普通干线公路监测工作的安全管理研究

对普通干线公路监测工作事故的成因进行了分析,得出影响监测工作安全的五大主要风险因素即人、设备、基础设施、环境、管理.然后利用解释结构模型(ISM)、系统化的建模技术和计算机工具对这五大风险因素间的逻辑关系进行了运算和分析,并由此建立了描述影响普通干线公路监测工作安全的多级递阶解释结构模型.在此基础上,利用风险矩阵法对普通干线公路监测工作中的各个风险源进行了评价并确定了风险等级.最后针对不同的风险等级,依据风险控制原则,对各个风险因素提出了相应的措施和建议,以提高普通干线公路监测工作中的安全管理水平.     

快速公交客流量影响因素解释结构模型构建与应用

针对当前交通拥堵、能源紧张、尾气污染等一系列问题,为加快快速公共交通的发展、提高公共交通利用率,本文找出了影响快速公交客流的主要因素,利用系统工程学中的ISM方法,构建了快速公交客流系统解释结构模型,通过分析探讨快速公交客流的主要影响因素,进而明确了各因素间的清晰结构,得到影响快速公交客流量的5级递阶结构;最后找出影响快速公交客流因素的各影响因素.通过影响因素,建立了多元回归模型,从影响程度、影响方向、影响时期分别归类分析.通过分析各层级因素对快速公交客流量的影响,可引导更多居民选择快速公交出行,对缓解交通压力提供了一定方法指导和决策支持.并通过合理分析为快速公交运输服务提出了建议.     

东直门交通枢纽客流量规律研究

分别对激光和视频2种检测设备进行了人工校验,利用统计学知识对枢纽客流数据进行统计分析,得到了东直门交通枢纽进出客流量和不同运输方式客流量的客流规律,对枢纽科学的运营管理和安全防范具有重要的指导意义.     

公交优先硬件在环测试评估平台设计

为了对复杂交通条件下的公交优先策略进行测试评估,比较不同信号机中公交控制策略的优劣,论文引入公交优先硬件在环仿真技术,设计搭建公交优先的硬件在环测试平台,开发信号转换器,实现信号机与仿真软件之间的通信,并能兼容不同信号机,实现对公交优先策略的仿真评估,同时开发上位机测试平台控制系统的运转.该测试平台可推广应用于线控、网控等控制策略测试,提高控制策略的有效性.     

北京市中小学生出行特征及校车方案设计原则研究

针对近年来私人小汽车接送学生日益普遍,使得学校周边交通状况日益恶化的问题,以北京西部城郊的6所小学和6所中学为研究对象,进行问卷调查,研究了中小学出行特征及学生家长对校车开行的需求及意见.在此基础上,从管理方案、人员及资金、法律监管、线路规划方面设计了校车开行方案.     

Flow-field guided steering control for rigid autonomous ground vehicles in low-speed manoeuvring

ABSTRACT

This paper studies the low-speed manoeuvring problem for autono-mous ground vehicles operating in complex static environments. Making use of the intrinsic property of a fluid to naturally find its way to an outflow destination, a novel guidance method is proposed. In this approach, a reference flow field is calculated numerically through Computational Fluid Dynamics, based on which both the reference path topology and the steering reference to achieve the path are derived in a single process. Steering control considers three constraints: obstacle and boundary avoidance, rigidity of the vehicle, plus the non-holonomic velocity constraints due to the steering system. The influences of the parameters used during the flow field simulation and the control algorithm are discussed through numerical cases. A divergency field is defined to evaluate the quality of the flow field in guiding the vehicle. This is used to identify any problematic branching features of the flow, and control is adapted in the neighbourhood of such branching features to resolve possible ambiguities in the control reference. Results demonstrate the effectiveness of the method in finding smooth and feasible motion paths, even in complex environments.     

Asynchronous n-step Q-learning adaptive traffic signal control

Ensuring transportation systems are efficient is a priority for modern society. Intersection traffic signal control can be modeled as a sequential decision-making problem. To learn how to make the best decisions, we apply reinforcement learning techniques with function approximation to train an adaptive traffic signal controller. We use the asynchronous n-step Q-learning algorithm with a two hidden layer artificial neural network as our reinforcement learning agent. A dynamic, stochastic rush hour simulation is developed to test the agent’s performance. Compared against traditional loop detector actuated and linear Q-learning traffic signal control methods, our reinforcement learning model develops a superior control policy, reducing mean total delay by up 40% without compromising throughput. However, we find our proposed model slightly increases delay for left turning vehicles compared to the actuated controller, as a consequence of the reward function, highlighting the need for an appropriate reward function which truly develops the desired policy.     

A study of the environmental impacts of intelligent automated vehicle control at intersections via V2V and V2I communications

This article presents a novel intersection traffic management system for automated vehicles and quantifies its impact on fuel consumption and greenhouse gas emissions of CO₂ relative to traditional traffic signal and roundabout intersection control. The developed intelligent traffic management (ITM) techniques, which are based on a spatiotemporal reservation scheme, ensure that vehicles proceed through the intersection without colliding with other vehicles while at the same time reducing the intersection delay and environmental impacts. Specifically, the spatiotemporal reservation scheme provides each vehicle a collision-free path that is decomposed into a speed profile along with navigational instructions. The integration of the developed microscopic traffic simulator with instantaneous emission model, provides improved assessments of the environmental impact of traffic control strategies at intersections. The simulator architecture integrates several ITM algorithms, vehicle sensors, V2V/V2I communications, and emission and fuel consumption models. Each vehicle is modeled by an agent and each agent provides information depending on the specific vehicle sensors. The ITM system is supported by V2V and V2I communications, allowing the exchange of information among vehicles and infrastructure. The data include the estimated vehicle position and speed. Compared with traditional traffic management techniques, the simulation results prove that the proposed ITM system reduces CO₂ emissions significantly. The research also shows that these reductions are more significant when the traffic flow increases.     

排阵式交叉口延误及最佳周期模型

为了提高排阵式交叉口这一非常规信号交叉口的运行效率，对其延误和最佳周期进行分析。首先针对先直行后左转、先左转后直行和直行左转交替通行3种信号相位相序，通过对排序区内车辆驶入、驶离、受信号控制阻滞等车流运行情况的分析，构建可反映排阵式交叉口车辆2次停车启动的车均延误计算模型。通过仿真对比可知，左转和直行延误估算误差均在10%范围内。在此基础上，以交叉口总延误最小为目标，考虑清空时长、主、预信号相位差、绿灯时长等约束条件，建立排阵式交叉口最佳周期理论模型。针对不同排阵式控制进口道数量设置的情况，通过对最佳周期的拟合分析，建立最佳周期简化模型。与理论模型相比，最佳周期简化模型的拟合优度在0.935~0.972范围内。通过模型对比和案例分析，对最佳周期简化模型的优化效益和稳定性进行检验。研究结果表明：在非饱和状态下，建立的最佳周期模型的平均误差和均方误差分别为2.13%和2.39%，均小于Webster模型和HCM2010模型的计算结果，具有较高的准确性和稳定性，案例中可降低车均延误36.46%；相较于传统信号控制交叉口，建议排阵式交叉口采用较小的周期时长，且当关键流量比大于0.6时尤为显著，分析中发现最佳周期减小14.53%~34.65%。     

电气化公路技术研究

交通是能源消纳的重要领域，其对社会能源结构的调整和节能减排具有重要的影响和带动作用。在陆路交通方面，轨道交通和小型客运车辆的电气化发展方向相对清晰，而公路货运尚无明确的技术路线。为充分利用清洁能源，减少化石能源在交通领域中的使用，发展并推广电气化公路技术，对重载卡车的蓄电池、燃料电池、混合动力3种电气化方案进行对比分析，结合卡车负载的特点，对不同电气化方案进行评估。研究融合新能源发电及并网、混合动力技术、智能驾驶技术的电气化公路技术，从车辆动力技术、供电线路、受电弓与计费、新能源并网、编组智能驾驶运行5个方面对电气化公路方案进行介绍，可以实现公路货运系统的绿色、安全、高效运行。以10公里线路电气化为例，进行规划和成本估算，构建单车和区域（或路段）电气化的运营成本计算模型，基于燃油消耗和阻力分析2种方法进行计算。对车辆购置及改装、基础设施建设、可再生能源发电、系统环保效益等4个方面进行技术分析与成本测算。从供能技术变革、商业模式和推广路线3个角度对电气化公路技术的未来推广进行论证展望。研究结果表明：电气化公路技术的应用与规模化能够带来巨大的经济与环境收益，其中的混合动力方案与架空线输电方案为面对未来可能的储能技术变革与无线输电普及打下重要的基础，提供了拓展空间，也是重载货车实现智能驾驶的重要步骤；与燃油驱动相比，电驱动百公里成本减少96元，单一重卡年运营成本减少18.8万元，百公里污染物（如：碳氧化物、氮氧化物等）均减少50%以上。