A dynamical systems approach based on averaging to model the macroscopic flow of freeway traffic

The flow of traffic exhibits distinct characteristics under different conditions, reflecting the congestion during peak hours and relatively free motion during off-peak hours. This requires one to use different mathematical equations to describe the diverse traffic characteristics. Thus, the flow of traffic is best described by a hybrid system, namely different governing equations for the different regimes of response, and it is such a hybrid approach that is investigated in this paper. Existing models for the flow of traffic treat traffic as a continuum or employ techniques similar to those used in the kinetic theory of gases, neither of these approaches gainfully exploit the hybrid nature of the problem. Spurious two-way propagation of disturbances that are physically unacceptable are predicted by continuum models for the flow of traffic. The number of vehicles in a typical section of the highway does not justify its being modeled as a continuum. It is also important to recognize that the basic premises of kinetic theory are not appropriate for the flow of traffic (see [S. Darbha, K.R. Rajagopal, Limit of a collection of dynamical systems: an application to modeling the flow of traffic, Mathematical Models and Methods in Applied Sciences 12 (10) (2002) 1381–1399] for a rationale for the same). A model for the flow of traffic that does not treat traffic as a continuum or use notions from kinetic theory is developed here and corroborated with real-time data collected on US 183 in Austin, Texas. Predictions based on the hybrid system model seem to agree reasonably well with the data collected on US 183.     

An extended multi-anticipative delay model of traffic flow

An extended multi-anticipative delay model is proposed by introducing multiple velocity differences and incorporating the reaction-time delay of drivers. The stability condition of the new model is obtained by applying the linear stability theory, and the modified Korteweg–de Vries (mKdV) equation is derived by the use of the nonlinear analysis method. The analytical and numerical results show that both the reaction-time delay of drivers and the information of multiple velocity differences have an important influence on the stability of the model, and that the stabilization of traffic flow is enhanced by appending the velocity difference information of multiple vehicles ahead or by decreasing the delay time.     

Analysis of traffic flow by the finite element method

A finite elernent methodology is developed for the numerical solution of traffic flow problems encountered in arterial streets. The simple continuum traffic flow model consisting of the equation of continuity and an equilibrium flow-density relationship is adopted. A Galerkin type finite element method is used to formulate the problem in discrete form and the solution is obtained by a step-by-step time integration in conjunction with the Newton-Raphson method. The proposed finite element methodology, which is of the shock capturing type, is applied to flow traffic problems. Two numerical examples illustrate the method and demonstrate its advantages over other analytical or numerical techniques.     

Modeling the asymmetry in traffic flow (a): Microscopic approach

The asymmetric characteristic of a vehicle’s ability in deceleration and acceleration, as well as its impact to micro- and macroscopic traffic flow has caused increased attention from both theoretical and practical sides. However, how to realistically model this property remains a challenge to researchers. This paper is one of the two studies on this topic, which is focused on the modeling at the microscopic level from the investigation of car-following behavior. The second part of the study [H. Liu, H. Xu, H. Gong, Modeling the asymmetry in traffic flow (b): macroscopic approach, Appl. Math. Model. (submitted for publication)] is focused on the modeling of this asymmetric property from the macroscopic scale. In this paper, we first present an asymmetric full velocity difference car-following approach, in which a higher order differential equation is developed to take into account the effect of asymmetric acceleration and deceleration in car-following. Then, efforts are dedicated to calibrate the sensitivity coefficients from field data to complete the theoretical approach. Using the data recorded from the main lane traffic and ramp traffic of a segment of the US101 freeway, the two sensitivity coefficients have been successfully calibrated from both congested and light traffic environments. The experimental study reveals that in the studied traffic flow, the intensity of positive velocity difference term is significantly higher than the negative velocity difference term, which agrees well with the results from studies on vehicle mechanics.     

Simulating vehicular traffic in a network using dynamic routing

We present a new numerical code which solves the Lighthill – Whitham model, the classic macroscopic model for vehicular traffic flow, in a network with multi-destinations. We use a high-resolution shock-capturing scheme with approximate Riemann solver to solve the partial differential equations of the Lighthill – Whitham theory. These schemes are very efficient, robust and moreover well adapted to simulations of traffic flows. We develop a theory of dynamic routing including a procedure for traffic flow assignment at junctions which reproduces the correct propagation of irregularities and ensures at the same time conservation of the number of vehicles.     

A new lattice model of traffic flow with the consideration of individual difference of anticipation driving behavior

In this paper, we propose a new lattice model of traffic flow with the consideration of individual difference of anticipation driving behavior. The linear stability condition and the mKdV equation are derived from linear stability analysis and nonlinear analysis, respectively. Furthermore, numerical simulation shows that the anticipation driving behavior can increase the cell number of low density, which means that more cars can run freely and traffic congestion can be suppressed efficiently by taking the anticipation driving behavior into account in lattice model. Moreover, with the coefficient of the anticipation driving behavior increasing, the low density region turns wide corresponding to individual difference of anticipation driving behavior.     

A hybrid macroscopic-based model for traffic flow in road networks

Recently, the unquestionable growth of interest to increase the operational efficiency and capability of transportation systems led to the development of a large number of traffic modeling theories. One of the major operational issues when developing a transportation system management model lies in the selection of the appropriate methodological approach with respect to several decisions, such as the selection of the type of input and output data as well as the qualitative representation and the computational power of the model. Despite the considerable effort in the area, there is still not an approach which per se models effectively the various dynamically evolving features of traffic in road networks. The present paper addresses this issue by introducing a new hybrid approach which combines the complementary features and capabilities of both continuum mathematical models e.g. 1, 6, 23 and 26 and knowledge-based models e.g. 7, 22 and 28 in order to describe effectively traffic flow in road networks.     

Modeling the asymmetry in traffic flow (b): Macroscopic approach

In [H. Xu, H. Liu, H. Gong, Modeling the asymmetry in traffic flow (a): microscopic approach, J. Appl. Math. Model. (submitted for publication)], the asymmetric characteristic of traffic flow has been studied from a microscopic approach through the modeling of car-following behavior. This paper further discusses the asymmetric traffic flow modeling at the macroscopic scale. The microscopic asymmetric full velocity difference model is extended to a continuum traffic flow model to study the anisotropic characteristic and diffusive influence under various traffic conditions. In order to accurately solve the mathematical problem, a weighted essentially no-oscillatory (WENO) approach is applied. The performance of the model is then demonstrated through thorough evaluation against select classic models and field data. The macroscopic model is the first of its kind that is directly developed from an asymmetric car-following approach. The results show that the model is able to present many complex traffic phenomena observed in the field such as shock waves, rarefaction waves, stop-and-go waves and local cluster effects at a better level of accuracy than most of the existing models.     

An efficient algorithm for computing traffic equilibria using TRANSYT model

Computing traffic equilibria with signal settings using TRANSYT model for an area traffic control road system is considered in this paper. Following Wardrop’s first principle, this problem can be formulated as a variational inequality problem. In this paper, we propose a novel algorithm to efficiently solve this equilibrium traffic assignment with global convergence. Numerical calculations are conducted on a grid-size road network. As it shows, the proposed method achieved greater savings in computational overheads than did those conventional methods for solving traffic equilibria when signal settings are particularly taken into account.     

On the derivation of the velocity and fundamental traffic flow diagram from the modelling of the vehicle–driver behaviors

This paper deals with derivation of the fundamental diagram by modelling the individual driver behavior that adjusts the velocity to the density of vehicles in order to respect the braking distance. A parameter is properly introduced to model the quality of the driver–vehicle subsystem referred to the environmental conditions. Subsequently, it is shown how to use this result in order to model traffic flows by the macroscopic representation and by the kinetic theory.     

Binary integer modeling of the traffic flow optimization problem,in the case of an autonomous transportation system

Our research aimed to optimize the transportation processes through the binary integer modeling of cooperative vehicle control by linking the dynamic traffic assignment approach and controlling the autonomous transport system. Our paper’s main contribution is a model transforming the optimal vehicle control problem into binary integer formulation, optimizing transport processes at the system level, and representing safety and dynamics related constraints on the vehicle level. Two small numerical case studies have illustrated the applicability and effectiveness of the model.     

Some general models of traffic flow in an isolated network

Starting form basic principles, we obtain mathematical models that describe the traffic of material objects in a network represented by a graph. We analyze existence, uniqueness, and positivity of solutions for some implicit models. Also, some linear models and their equilibria are analyzed. Copyright © 2017 John Wiley & Sons, Ltd.     

Sharp critical thresholds for a class of nonlocal traffic flow models

We study a class of traffic flow models with nonlocal look-ahead interactions. The global regularity of solutions depend on the initial data. We obtain sharp critical threshold conditions that distinguish the initial data into a trichotomy: subcritical initial conditions lead to global smooth solutions, while two types of supercritical initial conditions lead to two kinds of finite time shock formations. The existence of non-trivial subcritical initial data indicates that the nonlocal look-ahead interactions can help avoid shock formations, and hence prevent the creation of traffic jams.     

On air traffic flow management with rerouting. Part I: Deterministic case

In this paper a deterministic mixed 0-1 model for the air traffic flow management problem is presented. The model allows for flight cancelation and rerouting, if necessary. It considers several types of objective functions to minimize, namely, the number of flights exceeding a given time delay (that can be zero), separable and non-separable ground holding and air delay costs, penalization of alternative routes to the scheduled one for each flight, time unit delay cost to arrive to the nodes (i.e., air sectors and airports) and penalization for advancing arrival to the nodes over the schedule. The arrival and departure capacity at the airports is obviously considered, as well as the capacity of the different sectors in the airspace, being allowed to vary along the time horizon. So, the model is aimed to help for better decision-making regarding the ground holding and air delays imposed on flights in an air network, on a short term policy for a given time horizon. It is so strong that there is no additional cut appending, nor does it require the execution of the branch-and-bound phase to obtain the optimal solution for the problem in many cases of the testbeds with which we have experimented. In the other cases, the help of the cut identifying and heuristic schemes of the state-of-the art optimization engine of choice is required in order to obtain the solution of the problem, and the branch-and-bound phase is not required either. An extensive computational experience is reported for large-scale instances, some of which have been taken from the literature and some others were coming from industry.     

Optimization models for assessing the peak capacity utilization of intelligent transportation systems

With limited economic and physical resources, it is not feasible to continually expand transportation infrastructure to adequately support the rapid growth in its usage. This is especially true for traffic coordination systems where the expansion of road infrastructure has not been able to keep pace with the increasing number of vehicles, thereby resulting in congestion and delays. Hence, in addition to striving for the construction of new roads, it is imperative to develop new intelligent transportation management and coordination systems. The effectiveness of a new technique can be evaluated by comparing it with the optimal capacity utilization. If this comparison indicates that substantial improvements are possible, then the cost of developing and deploying an intelligent traffic system can be justified. Moreover, developing an optimization model can also help in capacity planning. For instance, at a given level of demand, if the optimal solution worsens significantly, this implies that no amount of intelligent strategies can handle this demand, and expanding the infrastructure would be the only alternative. In this paper, we demonstrate these concepts through a case study of scheduling vehicles on a grid of intersecting roads. We develop two optimization models namely, the mixed integer programming model and the space-time network flow model, and show that the latter model is substantially more effective. Moreover, we prove that the problem is strongly NP-hard and develop two polynomial-time heuristics. The heuristic solutions are then compared with the optimal capacity utilization obtained using the space-time network model. We also present important managerial implications.     

A path-based double projection method for solving the asymmetric traffic network equilibrium problem

In this paper we propose a new iterative method for solving the asymmetric traffic equilibrium problem when formulated as a variational inequality whose variables are the path flows. The path formulation leads to a decomposable structure of the constraints set and allows us to obtain highly accurate solutions. The proposed method is a column generation scheme based on a variant of the Khobotov’s extragradient method for solving variational inequalities. Computational experiments have been carried out on several networks of a medium-large scale. The results obtained are promising and show the applicability of the method for solving large-scale equilibrium problems. This work has been supported by the National Research Program FIRB/RBNE01WBBBB on Large Scale Nonlinear Optimization.     

General phase transition models for vehicular traffic with point constraints on the flow

In this paper, we present a general phase transition model that describes the evolution of vehicular traffic along a one‐lane road. Two different phases are taken into account, according to whether the traffic is low or heavy. The model is given by a scalar conservation law in the free‐flow phase and by a system of 2 conservation laws in the congested phase. The free‐flow phase is described by a one‐dimensional fundamental diagram corresponding to a Newell‐Daganzo type flux. The congestion phase is described by a two‐dimensional fundamental diagram obtained by perturbing a general fundamental flux. In particular, we study the resulting Riemann problems in the case a local point constraint on the flow of the solutions is enforced.     

On air traffic flow management with rerouting. Part II: Stochastic case

We present a framework for modeling multistage mixed 0-1 problems for the air traffic flow management problem with rerouting (ATFMRP) under uncertainty in the airport arrival and departure capacity, the air sector capacity and the flight demand. The model allows for flight cancelation, if necessary. It considers several types of objective functions to minimize, namely, total ground and air holding cost, penalization of the alternative routes to the scheduled one for each flight, delay cost for the flights to arrive to the airports and the air sector nodes, and penalization for advancing the arrival of the flights to the airport over the scheduled period. A scenario tree based scheme is used to represent the Deterministic Equivalent Model (DEM) of the stochastic mixed 0-1 program with full recourse. The nonanticipativity constraints that equate the so named common 0-1 and continuous variables from the same group of scenarios in each period are implicitly satisfied in the compact representation of DEM. Some computational experience is reported for medium-scale instances. The model is so tight that none of the instances of the testbed but two of them requires to execute the branch-and-cut phase of the MIP optimization engine of choice.     

Numerical algorithms for simulations of a traffic model on road networks

We introduce a simulation algorithm based on a fluid-dynamic model for traffic flows on road networks, which are considered as graphs composed by arcs that meet at some junctions. The approximation of scalar conservation laws along arcs is made by three velocities Kinetic schemes with suitable boundary conditions at junctions. Here we describe the algorithm and we give an example.     

A new car-following model with consideration of the prevision driving behavior

In the paper, a new car-following model is presented with the consideration of the prevision driving behavior on a single-lane road. The model’s linear stability condition is obtained by applying the linear stability theory. And through nonlinear analysis, a modified Korteweg–de Vries (mKdV) equation is derived to describe the propagating behavior of traffic density wave near the critical point. Numerical simulation shows that the new model can improve the stability of traffic flow by adjusting the driver’s prevision intensity parameter, which is consistent with the theoretical analysis.