On the critical conditions of traffic jams

Traffic wave theory is used to study the critical conditions for traffic jams according to their features. First, the characteristics of traffic wave propagation is analyzed for the simple signal-controlled lane and the critical conditions for oversaturation is established. Then, the basic road is decomposed into a series of one-way links according to its topological characteristics. Based on the decomposition, traffic wave propagation under complex conditions is studied. Three complicated factors are considered to establish the corresponding critical conditions of jam formation, namely, dynamic and insufficient split, channelized section spillover and endogenous traffic flow. The results show that road geometric features, traffic demand structures and signal settings influence the formation and propagation of traffic congestion. These findings can serve as a theoretical basis for future network jam control.

Feedback control scheme of traffic jams based on the coupled map car-following model

Based on the pioneering work of Konishi et al. [Phys. Rev. E （1999） 60 4000], a new feedback control scheme is presented to suppress traffic jams based on the coupled map car-following model under the open boundary condition. The effect of the safe headway on the traffic system is considered. According to the control theory, the condition under which traffic jams can be suppressed is analyzed. The results are compared with the previous results concerning congestion control. The simulations show that the suppression performance of our scheme on traffic jams is better than those of the previous schemes, although all the schemes can suppress traffic jams. The simulation results are consistent with theoretical analyses.

Traffic jam in signalized road network

Traffic jam in large signalized road network presents a complex nature.In order to reveal the jam characteristics,two indexes,SVS(speed of virtual signal) and VOS(velocity of spillover),were proposed respectively.SVS described the propagation of queue within a link while VOS reflected the spillover velocity of vehicle queue.Based on the two indexes,network jam simulation was carried out on a regular signalized road network.The simulation results show that:1) The propagation of traffic congestion on a signalized road network can be classified into two stages:virtual split driven stage and flow rate driven stage.The former stage is characterized by decreasing virtual split while the latter only depends on flow rate; 2) The jam propagation rate and direction are dependent on traffic demand distribution and other network parameters.The direction with higher demand gets more chance to be jammed.Our findings can serve as the basis of the prevention of the formation and propagation of network traffic jam.

Jam Formation of Traffic Flow in Harbor Tunnel

This paper reports a study concerning occurrence and growth of traffic jam in a harbor tunnel. The single-lane with three sections （downgrade, fiat, and upgrade） is taken into account and they are characterized with different velocity limit. At the low density, the traffic current increases linearly with density and saturates at some values of immediately density. As the density increases, the traffic jam appears firstly before the upgrade section and then extends to the downgrade section. Additionally, the relationships of the velocity and headway against position in different densities are obta/ned from simulation. These results clearly clarify where and when the traffic jam appears. Finally, the critical densities are derived via the theoretical analysis before and after the discontinuous fronts and the theoretical results are consistent with the critical values of simulation results.

Characterizing Peak-Time Traffic Jam Incidents in Kampala Using Exploratory Data Analysis

Traffic congestion is associated with increased environmental pollutions, as well as reduced socio-economic productivity due to significant delays in travel times. The consequences are worse in least developed countries where motorized road transport networks are often inefficiently managed in addition to being largely underdeveloped. Recent research on traffic congestion has mostly focused on infrastructural aspects of road networks, with little or no emphasis at all on motorists’ on-the-road behavior (MB). The current study thus aimed to bridge this knowledge gap by characterizing traffic jam incidents (TJI) observed over a period of 80 days in Uganda’s Capital City, Kampala. MB as well as road network infrastructural factors such as road blockage (RB), were captured for each of the observed TJI. A total of 483 peak-time TJI were recorded, and exploratory data analysis (EDA) subsequently performed on the TJI dataset. EDA involved Hierarchical clustering analysis (HCA) and K-means clustering of the TJI dataset, as well as a detailed descriptive statistical analysis of both the entire dataset and the emerging TJI clusters. A highlight finding of this study is that 48.2% of the observed TJIs were as a result of on-the-road motorist behavior. Furthermore, the intervention of traffic police officers in a bid to regulate traffic flow was equally responsible for 25.9% of the TJIs observed in this study. Overall, these results indicate that whereas road infrastructural improvement is warranted in order to improve traffic flow, introducing interventions to address inappropriate on-the-road motorists’ behavior could alone improve traffic flow in Kampala, by over 48%. Additionally, in-order to effectively regulate traffic flow in Kampala and other least developed cities with similar traffic congestion management practices, motorists’ on-the-road behavior ought to be factored into any data-driven mechanisms deployed to regulate traffic flow and thus potentially significantly curbing traffic congestion.

Asymptotic solution of a wide moving jam to a class of higher-order viscous traffic flow models

The boundary-layer method is used to study a wide moving jam to a class of higher-order viscous models. The equations for characteristic parameters are derived to determine the asymptotic solution. The sufficient and essential conditions for the wide moving jam formation are discussed in detail, respectively, and then used to prove or disprove the existence of the wide moving jam solutions to many well-known higher-order models. It is shown that the numerical results agree with the analytical results.

Lattice models of traffic flow considering drivers' delay in response

This paper proposes two lattice traffic models by taking into account the drivers＇ delay in response. The lattice versions of the hydrodynamic model are described by the differential-difference equation and difference-difference equation, respectively. The stability conditions for the two models are obtained by using the linear stability theory. The modified KdV equation near the critical point is derived to describe the traffic jam by using the reductive perturbation method, and the kink-antikink soliton solutions related to the traffic density waves are obtained. The results show that the drivers＇ delay in sensing headway plays an important role in jamming transition.

Travel time prediction with viscoelastic traffic model

Travel time through a ring road with a total length of 80 km has been predicted by a viscoelastic traffic model(VEM), which is developed in analogous to the non-Newtonian fluid flow. The VEM expresses a traffic pressure for the unfree flow case by space headway, ensuring that the pressure can be determined by the assumption that the relevant second critical sound speed is exactly equal to the disturbance propagation speed determined by the free flow speed and the braking distance measured by the average vehicular length. The VEM assumes that the sound speed for the free flow case depends on the traffic density in some specific aspects, which ensures that it is exactly identical to the free flow speed on an empty road. To make a comparison, the open Navier-Stokes type model developed by Zhang(ZHANG, H. M. Driver memory, traffic viscosity and a viscous vehicular traffic flow model. Transp. Res. Part B, 37, 27–41(2003)) is adopted to predict the travel time through the ring road for providing the counterpart results.When the traffic free flow speed is 80 km/h, the braking distance is supposed to be 45 m,with the jam density uniquely determined by the average length of vehicles l ≈ 5.8 m. To avoid possible singular points in travel time prediction, a distinguishing period for time averaging is pre-assigned to be 7.5 minutes. It is found that the travel time increases monotonically with the initial traffic density on the ring road. Without ramp effects, for the ring road with the initial density less than the second critical density, the travel time can be simply predicted by using the equilibrium speed. However, this simpler approach is unavailable for scenarios over the second critical.

An extended master-equation approach applied to aggregation in freeway traffic

We restudy the master-equation approach applied to aggregation in a one-dimensional freeway, where the decay transition probabilities for the jump processes are reconstructed based on a car-following model. According to the reconstructed transition probabilities, the clustering behaviours and the stochastic properties of the master equation in a one-lane freeway traffic model are investigated in detail The numerical results show that the size of the clusters initially below the critical size of the unstable cluster and initially above that of the unstable cluster all enter the same stable state, which also accords with the nucleation theory and is known from the result in earlier work. Moreover, we have obtained more reasonable parameters of the master equation based on some results of cellular automata models.

Cluster-size dependent randomization traffic flow model

In order to exhibit the meta-stable states, several slow-to-start rules have been investigated as modification to Nagel-Schreckenberg （NS） model. These models can reproduce some realistic phenomena which are absent in the original NS model. But in these models, the size of cluster is still not considered as a useful parameter. In real traffic, the slow-to-start motion of a standing vehicle often depends on the degree of congestion which can be measured by the clusters＇ size. According to this idea, we propose a cluster-size dependent slow-to-start model based on the speed- dependent slow-to-start rule （VDR） model. It gives expected results through simulations. Comparing with the VDR model, our new model has a better traffic efficiency and shows richer complex characters.

Traffic Flow Merging and Bifurcating at Junction on Two-Lane Highway

In this paper we study the traffic states and jams in vehicular traffic merging and bifurcating at a junction on a two-lane highway. The two-lane traffic model for the vehicular motion at the junction is presented where a jam occurs frequently due to merging, lane changing, and bifurcating. The traffic flow is called the weaving. At the weaving section, vehicles slow down and then move aside on the other lane for changing their direction. We derive the fundamental diagrams (flow-density diagrams) for the weaving traffic flow. The traffic states vary with the density, slowdown speed, and the fraction of vehicles changing the lane. The dynamical phase transitions occur. It is shown that the fundamental diagrams depend highly on the traffic states.

基于多智能体深度强化学习的高速公路可变限速协同控制方法

面向高速公路多路段可变限速协同控制需求,针对高维参数空间高效训练寻优难题,提出了应用多智能体深度确定性策略梯度(MADDPG)算法的高速公路可变限速协同控制方法。区别于既有研究的单个智能体深度确定性策略梯度(DDPG)算法,MADDPG将每个管控单元抽象为具备Actor-Critic强化学习架构的智能体,在算法训练过程中共享各智能体的状态、动作信息,使得各智能体具备推测其余智能体控制策略的能力,进而实现多路段协同控制。基于开源仿真软件SUMO,在高速公路典型拥堵场景对提出的控制方法开展管控效果验证。实验结果表明,提出的MADDPG算法降低了拥堵持续时间和路段运行速度标准差,分别减少69.23%、47.96%,可显著提高交通效率与安全。对比单智能体DDPG算法,MADDPG可节约50%的训练时间并提高7.44%的累计回报值,多智能体算法可提升协同控制策略的优化效率。进一步,为验证智能体间共享信息的必要性,将MADDPG与独立多智能体DDPG(IDDPG)算法进行对比:相较于IDDPG,MADDPG可使拥堵持续时间、速度标准差均值的改善提升11.65%、19.00%。

基于改进DV-HOP的道路交通拥堵传感节点快速监测

由于现有交通拥堵监控设备在海量三维交通信息中处理数据的能力较差,导致监测目标延迟较高,故设计一种基于改进DV-HOP的道路交通拥堵传感节点快速监测方法;在待监测区域安置无线视觉传感器,划分子节点与Sink节点,采集车辆通行状况和整体长度;把异常道路数据作为小概率事件,确立速率采集周期及交通状态采集周期,推算历史车辆速率均值和交通数据方差,设定拥堵临界值,分析路段是否产生拥堵;对道路交通拥堵节点进行初始化,确定全部道路交通拥堵节点,并将其转换到二维坐标中;利用改进DV-HOP算法获取道路交通拥堵节点位置信息,得出道路交通拥堵节点监测结果;实验分析表明:设计方法的均等系数值可达0.998,数据传输延时仅为3.5 s,表明交通拥堵监测精度较高。

收取车辆交通拥挤费与城市交通疏堵关系研究

征收城市运营车辆停车费逐渐成为城市交通管理决策者解决交通拥堵的一项重要政策。这是以市场手段的方式调节车辆出行规律,避免车辆集中出行导致城市交通拥堵造成城市交通的外部不经济。文章将以广州某货物转运中心为例,以该区域在某些特定时段交通持续性拥堵的影响为基础,研究采取停车收费政策来调节出行者出行时间,并从停车收费水平、收费结构、收费对象以及计费的方式等几个角度对交通部门制定收费政策提出建议,以期解决该区域长期存在的特定时段交通拥堵的积瘤。

贵阳市长岭路段交通拥堵治理优化研究

贵阳市作为一个新兴二线城市,经济发展较快,但随之而来的交通拥堵问题也日趋严重。2021年,贵阳市公安交管部门对贵阳市的重点缓行路段进行了大数据分析,摸排出了贵阳市城区32个早晚高峰城市堵点,其中3个堵点就涉及长岭路路段。观山湖区长岭路作为观山湖区主干道,影响着观山湖区居民出行。本文旨在通过参考我国主要城市交通拥堵治理方法,结合贵阳市交通拥堵实际治理情况,分析观山湖区长岭路交通拥堵原因,对长岭路交通拥堵治理优化提出相应的对策。

基于交通流参数相关的阻塞流短时预测卡尔曼滤波算法

提出一种考虑交通流参数相关关系的卡尔曼滤波算法,实现阻塞流状态下道路网交通流短时预测.在交通流守恒方程的基础上,借鉴偏微分方程求解Lax-Wendroff格式离散的思想,结合阻塞流状态下交通流时间和空间特性及进出口匝道等因素的影响,建立阻塞流状态下交通流短时预测状态空间模型,并设计基于卡尔曼滤波方法的模型求解算法.最后以北京市某一区域路网为例,进行了实证性研究.研究结果表明:所建立的阻塞流状态下交通流短时预测卡尔曼滤波算法由于同时考虑了时间和空间因素,能够使预测平均绝对百分比误差(MAPE)控制在10%以内;平均MAPE仅为7.96%.相同条件下,ARIMA模型和Elman模型预测MAPE分别为19.88%和10.51%.

基于G-P算法的快速路交通流参数相空间重构

提出了一种基于混沌分析的G-P(Grassberger-procaccia algorithm)算法将非平稳交通流参数时间序列近似转化为平稳时间序列的方法。首先采用自相关函数判断自由流状态、拥挤流状态和阻塞流状态下交通流基本参数时间序列的平稳性。然后应用G-P算法计算嵌入维,进行相空间重构,给出交通流参数时间序列平稳化方法。最后利用快速路交通流实测数据,对3种状态下非平稳的交通流参数时间序列的平稳化进行验证,结果表明:本文方法能够为交通流参数分析、拟合和预测提供科学合理的输入集。

基于事件的交通拥堵模拟与消散策略研究

区域交通拥堵是通过排队链环逐级传播的,其结构类似于树结构分岔。如果不能实施及时有效的疏散控制策略,极有可能造成拥堵闭环。运用改进的中观交通仿真模型模拟基于事故的理想格子网络的交通拥堵形成和消散过程。依据交通拥堵传播的空间结构特征,利用禁止转弯(转弯禁限)与车辆途中更换路径的交通管制和诱导措施,首次提出了"树控制"策略缓解交通拥堵。仿真结果表明,合理应用提出的拥堵消散控制策略可以有效缓解交通拥堵的传播。

弹性需求下拥挤道路收费的模型与算法研究

拥挤道路收费作为交通需求管理的一种有效措施在许多国家和地区开始提倡。研究了弹性需求下的拥挤道路收费问题 ,建立了双层规划模型 ,其中上层模型以用户盈余最大化为目标 ,下层模型满足弹性需求下的随机用户平衡 ( SUE)。基于双层模型求解的复杂性 ,设计了一个基于步长加速法和惩罚函数法的启发式算法 ,实例计算表明该模型与算法是有效的。

发展北京地下物流系统初探

城市地下物流系统对于改善由货运卡车引起的交通堵塞、空气污染和交通事故具有特别重要意义。文章阐述了地下物流的概念以及该领域的研究动态,分析了北京交通拥堵的现状,说明了北京发展地下物流系统的必要性,提出了北京发展地下物流系统在未来的定位和要求。在此基础上,指出了目前地下物流系统在北京的可能应用领域,并进行了相关的概念性分析和探讨。