Narrow Results

The Nagel–Schreckenberg-model (NaSch-model) describes macroscopic features of real traffic very well. However, the characterization of a single car driver's behavior in some details is not realistic, e.g., the NaSch-driver calculates his/her distance to the car in front from the position this car has just in the very moment and ignores that it could move further in the next time step. This behavior is rarely found in real traffic. Normally, a driver estimates the speed of the car in front, takes as well a certain braking distance into consideration and keeps distance accordingly. As an answer to this demand, the second rule of the NaSch-model is modified in the following.

We develop a two-dimensional cellular automaton (CA) as a simple model for agents moving from origins to destinations. Each agent moves towards an empty neighbor site corresponding to the minimal distance to its destination. The stochasticity or noise (p) is introduced in the model dynamics, through the uncertainty in estimating the distance from the destination. The friction parameter "μ" is also introduced to control the probability that movement of all involved agents to the same site (conflict) is denied at each time step. This model displays two states; namely the freely moving and the jamming state. If μ is large and p is low, the system is in the jamming state even if the density is low. However, if μ is large and p is high, a freely moving state takes place whenever the density is low. The cluster size and the travel time distributions in the two states are studied in detail. We find that only very small clusters are present in the freely moving state, while the jamming state displays a bimodal distribution. At low densities, agents can take a very long time to reach their destinations if μ is large and p is low (jamming state); but long travel times are suppressed if p becomes large (freely moving state).

We extended the Biham–Middleton–Levine model to incorporate the origin and destination effect of drivers trips on the traffic in cities. The destination sites are randomly chosen from some origin-destination distances probability distribution "ODDPD." We use three different distributions: exponential, uniform, and power-law. We found that the traffic dynamics of the model are greatly influenced by the ODDPD. Moreover, it is found that we can adjust the ODDPD to enhance the road capacity of the city and to minimize the arrival times of drivers.

In this paper, we propose a simple betweenness-driven model to capture the dynamics of traffic routing choice behaviors. By comparing with two other models (degree-driven and cost-driven), it is shown that the cost-driven routing strategy is more complex and sensitive to traffic congestion. Another result indicates that the load distributions are determined by the connectivity distribution and route choice behaviors of the traffic network. The model thus provides useful insight for the design of traffic networks.

An evacuation of the emergency vehicle (EV) from an origin point (e.g., accident location) to a destination point (e.g., hospital) in lower and higher congestions is simulated using city cellular automata models. We find that the mean speed of the EV and its arrival time all depend enormously on the cars density, the route length of the EV and the turn capability of the cars. Dangerous situations that occurred during the evacuation of the EV are also investigated. By allowing high turning capabilities to cars, considerable improvements are obtained. Indeed, the EV mean speed is enhanced and its arrival time is optimized. Moreover, at relatively high density, a significant reduction of the risk of accident is expected.

In this paper, we propose a cellular automaton model for the evacuation of an emergency vehicle (EV) in highways. To facilitate the evacuation of the EV, some additional lane-changing rules (pull over of ordinary vehicles) are introduced in a two-lane cellular automata model for traffic flow in highway. We find that this pulling over of ordinary vehicles promotes a faster moving of the EV in traffic flow and minimize the EV's impact on the overall traffic.

How to alleviate the damages of cascading failures triggered by the overload of edges/nodes is common in complex networks. To describe the whole cascading failures process from edges overloading to nodes malfunctioning and the dynamic spanning clustering with the evolvement of traffic flow, we propose a capacity assignment model by introducing an equilibrium assignment rule of flow in artificially created scale-free traffic networks. Additionally, the capacity update rule of node is given in this paper. We show that a single failed edge may undergo the cascading failures of nodes, and a small failure has the potential to trigger a global cascade. It is suggested that enhancing the capacity of node is particularly important for the design of any complex network to defense the cascading failures. Meanwhile, it has very important theoretical significance and practical application worthiness in the development of effective methods to alleviate the damage of one or some failed edges/nodes.

Congestion in communication networks is a topic of theoretical interest and practical importance. In this work, we propose a mixed routing strategy by considering the global static information (topology of the network) and local dynamic information (queue length of neighbor nodes). Under this routing strategy, the traffic capacity can be remarkably promoted compared with that by former efficient routing strategy [G. Yan et al., Phys. Rev. E73, 046108 (2006)]. Besides, the traffic capacity, the average packet number as well as the travel time are almost independent of a time delay in updating the local dynamic information.

Traffic flow at a single crossroad consisting of two perpendicular one-lane roads, treated earlier by Ishibashi and Fukui [J. Phys. Soc. Jpn.70, 2793 (2001); 70, 3747 (2001)], has been studied on the basis of the local occupation probability method. However, in this work, based on the novel theoretical analysis and computer simulations, we have studied this crossroad traffic model again and presented the same phase diagrams of traffic flow in the case of various maximum vehicle velocities. We have also derived the flow formulas in all regions in the phase diagrams, which are in good agreement with computer simulation results. Compared with the previous local occupation probability method, our analytical way is simpler and may be widely used for other traffic bottlenecks research.

A coarse-grained cellular automaton is proposed to simulate traffic systems. There, cells represent road sections. A cell can be in two states: jammed or passable. Numerical calculations are performed for a piece of square lattice with open boundary conditions, for the same piece with some cells removed and for a map of a small city. The results indicate the presence of a phase transition in the parameter space, between two macroscopic phases: passable and jammed. The results are supplemented by exact calculations of the stationary probabilities of states for the related Kripke structure constructed for the traffic system. There, the symmetry-based reduction of the state space allows to partially reduce the computational limitations of the numerical method.

The development of real time traffic flow models for urban road networks is of paramount importance for the purposes of optimizing and control of traffic flow. Motivated by the modeling of road networks in last decade, this paper proposes a different and simplified approach, known as section approach to model road networks in the framework of macroscopic traffic flow models. For evaluation of the traffic states on a single road, an anisotropic continuum GK-model developed by [Gupta and Katiyar, J. Phys. A38, 4069 (2005)] is used as a single-section model. This model is applied to a two-section single lane road with points of entry and exits. In place of modeling the effect of off- and on-ramps in the continuity equation, a set of special boundary condition is taken into account to treat the points of entry and exit. A four-section road network comprised of two one-lane roads is also modeled using this methodology. The performances of the section approaches are investigated and obtained results are demonstrated over simulated data for different boundary conditions.

The correlation between the velocity of two successive vehicles as a function of time headway is studied in the one-dimensional cellular automata (CA) NaSch model within parallel dynamic update. It is found that a strong correlation occurs in short time headway. The behavior of the correlation velocity as a function of the car density in different traffic states is also investigated. Moreover, our study is also extended to a more complicated situation where the two vehicles are separated by a number n of other vehicles. It is shown that the velocity correlation coefficient depends strongly on the number n of vehicles in between and on their positions.

In this paper, we propose a pair-dependent rejection rate of packet information between routers in the framework of the minimal traffic model applied to scale-free networks. We have shown that the behavior of the transition point from the phase where the system balances the inflow of new information packets with successful delivery of the old ones to the congested phase depends on the underlying mechanism of packet rejection. It is possible to achieve larger values for the critical load by varying the rejection of the packets issued from a given node by its neighbors. We have proposed an asymmetric protocol, where we found the existence of a whole interval where the packet rejection is strongly beneficial to the overall performance of the system. We have also shown that for the dynamic protocol, the transition point is shifted toward higher values permitting the network to handle more traffic load, despite the fact that the critical load decreases when increasing the rejection parameter.

This work is part of our ongoing effort to design and implement a traffic simulation application capable of handling realistic problem sizes in multiple real-time. Our traffic simulation model includes multi-lane vehicular traffic and individual route-plans. On a 16-CPU SGI Power Challenger and a 12-CPU SUN workstation-cluster we have reached real-time for the whole German Autobahn network.

Traffic simulations are made more realistic by giving individual drivers intentions, i.e., an idea of where they want to go. One possible implementation of this idea is to give each driver an exact pre-computed path, that is, a sequence of roads this driver wants to follow. This paper shows, in a realistic road network, how repeated simulations can be used so that drivers can explore different paths, and how macroscopic quantities such as locations of jams or network throughput change as a result of this.

We describe a simple framework for microsimulation of city traffic. A medium-sized excerpt of Dallas was used to examine different levels of simulation fidelity of a cellular automaton method for the traffic flow simulation and a simple intersection model. We point out problems arising with the granular structure of the underlying rules of motion.

Saturated capacities in traffic systems evoke increasing interest in simulations of complex networks serving as laboratory environment for developing management strategies. Especially for urban areas questions concerning overall traffic control have to be considered with regard to their impacts on the whole network. Modeling traffic flow dynamics using cellular automata allows us to run large network traffic simulations with only comparatively low computational efforts. We present a traffic simulation tool for urban road networks which is based on the Nagel–Schreckenberg Model. Arbitrary kinds of roads and crossings are modeled as combinations of only a few basic elements. Furthermore parking capacities are considered as well as circulations of public transports. The vehicles are driven corresponding to route plans or at random depending on the available data. The application of this network simulation covers investigations on the field of traffic planning as well as online simulations based on real-time traffic data as basis for dynamic traffic management systems.

Iterative transportation microsimulations adjust traveler route plans by iterating between a microsimulation and a route planner. At each iteration, the route planner adjusts individuals' route choices based on the preceding microsimulations. Empirically, this process yields good results, but it is usually unclear when to stop the iterative process when modeling real-world traffic. This paper investigates several criteria to judge relaxation of the iterative process, emphasizing criteria related to traveler decision-making.

Within the framework of Biham–Middleton–Levine traffic model with origin–destination trips, we study the evacuation processes of cars in cities. Cars move from the origin to the destination points. A driver which reaches its destination disappears with rate β. It is found that the evacuation processes are greatly influenced by the origin–destination distance probability distribution. We also find that the evacuation time of drivers diverges in the form of a power law τ ∝ β^-ν, with ν = 1.

In this paper, a hybrid queuing strategy (HQS) is proposed in traffic dynamics model on scale-free networks, where the delivery priority of packets in the queue is related to their distance to destination and the queue length of next jump. We compare the performance of the proposed HQS with that of the traditional first-in-first-out (FIFO) queuing strategy and the shortest-remaining-path-first (SRPF) queuing strategy proposed by Du et al. It is observed that the network traffic efficiency utilizing HQS with suitable value of parameter h can be further improved in the congestion state. Our work provides new insights for the understanding of the networked-traffic systems.