交通态势识别及状态转换机制研究

利用驾驶员的认知拓扑生成动态交通态势图,以克服驾驶员视野盲区的缺陷,可进一步避免交通事故的发生,为交通运输系统和智能车辆系统的仿真研究和实物开发提供理论指导与技术支持。

新冠肺炎疫后交通运输新业态发展情景研判及对策建议

为了把握新冠肺炎疫情给交通运输新业态带来的机遇,考虑疫情对交通相关者的心理影响,采用比较研究法,对比"非典"疫情引发的变化,结合当前经济、科技形势,对新冠肺炎疫后交通运输新业态的发展情景进行了预判。研究认为:慢行交通、定制公交、中小运量轨道交通等将逐渐得到公众认同,城市客运交通将更加均衡化发展;货运物流智能化、全球化、全链条化趋势明显,并将促进物流业转型升级;跨部门大数据平台、协同治理需求加剧,为构建基于综合交通大数据的智能化产业链奠定基础。最后,建议科学识别发展契机,重视新技术和新业态发展的规划引导、标准规范、政策储备等,推动短期影响转变为有益于行业发展的长远趋势。

高速公路稳态交通流的最优问题

本文针对高速公路稳态交通流，首先建立了高速公路稳态交通流模型，并将总服务流量、行程时间作为稳态控制的性能指标，提出了稳态交通流状态下的两个最优问题及解法。

基于西门子S7-200与组态王的十字交通灯控制系统设计

本文分析了十字交通灯的控制需求,利用西门子系列的S7-200型PLC与上位机即工控组态软件组态王6.53实现了十字交通灯控制系统的设计。基于PLC和工控监控软件的系统具有抗干扰强和运行可靠等优点,本设计为十字交通灯监控的可视化,网络化,区域化,远程化的研究打下基础。

上海市高架道路宏观稳态交通流建模及应用

利用宏观交通流模型对高架道路的运行状况进行了评价。在模型的选择上包括三个基本模型:递推模型、起始—到达模型、起始—终点模型。利用递推模型分析了上海市中心城区高架道路交通流状态。并与实际运行情况基本吻合。可见模型从宏观层次上能充分反映高架交通流随时间和空间的变化及分布规律,从而分析、评价了高架道路各路段的交通运行状态,指出了高架道路的交通瓶颈所在。

交通流动态扰动下的区域交通信号协调控制

针对区域交通信号,考虑智能交通系统中交通流的动态特性,提出了区域交通系统改进的存储-转发模型.考虑大型区域的复杂性和协调性,将区域交通划分成N个子区域,分别建立了对应的子区域模型.针对子区域模型,提出了基于分层模型预测控制的过饱和区域交通信号控制优化目标.通过引入拉格朗日对偶原理解决约束条件问题的方法,对子区域的车辆排队数量进行了预测,并对有效绿灯时间进行优化控制.为了验证所提区域交通控制算法的有效性,给出了本文改进的模型与存储-转发模型的对比仿真.实验结果表明,在达到相同的控制效果时,本文改进模型的控制算法所需的计算时间较短,计算成本较低.

城市交通仿真中的延误计算模型对比分析

论述了三种城市交通仿真(HCM2000、CORSIM软件、VISSIM软件)中的延误计算模型,在不同的交通流状态下,应用此三种延误计算模型进行了单点固定控制交叉口控制周期延误的仿真计算,并采用典型的美国城市交叉口实例进行了对比分析,得出以下结论:在交叉口接近饱和状态的情况下,HCM2000的延误计算模型得到的周期延误最小,在过饱和的情况下,HCM2000计算得到的周期延误最大。

考虑交通流动态分布的路面养护最优决策

为准确评估路面全生命周期用户成本以制定最优的路面养护策略,提出基于网络交通流均衡条件的路面养护多阶段动态规划最优决策模型,通过交通流的仿真,模拟用户成本在路网养护前、后以及生命周期各阶段的动态变化,并设计启发式迭代算法。该模型最小化的目标函数包含了生命周期的养护成本、用户成本、路面资产残值,约束条件主要由交通量守恒约束条件、养护预算约束、路面性能转移约束等。为证明模型的有效性,以一个具有10个路段的路网为例,采用提出的模型制定该路网连续20年的最优养护决策方案。研究结果表明:在每年养护预算确定的情况下,全生命周期路面养护最优方案是一个决策方案组合,该最优方案可以提高养护资金使用效率并有效减少用户成本,可为路面养护管理提供决策支持。通过对不同养护约束情景下的全生命周期用户成本的模拟表明:随着养护预算的增加,养护路段增多,导致用户成本增加,使养护成本的边际收益发生递减,养护预算在理论上具有一个最优值,并且养护成本收益与养护预算之间呈倒U形函数关系。

Proactive traffic responsive control based on state-space neural network and extended Kalman filter

The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagging of the signal timing plans to traffic conditions. Utilizing the traffic conditions in current and former intervals, the network topology of the state-space neural network （SSNN）, which is derived from the geometry of urban arterial routes, is used to predict the optimal timing plan corresponding to the traffic conditions in the next time interval. In order to improve the effectiveness of the SSNN, the extended Kalman filter （EKF） is proposed to train the SSNN instead of conventional approaches. Raw traffic data of the Guangzhou Road, Nanjing and the optimal signal timing plan generated by a multi-objective optimization genetic algorithm are applied to test the performance of the proposed model. The results indicate that compared with the SSNN and the BP neural network, the proposed model can closely match the optimal timing plans in futuristic states with higher efficiency.

Passenger flow dynamic assignment model of urban mass transit and its application during interval interrupted operation

In order to find a method which can describe the passenger flow dynamical distribution of urban mass transit during interval interrupted operation,an urban railway network topology model was built based on the travel path dual graph by considering interchange,crowd and congestion.The breadth first valid travel path search algorithm is proposed,and the multipath passenger flow distribution logit model is improved.According to the characteristics of passengers under the interruption condition,the distribution rules of different types of passenger flow are proposed.The method of calculating the aggregation number of station is proposed for the case of insufficient transport capacity.Finally,the passenger flow of Beijing urban mass transit is simulated for the case study.The results show that the relative error of most of transfer passenger flow is below 10%.The proposed model and algorithm can accurately assign the daily passenger flow,which provides a theoretical basis for urban mass transit emergency management and decision.

En-route Sector Complexity Control Strategies in Air Traffic Management

Along with the rapid development of air traffic, the contradiction between conventional air traffic management(ATM)and the increasingly complex air traffic situations is more severe,which essentially reduces the operational efficiency of air transport systems. Thus,objectively measuring the air traffic situation complexity becomes a concern in the field of ATM. Most existing studies focus on air traffic complexity assessment,and rarely on the scientific guidance of complex traffic situations. According to the projected time of aircraft arriving at the target sector boundary,we formulated two control strategies to reduce the air traffic complexity. The strategy of entry time optimization was applied to the controllable flights in the adjacent upstream sectors. In contrast,the strategy of flying dynamic speed optimization was applied to the flights in the target sector. During the process of solving complexity control models,we introduced a physical programming method. We transformed the multi-objective optimization problem involving complexity and delay to single-objective optimization problems by designing different preference function. Actual data validated the two complexity control strategies can eliminate the high-complexity situations in reality. The control strategy based on the entry time optimization was more efficient than that based on the speed dynamic optimization. A basic framework for studying air traffic complexity management was preliminarily established. Our findings will help the implementation of a complexity-based ATM.

Road traffic states estimation algorithm based on matching of regional traffic attracters

To effectively solve the traffic data problems such as data invalidation in the process of the acquisition of road traffic states,a road traffic states estimation algorithm based on matching of the regional traffic attracters was proposed in this work.First of all,the road traffic running states were divided into several different modes.The concept of the regional traffic attracters of the target link was put forward for effective matching.Then,the reference sequences of characteristics of traffic running states with the contents of the target link's traffic running states and regional traffic attracters under different modes were established.In addition,the current and historical regional traffic attracters of the target link were matched through certain matching rules,and the historical traffic running states of the target link corresponding to the optimal matching were selected as the initial recovery data,which were processed with Kalman filter to obtain the final recovery data.Finally,some typical expressways in Beijing were adopted for the verification of this road traffic states estimation algorithm.The results prove that this traffic states estimation approach based on matching of the regional traffic attracters is feasible and can achieve a high accuracy.

Travel time prediction model of freeway based on gradient boosting decision tree

To investigate the travel time prediction method of the freeway, a model based on the gradient boosting decision tree (GBDT) is proposed. Eleven variables (namely, travel time in current period T i , traffic flow in current period Q i , speed in current period V i , density in current period K i , the number of vehicles in current period N i , occupancy in current period R i , traffic state parameter in current period X i , travel time in previous time period T i -1 , etc.) are selected to predict the travel time for 10 min ahead in the proposed model. Data obtained from VISSIM simulation is used to train and test the model. The results demonstrate that the prediction error of the GBDT model is smaller than those of the back propagation (BP) neural network model and the support vector machine (SVM) model. Travel time in current period T i is the most important variable among all variables in the GBDT model. The GBDT model can produce more accurate prediction results and mine the hidden nonlinear relationships deeply between variables and the predicted travel time.

Empirical Exploration of Air Traffic Control Behaviour at Terminal Maneuvering Area:From an Air Traffic Flow Aspect

In a large-volume,high-density traffic background,air traffic manifests fluid-like microscopical characteristics.The characteristics are formed by the micro tailing actions between individual aircraft.Aircraft headway refers to the time interval between successive flying aircraft in air traffic flow,which is one of the most important characteristics of air traffic flow.The variation in aircraft headway reveals the air traffic control behaviour.In this paper,we study the characteristics of air traffic control behaviours by analyzing radar tracks in a terminal maneuvering area.The headway in arrival traffic flow is measured after the determination of aircraft trailing relationships.The headway evolutionary characteristics for different control decisions and the headway evolutionary characteristics in different phase-states are discussed,and some interesting findings are gotten.This work may be helpful for scholars and managers in understanding the intrinsic nature of air traffic flow and in the development of intelligent assistant decision systems for air traffic management.

Vehicle emissions calculation for urban roads based on the Macroscopic Fundamental Diagram method and real-time traffic information

Vehicle emissions calculation methods mostly use ownership information or annual road monitoring data as the activity level to calculate air pollutant emissions,but it is hard to reflect either the emissions intensity under different conditions or the spatiotemporal characteristics in various sections based on such approaches.This paper presents a method based on the Macroscopic Fundamental Diagram and real-time traffic data to calculate vehicle emissions,which could reflect the operation conditions and emission characteristics of vehicles.Following the‘Technical Guide for the Compiling of Road Vehicle Air Pollutant Emissions Inventories’,the emissions of three roads with different lane numbers and road grades in Beijing were estimated and verified using this method.Compared with monitoring data,the average deviations of the traffic flow on the Fifth Expressway,Jingfu National Highway,and Jingzhou Provincial Highway were?25.5%,?26.5%,and?13.4%,respectively,and the average deviations of nitrogen oxides emissions were?27.7%,?12.9%,and?12%,respectively.This method showed good application potentials to construct the emissions inventory applied to the block-scale model and analyze the spatiotemporal distribution characteristics of motor vehicle emissions in urban areas.

Real-time road traffic states estimation based on kernel-KNN matching of road traffic spatial characteristics

The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work,an algorithm based on kernel-k nearest neighbor(KNN) matching of road traffic spatial characteristics is presented to estimate road traffic states. Firstly, the representative road traffic state data were extracted to establish the reference sequences of road traffic running characteristics(RSRTRC). Secondly, the spatial road traffic state data sequence was selected and the kernel function was constructed, with which the spatial road traffic data sequence could be mapped into a high dimensional feature space. Thirdly, the referenced and current spatial road traffic data sequences were extracted and the Euclidean distances in the feature space between them were obtained. Finally, the road traffic states were estimated from weighted averages of the selected k road traffic states, which corresponded to the nearest Euclidean distances. Several typical links in Beijing were adopted for case studies. The final results of the experiments show that the accuracy of this algorithm for estimating speed and volume is 95.27% and 91.32% respectively, which prove that this road traffic states estimation approach based on kernel-KNN matching of road traffic spatial characteristics is feasible and can achieve a high accuracy.

Dynamic signal control for at-grade intersections under preliminary autonomous vehicle environment

Autonomous vehicle technology will transform fundamentally urban traffic systems.To better enhance the coming era of connected and autonomous vehicles,effective control strategies that interact wisely with these intelligent vehicles for signalized at-grade intersections are indispensable.Vehicle-to-infrastructure communication technology offers unprecedented clues to reduce the delay at signalized intersections by innovative information-based control strategies.This paper proposes a new dynamic control strategy for signalized intersections with vehicle-to-signal information.The proposed strategy is called periodic vehicle holding(PVH)strategy while the traffic signal can provide information for the vehicles that are approaching an intersection.Under preliminary autonomous vehicle(PAV)environment,left-turning and through-moving vehicles will be sorted based on different information they receive.The paper shows how PVH reorganizes traffic to increase the capacity of an intersection without causing severe spillback to the upstream intersection.Results show that PVH can reduce the delay by approximately 15%at a signalized intersection under relatively high traffic demand.

Dynamics of Motorized Vehicle Flow under Mixed Traffic Circumstance

To study the dynamics of mixed traffic flow consisting of motorized and non-motorized vehicles, a carfollowing model based on the principle of collision free and cautious driving is proposed. Lateral friction and overlapping driving are introduced to describe the interactions between motorized vehicles and non-motorized vehicles. By numerical simulations, the flux-density relation, the temporal-spatial dynamics, and the velocity evolution are investigated in detail The results indicate non-motorized vehicles have a significant impact on the motorized vehicle flow and cause the maximum flux to decline by about 13%. Non-motorized vehicles can decrease the motorized vehicle velocity and cause velocity oscillation when the motorized vehicle density is low. Moreover, non-motorized vehicles show a significant damping effect on the oscillating velocity when the density is medium and high, and such an effect weakens as motorized vehicle density increases. The results also stress the necessity for separating motorized vehicles from non-motorized vehicles.

Method of the left-turning bus priority at intersections based on a variable lane

To reduce the delay of left-turning buses and improve the traffic efficiency at signalized intersections,a novel variable bus approach lane(VBAL)control method based on bus pre-signals is proposed.This method combines the variable lane with the bus priority pre-signal,and realizes the left-turning bus priority without causing great impact on other vehicles.To validate the effectiveness of the method,the VBAL scheme was compared with the single left-turn lane scheme(SLTL)and the double left-turn lane scheme(DLTL).On this basis,the delay change calculation model of left-turning buses and through vehicles were established by the cumulative curve graphic method.The influence of vehicle proportion and green split on the model was studied through sensitivity analysis.The results show that VBAL can reduce the delay of left-turning bus and the increase of through vehicle delay to the greatest extent.Finally,the scheme was applied to a real-world intersection,and the results demonstrate the effectiveness and advantage of the VBAL scheme.

城市快速路口充电站建模研究

提出了一种基于泊松分布到达模型(poisson arrival location model,PALM)和M/M/s排队理论的电动汽车充电负荷预测方法。首先对传统交通流动态模型进行修正后得到基于PALM的路网模型,详细分析了驶入或驶出城市快速路的车辆,并将驶出车辆分成永久性驶出与暂时性驶出两类,然后基于交通基本定律建立了快充站排队车辆到达模型,得到快充站的车辆到达率,从而基于M/M/s排队理论预测电动汽车的充电负荷时空分布。仿真算例表明,文章提出的方法可以预测快充站的电动汽车充电负荷,为快充站的选址定容提供重要的依据。