结果可知：模型的预测效果较好，泛化程度较高，其分类准确率，真正类率和真负类率分别为98.4%,98.5%和94.7%；与二项逻辑回归模型及支持向量机模型预测结果进行对比，分析结果表明CART分类树模型在预测交织区汇入行为上具有更高的准确率与正确率，并表现出更优异的性能。由变量敏感性分析结果可知：汇入车辆速度、辅助车道上前车与主体车辆之间的距离、主体车辆的超车时间等对交织区汇入行为具有重要影响。     

交通信号灯转换期间的违规穿越行为及其影响因素分析

交通信号灯转换包括绿灯变黄灯、黄灯变红灯、红灯变绿灯三种情况,信号转换期与红灯稳定期的穿越行为会有所差异,本文研究行人及非机动车在信号灯转换期间的穿越行为特征及其关键影响因素。通过实地拍摄采集数据,将违规行为发生时刻细分为黄灯及全红时期违规、红灯稳定期违规和红灯最后4s的早启动违规,构建logit模型分析个体特征、情景条件和环境条件三类因素对各类违规行为的影响。研究结果表明穿越前是否有冲突车辆对黄灯及全红时期的违规行为产生显著影响;性别、停止纵向位置、等待时间、平均车流量和穿越前是否有冲突车辆对红灯稳定期间的违规有显著影响;交通方式和等待时前面违规的人数对早启动违规产生显著影响。     

导航电子地图加入实时灯态信息的必要性和可行性研究

正在导航电子地图中加入实时交通信号信息,不仅非常必要,具有广阔市场前景,而且随着5G技术推广以及自动驾驶技术的普及,很多技术问题也在逐步解决。现在的导航电子地图均有显示路口的交通信号灯,但在绝大部分城市,电子地图无法显示交通信号灯的实时灯态。本文就电子导航地图显示实时灯态的必要性、可行性进行初步探讨。     

基于计算机视觉技术的交通场景检测平台的设计与应用

文章针对当前城市智慧交通监管的路口交通的饱和度、拥堵以及闯红灯等问题,设计了基于计算机视觉技术的交通场景检测平台,结合机器学习算法,并融合多项高新技术,实现对路口过往车辆的流量和车速检测,对机动车闯红灯、斑马线不礼让行人、占用公交车道、违停或违规越线等违章行为进行检测识别,为公共交通的管理提供极大便利,实现了“智慧交通”和“平安交通”。     

信号交叉口违规者间隙接受行为研究

本文研究信号交叉口行人、自行车和电动车的间隙接受行为。通过拍摄录像获取相关数据,比较了不同交通方式的接受间隙,并用Logit回归建立了间隙接受模型。结果表明三者在近边的接受间隙都显著大于远边,三者之间的接受间隙无显著差异,但非机动车的违规率明显高于行人。主要原因是在有车冲突(有接受间隙)时三者的违规率无明显差异,而在无车冲突(无接受间隙)时非机动车的违规率显著高于行人。等待情况、可接受间隙值、间隙类型和冲突车辆的行为会显著影响间隙接受行为。本文的结果对理解违规群体的穿越行为及其建模有参考价值。     

城市轨道交通突发事件应急疏散仿真方法

地铁车站客流量较大,行人流线复杂,在运营过程中往往存在着一系列的突发事件。为此,文章从城市轨道交通客流需求与运输能力着手,结合车站设备、构造、旅客行为特征,提出了基于MassMotion的城市轨道交通突发事件应急疏散仿真方法。通过确定仿真环境中所需要的基本参数,结合交通过程中行人行为特性,以三维模型空间为基础,对突发大客流进行仿真模拟研究,将车站的设施和行人抽象化。仿真结果显示,突发情况下车站内的所有人员能够在安全时间内迅速撤离到安全区,旅客的疏散速度、工作人员的疏散能力等行为特性对疏散效率有很大的影响。     

基于轨迹数据的无信号交叉口行人过街行为研究

随着我国城市道路运行效率的降低和交通安全形势的日益严峻,行人交通安全已成为普遍关注的问题。本文从无信号道路交叉口的实测轨迹数据中提取相关参数,建立基于随机森林算法的行人过街决策模型并进行效果检验和验证,模型正确率可达93.1%。同时挖掘出各个解释变量对过街决策的重要度,证明将随机森林算法用于行人过街决策分析具有较好的拟合度与预测精度。本研究可应用于人车交互仿真工具中模拟行人行为,为进一步研究人车混行的无信号道路交叉口仿真和控制系统奠定了基础。     

基于蝙蝠算法优化的BP神经网络煤层冲击危险性智能综合评价研究

通过深入分析开采深度、煤层顶底板性质、冲击倾向性、地质构造、开采技术等因素对煤层冲击危险性的影响，建立基于蝙蝠算法优化的BP神经网络煤层冲击危险性智能综合评价模型；将所确定的影响冲击危险性因素进行数量级划分，利用蝙蝠算法对BP神经网络的权值与阈值最优值选择进行优化，对煤层冲击危险性进行无冲击危险性、弱冲击危险性、中等冲击危险性、强冲击危险性的等级评价。应用蝙蝠算法优化BP神经网络煤层冲击危险性智能综合评价模型对内蒙古某矿的煤层及江苏某煤矿工作面进行实例验证，评价结果与综合指数法计算所得结果一致，表明该模型可以用于煤层冲击危险性评价。应用该模型对煤层进行冲击危险性评价时，改善了BP网络结构在权值和阈值确定上的随机缺陷，提高了算法稳定性，因而得到的评价结果更加合理。     

Investigating the applicability of exclusive pedestrian phase at two‐phase actuated controlled intersections

The exclusive pedestrian phase (EPP) has been used in many countries to promote walking around downtown areas by increasing the ease and convenience of pedestrian crossing. However, its applicability has not been systematically demonstrated, especially when an intersection is operated in actuated mode. This paper presents an extensive simulation‐based analysis of the applicability of EPP as compared with a normal concurrent pedestrian‐phase pattern at an isolated intersection controlled by actuated logic. Actuated signal control logics for EPP‐actuated and conventional concurrent pedestrian phase‐actuated controls are developed. Both of these control logics consider pedestrian crossing demands and can adapt to changes in vehicle traffic to reduce vehicle delay as well. A simulation model of a two‐phase controlled intersection is built and calibrated based on field data using VISSIM (PTV Planung Transport Verkehr AG in Karlsruhe, Germany). Extensive analysis is conducted to reveal fully the applicable EPP domain in terms of vehicle traffic demand, pedestrian demand, vehicle turning ratio, and pedestrian diagonal crossing ratio. The results show that the performance and applicable domain of EPP are jointly determined by those five factors. EPP significantly outperforms concurrent pedestrian phase if the vehicle turning ratio is greater than 0.6 and the pedestrian diagonal crossing ratio is greater than 0.6. These results can help traffic engineers in choosing the appropriate pedestrian‐phase patterns at actuated signalized intersections. Copyright © 2015 John Wiley & Sons, Ltd.     

A dynamic evacuation model for pedestrian–vehicle mixed-flow networks

In urban emergency evacuation, a potentially large number of evacuees may depend either on transit or other modes, or need to walk a long distance, to access their passenger cars. In the process of approaching the designated pick-up points or parking areas for evacuation, the massive number of pedestrians may cause tremendous burden to vehicles in the roadway network. Responsible agencies often need to contend with congestion incurred by massive vehicles emanating from parking garages, evacuation buses generated from bus stops, and the conflicts between evacuees and vehicles at intersections. Hence, an effective plan for such evacuation needs to concurrently address both the multi-modal traffic route assignment and the optimization of network signal controls for mixed traffic flows. This paper presents an integrated model to produce the optimal distribution of vehicle and pedestrian flows, and the responsive network signal plan for massive mixed pedestrian–vehicle flows within the evacuation zone. The proposed model features its effectiveness in accounting for multiple types of evacuation vehicles, the interdependent relations between pedestrian and vehicle flows via some conversion locations, and the inevitable conflicts between intersection turning vehicle and pedestrian flows. An illustrating example concerning an evacuation around the M&T stadium area has been presented, and the results indicate the promising properties of our proposed model, especially on reflecting the complex interactions between vehicle and pedestrian flows and the favorable use of high-occupancy vehicles for evacuation operations.     

Real-time queue length estimation for congested signalized intersections

How to estimate queue length in real-time at signalized intersection is a long-standing problem. The problem gets even more difficult when signal links are congested. The traditional input–output approach for queue length estimation can only handle queues that are shorter than the distance between vehicle detector and intersection stop line, because cumulative vehicle count for arrival traffic is not available once the detector is occupied by the queue. In this paper, instead of counting arrival traffic flow in the current signal cycle, we solve the problem of measuring intersection queue length by exploiting the queue discharge process in the immediate past cycle. Using high-resolution “event-based” traffic signal data, and applying Lighthill–Whitham–Richards (LWR) shockwave theory, we are able to identify traffic state changes that distinguish queue discharge flow from upstream arrival traffic. Therefore, our approach can estimate time-dependent queue length even when the signal links are congested with long queues. Variations of the queue length estimation model are also presented when “event-based” data is not available. Our models are evaluated by comparing the estimated maximum queue length with the ground truth data observed from the field. Evaluation results demonstrate that the proposed models can estimate long queues with satisfactory accuracy. Limitations of the proposed model are also discussed in the paper.     

Optimization of pedestrian phase patterns at signalized intersections: a multi‐objective approach

This paper presents a multi‐objective optimization model and its solution algorithm for optimization of pedestrian phase patterns, including the exclusive pedestrian phase (EPP) and the conventional two‐way crossing (TWC) at an intersection. The proposed model will determine the optimal pedestrian phase pattern and the corresponding signal timings at an intersection to best accommodate both vehicular traffic and pedestrian movements. The proposed model is unique with respect to the following three critical features: (1) proposing an unbiased performance index for comparison of EPP and TWC by explicitly modeling the pedestrian delay under the control of TWC and EPP; (2) developing a multi‐objective model to maximize the utilization of the available green time by vehicular traffic and pedestrian under both EPP or TWC; and (3) designing a genetic algorithm based heuristic algorithm to solve the model. Case study and sensitivity analysis results have shown the promising property of the proposed model to assist traffic practitioners, researchers, and authorities in properly selecting pedestrian phase patterns at signalized intersections. Copyright © 2013 John Wiley & Sons, Ltd.     

Application of social force model to pedestrian behavior analysis at signalized crosswalk

Limited pedestrian behavior models shed light on the case at signalized crosswalk, where pedestrian behavior is characterized by group or individual evasion with surrounding pedestrians, collision avoidance with conflicting vehicles, and response to signal control and crosswalk boundary. This study fills this gap by developing a microscopic simulation model for pedestrian behavior analysis at signalized intersection. The social force theory has been employed and adjusted for this purpose. The parameters, including measurable and non-measurable ones, are either directly estimated based on observed dataset or indirectly derived by maximum likelihood estimation. Last, the model performance was confirmed in light of individual trajectory comparison between estimation and observation, passing position distribution at several cross-sections, collision avoidance behavior with conflicting vehicles, and lane-formation phenomenon. The simulation results also concluded that the model enables to visually represent pedestrian crossing behavior as in the real world.     

Modeling pedestrians’ road crossing behavior in traffic system micro-simulation in China

In many Chinese cities, pedestrian’s road crossing behavior is different from that of pedestrians in developed countries. This paper presents a pedestrian model for traffic system micro-simulation in China. Considering the high rate of signal non-compliance, we classify pedestrians into two types: law-obeying ones and opportunistic ones. Opportunistic ones decide whether to violate traffic signal during red man, depending on the states of some external factors (like policeman, vehicle flow and other pedestrians’ behaviors). Questionnaires were used to determine the proportions of these two types of pedestrians under different circumstances. In addition, a time gap distribution extracted from videotape were used to determine the criterion for pedestrians to decide whether to walk or wait when they conflict with vehicle flows. However, simulation results deviate from the data extracted from videotape in some degree. By adjusting the parameters on the basis of analyzing the occurrence of the deviations, the simulation results agree with the field results better. This model has represented the high rate of pedestrians’ red light running and the mixed characteristics of traffic flows in Chinese cities, and it may be applicable in the micro-simulation of traffic system in other developing cities.     

Multi-objective signal control of urban junctions – Framework and a London case study

This paper presents an approach to multi-objective signal control using fuzzy logic. The signal control uses fuzzy logic where the membership functions are optimised according to the Bellman–Zadeh principle of fuzzy decision-making. This approach is both practical for the decision-maker and efficient, as it leads directly to a Pareto-optimal solution. Signal control priorities are ultimately a political decision. Therefore the tool developed in this research allows the traffic engineer to balance the objectives easily by setting acceptability and unacceptability thresholds for each objective. Particular attention is given in the example to pedestrian delays. The membership functions of the fuzzy logic are optimised by a genetic algorithm coupled to the VISSIM microscopic traffic simulator. The concept is illustrated with a case study of the Marylebone Road–Baker Street intersection in London at which pedestrians as well as vehicle flows are high. The results prove the feasibility of the framework and show the vehicle delays for a more pedestrian friendly signal control strategy.     

Modeling the effect of electric vehicle adoption on pedestrian traffic safety: An agent-based approach

When operated at low speeds, electric and hybrid vehicles have created pedestrian safety concerns in congested areas of various city centers, because these vehicles have relatively silent engines compared to those of internal combustion engine vehicles, resulting in safety issues for pedestrians and cyclists due to the lack of engine noise to warn them of an oncoming electric or hybrid vehicle. However, the driver behavior characteristics have also been considered in many studies, and the high end-prices of electric vehicles indicate that electric vehicle drivers tend to have a higher prosperity index and are more likely to receive a better education, making them more alert while driving and more likely to obey traffic rules. In this paper, the positive and negative factors associated with electric vehicle adoption and the subsequent effects on pedestrian traffic safety are investigated using an agent-based modeling approach, in which a traffic micro-simulation of a real intersection is simulated in 3D using AnyLogic software. First, the interacting agents and dynamic parameters are defined in the agent-based model. Next, a 3D intersection environment is created to integrate the agent-based model into a visual simulation, where the simulation records the number of near-crashes occurring in certain pedestrian crossings throughout the virtual time duration of a year. A sensitivity analysis is also carried out with 9000 subsequent simulations performed in a supercomputer to account for the variation in dynamic parameters (ambient sound level, vehicle sound level, and ambient illumination). According to the analysis, electric vehicles have a 30% higher pedestrian traffic safety risk than internal combustion engine vehicles under high ambient sound levels. At low ambient sound levels, however, electric vehicles have only a 10% higher safety risk for pedestrians. Low levels of ambient illumination also increase the number of pedestrians involved in near-crashes for both electric vehicles and combustion engine vehicles.     

A model of pedestrians’ intended waiting times for street crossings at signalized intersections

For the purposes of both traffic-light control and the design of roadway layouts, it is important to understand pedestrian street-crossing behavior because it is not only crucial for improving pedestrian safety but also helps to optimize vehicle flow. This paper explores the mechanism of pedestrian street crossings during the red-man phase of traffic light signals and proposes a model for pedestrians’ waiting times at signalized intersections. We start from a simplified scenario for a particular pedestrian under specific traffic conditions. Then we take into account the interaction between vehicles and pedestrians via statistical unconditioning. We show that this in general leads to a U-shaped distribution of the pedestrians’ intended waiting time. This U-shaped distribution characterizes the nature of pedestrian street-crossing behavior, showing that in general there are a large proportion of pedestrians who cross the street immediately after arriving at the crossing point, and a large proportion of pedestrians who are willing to wait for the entire red-man phase. The U-shaped distribution is shown to reduce to a J-shaped or L-shaped distribution for certain traffic scenarios. The proposed statistical model was applied to analyze real field data.     

The use of gait parameters to evaluate pedestrian behavior at scramble phase signalized intersections

Pedestrian scramble phasing is usually implemented to reduce pedestrian‐vehicle conflicts and therefore increase the safety of the intersection. However, to adequately determine the benefits of scramble phasing, it is necessary to understand how pedestrians react to such an unconventional design. This study investigates changes in pedestrian crossing behavior following the implementation of a scramble phase by examining the spatiotemporal gait parameters (step length and step frequency). This detailed microscopic‐level analysis provides insight into changes in pedestrian walking mechanisms as well as the effect of various pedestrian and intersection characteristics. The study uses video data collected at a scramble phase signalized intersection in Oakland, California. Gait parameters were found to be influenced by pedestrian gender, age, group size, crosswalk length, and pedestrian signal indications. Both average step length and walking speed were significantly higher for diagonally crossing pedestrians compared with pedestrians crossing on the conventional crosswalks. Pedestrians were found to have the tendency to increase their step length more than their step frequency to increase walking speed. It was also found that, compared with men, women generally increase their walking speed by increasing their step frequency more than step length. However, when in non‐compliance with signal indications, women increase their walking speed by increasing their step length more than step frequency. It was also found that older pedestrians do not significantly change their walking behavior when in non‐compliance with signal indications. Copyright © 2014 John Wiley & Sons, Ltd.