Probe vehicle-based traffic state estimation method with spacing information and conservation law

This paper proposes a method of estimating a traffic state based on probe vehicle data that contain spacing and position of probe vehicles. The probe vehicles were assumed to observe spacing by utilizing an advanced driver assistance system, that has been implemented in practice and is expected to spread in the near future. The proposed method relies on the conservation law of the traffic flow but is independent of a fundamental diagram. The conservation law is utilized for reasonable aggregation of the spacing data to acquire the traffic state, i.e., a flow, density and speed. Its independence from a fundamental diagram means that the proposed method does not require predetermined nor exogenous assumptions with regard to the traffic flow model parameters. The proposed method was validated through a simulation experiment under ideal conditions and a field experiment conducted under actual traffic conditions; and empirical characteristics of the proposed method were investigated.     

MPLS TE在高铁数据网系统中的配置设计与验证测试

MPLS TE技术提供了服务质量（QoS）和流量工程（TE）控制的能力,可以根据路径、资源可用性和QoS请求为数据流设置直接的路由,避免链路拥塞导致数据传送丢失。介绍MPLS TE技术、TE配置和路由保护配置,及其测试方案。     

公路隧道交通量的预测及分析

公路隧道交通量的预测对隧道通风系统的节能以及降低隧道运营成本有很重要的意义。分别利用多元统计分析法和BP神经网络两种方法对公路隧道交通量进行了预测,并对两类预测数据进行分析比较,得出了多元统计分析法适用于车流量少而且稳定的公路隧道的预测,而BP神经网络法则适用于车流量大而且不稳定的公路隧道的预测的结论。     

分散自律调度集中试验验收方法探讨

主要介绍了成都铁路局遂渝线FZy-CTC分散自律调度集中系统的试验验收方法、具体内容及步骤，对围绕分散自律调度集中系统本身特点进行的试验及验收内容进行了详细的介绍，为将来新建的分散自律调度集中系统的验收提供了可参考的方法。     

汽车碰撞事故再现模型病态问题处理方法研究

为了解决汽车碰撞事故再现模型病态性的问题,采用汽车碰撞模拟法分析了参数变化对模型病态性的影响;以矩阵扰动理论为基础分析了模型病态性的本质;提出了通过处理原始方程组解决模型病态性的方法,通过实例验证了该方法的有效性。     

基于混沌蚁群算法的最短路径选择研究

如何解决最短路径选择问题一直是城市交通流诱导系统的关键之一.基于群体仿生理论的蚁群算法是解决此问题的一种方法,针对采用蚁群算法进行最短路径选择时易出现的陷入局部最优解问题,引入混沌理论,采用混沌蚁群算法利用混沌初始化进行改善个体质量和利用混沌扰动避免在蚁群算法搜索过程中陷入局部极值,同时降低了蚁群算法的时间复杂度,从而更好的解决了最短路径选择问题.     

Stop-and-go traffic analysis: Theoretical properties,environmental impacts and oscillation mitigation

This study aims (i) to analyze theoretical properties of a recently proposed describing-function (DF) based approach (Li and Ouyang, 2011; Li et al., 2012) for traffic oscillation quantification, (ii) to adapt it for estimating fuel consumption and emission from traffic oscillation and (iii) to explore vehicle control strategies of smoothing traffic with advanced technologies. The DF approach was developed to predict traffic oscillation propagation across a platoon of vehicles following each other by a nonlinear car-following law with only the leading vehicle’s input. We first simplify the DF approach and prove a set of properties (e.g., existence and uniqueness of its solution) that assure its prediction is always consistent with observed traffic oscillation patterns. Then we integrate the DF approach with existing estimation models of fuel consumption and emission to analytically predict environmental impacts (i.e., unit-distance fuel consumption and emission) from traffic oscillation. The prediction results by the DF approach are validated with both computer simulation and field measurements. Further, we explore how to utilize advantageous features of emerging sensing, communication and control technologies, such as fast response and information sharing, to smooth traffic oscillation and reduce its environmental impacts. We extend the studied car-following law to incorporate these features and apply the DF approach to demonstrate how these features can help dampen the growth of oscillation and environmental impact measurements. For information sharing, we convert the corresponding extended car-following law into a new fixed point problem and propose a simple bisecting based algorithm to efficiently solve it. Numerical experiments show that these new car-following control strategies can effectively suppress development of oscillation amplitude and consequently mitigate fuel consumption and emission.     

The fleet size and mix pollution-routing problem

This paper introduces the fleet size and mix pollution-routing problem which extends the pollution-routing problem by considering a heterogeneous vehicle fleet. The main objective is to minimize the sum of vehicle fixed costs and routing cost, where the latter can be defined with respect to the cost of fuel and CO₂ emissions, and driver cost. Solving this problem poses several methodological challenges. To this end, we have developed a powerful metaheuristic which was successfully applied to a large pool of realistic benchmark instances. Several analyses were conducted to shed light on the trade-offs between various performance indicators, including capacity utilization, fuel and emissions and costs pertaining to vehicle acquisition, fuel consumption and drivers. The analyses also quantify the benefits of using a heterogeneous fleet over a homogeneous one.