Dynamic Characteristics Of Traffic Flow Research Articles

Impacts of connected autonomous vehicle platoon breakdown on highway

Connected Autonomous Vehicles (CAVs) are seen as an opportunity to improve traffic efficiency and safety. However, further studies are needed to prove such positive effects. Particularly, there is a lack of quantitative research on the impacts of CAV breakdown in mixed traffic flow conditions (i.e., CAVs and Human Driven Vehicles, HDVs). The aim of this research is to explore the impacts generated by the breakdown of the leading vehicle of a CAV platoon in mixed traffic conditions, which in turn generates CAV platoon breakdown (i.e., platoon dispersion), by considering several CAV market penetration rates (MPRs) and platoon size. In this perspective, a control algorithm (“avoidance algorithm”) for modelling CAVs and HDVs behaviour to avoid obstacles (i.e., breakdown CAV platoon) has been developed and tested on some simulation scenarios in order to derive key traffic flow parameters. The dynamic characteristics of traffic flow with CAV platoon breakdown have been explored for both low and high traffic flow volumes. Finally, potential conflicts, congestions, as well as energy consumption and CO2 emissions resulting from the breakdown of CAV platoons in mixed traffic streams have been assessed and discussed. Results suggest that (1) CAV platoon breakdown can reduce the traffic capacity by about 20 %; (2) higher CAV MPRs are more suitable for enhancing highway safety even in breakdown conditions, decreasing energy consumption, and reducing CO2 emissions; (3) platoon size should be limited to 4, since larger sizes affect traffic safety, increases vehicle average delay time as well as energy consumption. The obtained results provide useful insights for transportation planners and transport infrastructure management companies to design and apply policies aimed at improving driving conditions, traffic quality, and safety on highways.

STBGRN: A Traffic Prediction Model Based on Spatiotemporal Bidirectional Gated Recurrent Units and Graph Convolutional Residual Networks

With the development of society and the advancement of urbanization, the development of intelligent transportation system has attracted much attention. Efficient and accurate traffic flow prediction is one of the core tasks in the research of intelligent transportation system. Most of the existing spatiotemporal traffic flow prediction models do not make full use of various periodic spatiotemporal dynamic characteristics of traffic flow, and it is difficult to effectively capture the complex spatiotemporal changes of traffic flow. To accurately predict the traffic flow of complex urban network, this paper proposes a traffic prediction model (STBGRN) based on spatiotemporal bidirectional gated cycle unit (ST-BiGRU) and graph convolution residual network (GCN-ResNet). The spatiotemporal information in traffic data is learned using the spatiotemporal bidirectional GRU coupled with the forward and backward information of the current time step, and the topology structure of the traffic network is captured by the graph convolution residual network, which is combined to complete the prediction task of the urban traffic network. In this paper, STBGRN was tested on two real data sets, SZ-taxi and Los-loop, and compared with other baseline methods, the RMSE index reached 3.857 and 5.461, and the MAE index reached 2.702 and 3.781, respectively. Accuracy index is 72.66% and 91.35%, R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document} index is 0.858 and 0.845, var index is 0.858 and 0.849. The experimental results show that STBGRN model can obtain spatiotemporal dependence from complex traffic data, and can be used in spatiotemporal traffic data processing.

STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction

As an important component of intelligent transportation-management systems, accurate traffic-parameter prediction can help traffic-management departments to conduct effective traffic management. Due to the nonlinearity, complexity, and dynamism of highway-traffic data, traffic-flow prediction is still a challenging issue. Currently, most spatial–temporal traffic-flow-prediction models adopt fixed-structure time convolutional and graph convolutional models, which lack the ability to capture the dynamic characteristics of traffic flow. To address this issue, this paper proposes a spatial–temporal prediction model that can capture the dynamic spatial–temporal characteristics of traffic flow, named the spatial–temporal self-attention graph convolutional network (STA-GCN). In terms of feature engineering, we used the time cosine decomposition and one-hot encoding methods to capture the periodicity and heterogeneity of traffic-flow changes. Additionally, in order to build the model, self-attention mechanisms were incorporated into the spatial–temporal convolution to capture the spatial–temporal dynamic characteristics of traffic flow. The experimental results indicate that the performance of the proposed model on two traffic-volume datasets is superior to those of several baseline models. In particular, in long-term prediction, the prediction error can be reduced by over 5%. Further, the interpretability and robustness of the prediction model are addressed by considering the spatial dynamic changes.

Social optimum for linear staggered shifts in a single-entry traffic corridor with no late arrivals

The staggered shifts scheme has been widely discussed to alleviate traffic congestion in recent years. However, most previous research considers limited numbers of work start times and ignores the dynamic characteristics of traffic flow. In this paper, we consider a linear staggered shifts (LSS) scheme, which assumes the work start times for commuters is a continuous time period, to replace the identical work start time in a single-entry traffic corridor. We analytically derive the social optimal (SO) assignment and find the optimal LSS scheme in the flow congestion model with no late arrivals. The evolvement laws of traffic flow in the time–space dimension and corresponding economic properties with the optimal LSS scheme are also investigated. Results demonstrate that the application of the proposed optimal LSS scheme will not change the flow pattern of commuters in SO but significantly reduce the total trip cost.

An iterative learning identification strategy for nonlinear macroscopic traffic flow model

The parameters of traffic flow model are important to the management and control of urban road network. In order to more accurately describe the actual operation of traffic flow in urban road network, this paper proposes a nonlinear macroscopic traffic flow model containing unknown time-varying multi-parameter on the basis of the steady-state and dynamic characteristics of traffic flow, and designs a time-varying multi-parameter iterative learning identification strategy by using the inherent repetitive characteristics of traffic flow. In the finite time interval, the iterative learning identification strategy is used to transform the parameter identification problem into the optimal tracking control problem, so that the number of queuing vehicles at each entrance of intersection tends to the true value, thus improving the accuracy of the model. Finally, the convergence of the algorithm is proved by a strict theoretical derivation, and the effectiveness of the method is further verified by simulation experiments using the model-based control method.

Density waves in an improved car-following model under intelligent transportation system environment

The intelligent transportation system (ITS) can effectively utilize the existing transportation facilities and improve traffic safety and efficiency. To study and evaluate the dynamic characteristics of traffic flow, a novel car-following model is proposed based on the two-velocity difference model (TVDM) with connected consideration of headway memory and backward looking effect under ITS environment. The linear stability theory is used to acquire the stability condition of the improved model, which shows that the proposed model can effectively strengthen the traffic flow stability. According to the nonlinear theory, the time-dependent Ginzburg–Landau (TDGL) equation and modified Korteweg–de Vries (mKdV) equation are obtained to exhibit the evolution characteristics of traffic flow density wave. At last, numerical simulation is performed to further validate that the improved model can enhance the traffic flow stability and eliminate traffic congestion.

An improved car-following model considering the preceding vehicle’s velocity feedback and desired inter-vehicle distance

In this paper, an improved car-following model is raised by taking the preceding vehicle’s velocity feedback and desired inter-vehicle distance into account. The asymptotic and local stability criteria are obtained via control theory method to analyze the effect of the preceding vehicle’s velocity feedback and desired inter-vehicle distance on dynamic characteristics of traffic flow. In addition, numerical simulations are constructed on vehicle starting and traffic flow evolution processes, which show that the proposed car-following model can better present the characteristics of traffic flow. The preceding vehicle’s velocity feedback and desired inter-vehicle distance play a vital role on vehicle starting process and the stability of traffic flow.

Dynamic platoon dispersion model based on real‐time link travel time

Adaptive signal control system aims to improve the operation efficiency of intersections, in which the platoon dispersion model is one of the key component modules. The problem of the traditional Robertson's model is that they are based on historical data, which cannot effectively capture the dynamic characteristics of traffic flow and be further applied to adaptive signal control systems. Thanks to the unprecedented availability of real-time travel time data provided by the modern intelligent transportation systems, there is an opportunity to calibrate Robertson's model online using real-time travel time data. This study proposes a dynamic Robertson's platoon dispersion model based on real-time travel times. Then, based on field observations, the prediction error and computational efficiency of the traditional Robertson's model, the Gaussian model, and the proposed model are compared and analysed. The results show that the proposed model can better capture the law of dynamic dispersion for traffic flow. The reduced average mean square error of prediction can be as much as 16.86 and 7.07% compared with the traditional Robertson's model and the Gaussian model, respectively. In addition, the average computational time of the proposed model is 0.46 s, which can be applied in the dynamic predictive signal control systems.

Empirical Approximation for the Stochastic Fundamental Diagram of Traffic Flow on Signalized Intersection

The wide scattering nature of the fundamental diagram (FD) with observed flow-density data may be associated with the dynamical traffic flow process, especially on signalized intersection. To describe the uncertainty of FD, in this work we established stochastic fundamental diagram (SFD) which is defined by the distributions of shockwave speed. Our approach is based on a two-level stochastic process of the traffic flow system in terms of the dynamics of traffic density and state mode associated with signal phases which is named switching linear dynamical systems (SLDS). Then, variational Bayesian learning method is adopted to compute the distributions of SFD parameter to approximate the experimental distributions of shockwave calculated by the observed flow-density data. Given traffic flow data from the NGSIM program, the verification result demonstrated that the SFD can be more helpful to capture the main features of the observed widely scattering of the flow-density data compared with FD. With the shockwave speed sampled from the SFD, the SLDS could describe the dynamic characteristics of traffic flow and be applied to the maximum likelihood estimation of traffic density or flow rate. Because it is simple and automatically calculated, the SFD provides an alternative description for fundamental diagram and its uncertainty in the traffic flow.

User equilibrium of a single-entry traffic corridor with continuous scheduling preference

ABSTRACT The flow congestion model provides a realistic form of depicting congestion by considering the spatial dynamic characteristics of traffic flow. For reducing the complication of analysis, previous studies adopted the α − β − γ preference of travel time, arrival early and late penalties. This handling method destroys the continuity of user equilibrium (UE) inflow rate as pointed out by Li and Huang (2017). In this paper, we investigate the single-entry traffic corridor with continuous scheduling preference (CSP) and develop a customized method to find the computational solution of UE flow pattern. Analytical and numerical results show that the introduction of CSP lets inflow rate of early arrivals first increase then decrease. This extends the knowledge given by classical bottleneck model and flow congestion model with α − β − γ preference. Another finding is that, even though the introduction of CSP can smooth inflow rate at departure time of the punctual commuter and make the UE flow pattern more stable, a series of shock waves still exist.

Dynamic characteristics of traffic flow with consideration of crossing pedestrians’ behavior at a nonsignalized T-shaped intersection

In this paper, we present a cellular automata model to depict the traffic characteristics at such a nonsignalized [Formula: see text]-shaped intersection in the presence of pedestrian crossings. The vehicular traffic flow characteristics with different parameters are studied. Throughput, phase diagrams, space-time and density profiles are built to point various traffic states and the phase transition features. The principal finding of the simulation are as follows: (1) The presence of crossing pedestrians has a significant influence on the vehicular traffic and the system performance, (2) The intersection capacity has a close relevance with the state of the weaving section. The smaller the interactions are at the weaving section, the higher the capacity and (3) In order to have a higher system ability, the crosswalk location should not be far from the yield line of the weaving section.

The effects of vehicular gap changes with memory on traffic flow in cooperative adaptive cruise control strategy

To evaluate the impacts of new influence factor in cooperative adaptive cruise control strategy on the dynamic characteristics of traffic flow, an improved cooperative car-following model considering multiple vehicular gap changes with memory is developed to study the influences of multiple vehicular gap changes with memory on each car’s speed, acceleration and relative distance. Some numerical simulations are carried out and the results show that considering multiple vehicular gap changes with memory in designing the cooperative adaptive cruise control strategy can improve the stability of traffic flow and reduce the accidental probability.

Analysis of evolution mechanism of traffic energy dissipation by considering driver’s forecast effect

Starting from the full velocity difference model, an extended car-following model is proposed by considering the influence that in real traffic the driver’s forecast has an effect on car-following behavior of traffic flow. The mechanism how the stability and energy dissipation of traffic flow are in fluenced by the driver’s forecast effect is revealed by the application of the proposed new model. The linear stability condition of the new model is derived theoretically through linear stability theory. The phase diagram of linear stability condition is divided into two regions by each stability curve: the stable and unstable regions. And the corresponding stable region will be enlarged with the increase of driver’s forecast time, hence the traffic condition will be improved through considering driver’s forecast effect. By numerical simulation method, the space-time evolution relation between the velocity and headway of vehicles in car-following queue is investigated systematically under the influence of driver’s forecast. In the same time, the evolution mechanisms of the overall average energy dissipation of traffic flow and individual vehicle energy consumption with the addition of small disturbance are discussed explicitly under a periodic condition, and it is discovered that the overall average energy consumption in traffic flow and the energy dissipation of individual vehicle is accompanied by a complex critical phase transition process. Good agreement between the numerical simulation and the theoretical analysis show that by considering of driver’s forecast effect, not only the stability of traffic flow is enhanced obviously, but the energy consumption is reduced remarkably as we expect. Furthermore, it is verified that both the overall average energy consumption of the considered traffic flow and the energy consumption of an individual vehicle are reduced gradually along with the increase of driver’s forecast time. On the other hand, numerical simulation results verify that the shortcoming of negative speed appearing in the full velocity difference model with low reaction coefficient can be effectively avoided by increasing the driver’s forecast time in the improved model, which means that the dynamic characteristics of traffic flow can be described more precisely by the proposed model.

Revisiting Jiang’s dynamic continuum model for urban cities

Jiang et al. (Jiang, Y.Q., Wong, S.C., Ho, H.W., Zhang, P., Liu, R.X., Sumalee, A., 2011. A dynamic traffic assignment model for a continuum transportation system. Transportation Research Part B 45 (2), 343–363) proposed a predictive continuum dynamic user-optimaDUO-l to investigate the dynamic characteristics of traffic flow and the corresponding route-choice behavior of travelers. Their modeled region is a dense urban city that is arbitrary in shape and has a single central business district (CBD). However, we argue that the model is not well posed due to an inconsistency in the route-choice strategy under certain conditions. To overcome this inconsistency, we revisit the PDUO-C problem, and construct an improved path-choice strategy. The improved model consists of a conservation law to govern the density, in which the flow direction is determined by the improved path-choice strategy, and a Hamilton–Jacobi equation to compute the total travel cost. The simultaneous satisfaction of both equations can be treated as a fixed-point problem. A self-adaptive method of successive averages (MSA) is proposed to solve this fixed-point problem. This method can automatically determine the optimal MSA step size using the least squares approach. Numerical examples are used to demonstrate the effectiveness of the model and the solution algorithm.

Dynamic characteristics of traffic flow with consideration of pedestrians’ road-crossing behavior

Pedestrians’ road-crossing behavior can often interrupt traffic flow and cause vehicle queueing. In this paper, we propose some moving rules for modeling the interaction of vehicles and pedestrians. The modified visual angle car-following model is presented for the movement of vehicles with consideration of the lateral effect of waiting pedestrians. The pedestrians’ behavior is summarized as consisting of three steps: pedestrian arrival, gap acceptance, and pedestrian crossing. Some characteristic parameters of pedestrians are introduced to characterize pedestrians’ behavior. Simulation results show that the interaction of vehicles and pedestrians lowers the traffic capacity and increases delays to both vehicles and pedestrians.

A dynamic traffic assignment model for a continuum transportation system

A predictive continuum dynamic user-optimal (PDUO-C) model is formulated in this study to investigate the dynamic characteristics of traffic flow and the corresponding route-choice behavior of travelers within a region with a dense urban road network. The modeled region is arbitrary in shape with a single central business district (CBD) and travelers continuously distributed over the region. Within this region, the road network is represented as a continuum and travelers patronize a two-dimensional continuum transportation system to travel to the CBD. The PDUO-C model is solved by a promising solution algorithm that includes elements of the finite volume method (FVM), the finite element method (FEM), and the explicit total variation diminishing Runge–Kutta (TVD-RK) time-stepping method. A numerical example is given to demonstrate the utility of the proposed model and the effectiveness of the solution algorithm in solving this PDUO-C problem.