Dong Ngoduy

Multiclass first-order traffic model using stochastic fundamental diagrams

The first-order continuum traffic model has been extensively studied in the state-of-the-art of traffic flow theory due to its simplicity and capability to represent many real traffic problems, such as a shock-wave formation. Furthermore, recent extension of the first-order model to multiclass traffic dynamics has revealed some interesting non-linear traffic phenomena such as hysteresis and capacity drop. However, most of the existing first-order continuum models do not display the widely scattered flow–density relationship. We argue in this article that the widely scattered flow–density relationship might be caused by the random variations in driving behaviour. It is shown that both of the hysteresis transitions and the wide scattering can be reproduced by a multiclass first-order model with a stochastic setting in the model parameters. The simulation results support our findings and are in good agreement with the real data.

Multiclass first-order modelling of traffic networks using discontinuous flow-density relationships

Recently, the modelling of heterogeneous traffic flow has gained significant attention from traffic theorists. The influence of slow vehicles (e.g. trucks) on traffic operations has been studied both from a micro and macroscopic level. Though multiclass traffic models have been successfully developed in literature, few of them are adequately used to describe traffic network operations. To this end, this article aims to propose a model to study the (heterogeneous) traffic network operations based on the macroscopic modelling approach. More specifically, on the one hand, we introduce an extension of the classic Lighthill–Whitham–Richards model to describe multiclass traffic operations in the network. The proposed model is based on solving a system of hyperbolic partial differential equations describing multiclass traffic dynamics with discontinuous fluxes. On the other hand, a dynamic routing algorithm is applied to determine the turning flow at nodes based on the information provision of the current network situation. Numerical results have shown that the proposed model can capture some real traffic phenomena in multiclass traffic networks.

Application of gas-kinetic theory to modelling mixed traffic of manual and ACC vehicles

In this article, we propose an extended multiclass gas-kinetic theory applicable to mixed traffic flow of manual and adaptive cruise control (ACC) vehicles. In the model, the acceleration/deceleration of ACC vehicles is specified explicitly using microscopic models. The macroscopic multiclass traffic equations are obtained from the proposed gas-kinetic model using the well-known method of moments. The influencing conditions to traffic flow stability with respect to a small perturbation are derived by the linear stability method. The analytical results show that ACC vehicles contribute to the improved stabilisation of traffic flow. The numerical simulations of our developed multiclass macroscopic model on a circular freeway support our analytical findings and indicate that increasing the penetration of ACC vehicles results in more stable traffic flow. Simulations of merging flows at an on-ramp in an open freeway are carried out to describe the effects of the penetration of ACC vehicles on the bottleneck capacity. It is found that around 30% ACC vehicles in traffic flow leads to significantly increased capacity and reduced travel time. We argue that, together with other microscopic models, our model provides a wider picture of the effects of ACC vehicles on traffic flow characteristics.

Platoon-based macroscopic model for intelligent traffic flow

This paper proposes a macroscopic model to describe the dynamics of intelligent traffic flow where intelligent vehicles are driving closer to each other than manual vehicles and operating in a form of many platoons each of which contains several vehicles. The model is developed from a car-following model which allows us to obtain well tractable macroscopic equations for such platoon-based traffic operation. A linear stability diagram is constructed from the developed model based on the linear stability method for a certain model parameter set. It is found analytically that platoon-based driving behaviour of intelligent vehicles enhances the stabilisation of traffic flow with respect to a small perturbation. Numerical simulation of an open freeway with an on-ramp bottleneck supports our analytical results. We have argued that the newly developed macroscopic model will provide a better insight into the dynamics of intelligent traffic flow.

DERIVATION OF CONTINUUM TRAFFIC MODEL FOR WEAVING SECTIONS ON FREEWAYS

This paper presents a new continuum model describing the dynamics of multiclass traffic flow on multilane freeways including weaving sections. In this paper, we consider a specific freeway weaving type, which is formed when an on ramp is near to an off ramp and these two ramps are joined by an auxiliary lane. Traffic interactions in this weaving zone are very complex due to the involvement of weaving flows and non-weaving flows in the so-called mandatory lane-changing process. To handle this complexity, it is essential to have a good understanding of the (microscopic) driving behavior within the weaving zones. These behaviors are modeled based on a gap-acceptance model. The methodology to obtain a weaving continuum traffic model is thus twofold. On the one hand, we develop a (macroscopic) model to determine the mandatory lane-changing probability based on a renewal process. On the other hand, we implement the lane-changing model into a current gas-kinetic traffic flow model for heterogeneous traffic flow on multilane roadways. From this, corresponding macroscopic model is obtained based on the method of moments.

Multianticipative Nonlocal Macroscopic Traffic Model

Multianticipative driving behavior, where a vehicle reacts to many vehicles in front, has been exten- sively studied and modeled using a car-following (i.e., microscopic) approach. A lot of effort has been under- taken to model such multianticipative driving behav- ior using a macroscopic approach, which is useful for real-time prediction and control applications due to its fast computational demand. However, these macroscopic models have increasingly failed with an increased num- ber of anticipations. To this end, this article puts for- ward derivation of an improved macroscopic model for multianticipative driving behavior using a modified gas- kinetic approach. First, the basic (microscopic) gener- alized force model, which has been claimed to fit well with real traffic data, is chosen for the derivation. Sec- ond, the derivation method relaxes the condition that de- celeration happens instantaneously. Theoretical analysis and numerical simulations of the model are carried out to show the improved performance of the derived model over the existing (multianticipative) macroscopic models.

The two-regime transmission model for network loading in dynamic traffic assignment problems

Dynamic network loading (DNL) model is concerned with moving traffic in space and time along road network links in dynamic traffic assignment (DTA) models. DNL models strive to build in traffic realism such as modelling transient queues and spillback to upstream links, yet they need to remain computable. Most models in the literature are skewed towards either realism or computability and thus leave a wide scope for further research in arriving at a balanced model. This research proposes a new DNL model called the Two-regime transmission model (TTM) based on widely accepted first-order traffic flow theory. The TTM aims to be quick and accurate enough for planning purposes, when embedded into the framework of a DTA. The TTM considers the time-dependent density states of network links over two regimes namely, free-flowing and congested regimes, and dynamically models the time-dependent queue length, but without the need to break the link into cells. This article sets out the theoretical background necessary for developing the TTM and it also illustrates the principles with the help of a simple network serving a single OD pair. Although the numerical tests are only preliminary indicators, the TTM has been found to produce promising results, for example producing results that are apparently closer than the cell transmission model (CTM) to predicting the dissipation and formation of a queue in a homogeneous link for the same level of time discretisation. We believe that our work establishes TTM as a candidate worthy of future exploration, especially for representing plausible, first-order traffic dynamics within a dynamic user equilibrium model with a lower number of variables/side-constraints than the CTM.

Kernel Smoothing Method Applicable to the Dynamic Calibration of Traffic Flow Models

: Real time traffic flow simulation models are used to provide traffic information for dynamic traffic management systems. Those simulation models are supplied by traffic data in order to estimate and predict traffic conditions in unobserved sections of a traffic network. In general, most of recent real time traffic simulators are based on the macroscopic model because the macroscopic model replicates the average traffic behavior in terms of observable variables such as (time–space) flow and speed at a relatively fast computational time. Like other simulation models, an important aspect of the real time macroscopic simulator is to calibrate the model parameters online. The most conventional way of the online calibration is to add a random walk to the parameters to constitute an augmentation of the traffic variables and the model parameters to be estimated. Actually, this method allows the parameters to vary at every time step and, therefore, describes the adaptation of the model to the prevailing traffic conditions. However, it has been reported that the use of the random walk results in a loss of information and an increase of the covariance of parameters, which consequently leads to posteriors that are far more diffuse than the theoretical posteriors for the true parameters. To this end, this article puts forward a Kernel density estimation technique in the calibration process to handle the covariance issue and to avoid the information loss. The Kernel density estimation technique is embedded in the particle filter algorithm, which is extended to the calibration problems. The proposed framework is investigated using real-life data collected in a freeway in England.

Effect of the car-following combinations on the instability of heterogeneous traffic flow

Heterogeneous traffic flow contains the mixed operation of the different vehicle classes such as cars, trucks and buses. Previous study has indicated that the overall stability of the heterogeneous traffic flow is governed by a weighted mean of stability parameters corresponding to each vehicle class and is not affected by the order in which a vehicle class is put into the system, for example either car or truck. However, it has been recently reported that the effect of mixed composition on traffic flow dynamics is also determined by the car-following vehicle type, that is either car following a car or car following a truck. This paper aims at investigating analytically how different car-following combinations (i.e. the following type) influence the overall stability of heterogeneous traffic flow dynamics. More specifically, we will consider the four types of car-following behaviour such as car–car, car–truck, truck–car, and truck–truck. The effect of such car-following types on the overall linear stability condition of the heterogeneous traffic flow will be studied using a generalised multi-class car-following model in which the optimal speed function is not only dependent on the the space headway between two consecutive vehicles as in previous study of Mason and Woods [1997. “Car-following Model of Multispecies Systems of Road Traffic.” Physical Review E: 2203–2214] but also on the and the relative speed between those two consecutive vehicles.

Macroscopic effects of reaction time on traffic flow characteristics

Reaction time is defined as a physiological parameter reflecting the period of time between perceiving a stimulus and performing a relevant action. In the traffic flow theory literature, the effects of reaction time on string stability have been described using the microscopic modeling approach. This paper presents a distinct approach to investigate how reaction time influences traffic flow stability using a macroscopic model. In the paper, the distinction between string stability and flow stability is defined. The flow stability conditions are derived based on the macroscopic model, which is developed from a gas-kinetic principle. From linear analysis, we find that at macroscopic scale the reaction time influences how instabilities propagate but does not contribute to whether those (linear) instabilities occur. Nevertheless, nonlinear analysis might give a different view on the impact of reaction time on traffic flow stability, but the effect is nonlinear. We argue that the findings provide a better understanding of the effects of reaction time on traffic flow characteristics.