Narrow Results

In this paper, we propose a growing spatial network (GSN) model and investigate its topology properties and dynamical behaviors. The model is generated by adding one node i with m links into a square lattice at each time step and the new node i is connected to the existing nodes with probabilities proportional to: , where k_j is the degree of node j, α is the tunable parameter and d_ij is the Euclidean distance between i and j. It is found that both the degree heterogeneity and the clustering coefficient monotonously increase with the increment of α, while the average shortest path length monotonously decreases. Moreover, the evolutionary game dynamics and network traffic dynamics are investigated. Simulation results show that the value of α can also greatly influence the dynamic behaviors.

Despite of intensive investigations on transportation dynamics taking place on complex networks with fixed structures, a deep understanding of networks consisting of mobile nodes is challenging yet, especially the lacking of insight into the effects of routing strategies on transmission efficiency. We introduce a distance-based routing strategy for networks of mobile agents toward enhancing the network throughput and the transmission efficiency. We study the transportation capacity and delivering time of data packets associated with mobility and communication ability. Interestingly, we find that the transportation capacity is optimized at moderate moving speed, which is quite different from random routing strategy. In addition, both continuous and discontinuous transitions from free flow to congestions are observed. Degree distributions are explored in order to explain the enhancement of network throughput and other observations. Our work is valuable toward understanding complex transportation dynamics and designing effective routing protocols.

The link congestion based traffic model can more accurately reveal the traffic dynamics of many real complex networks such as the Internet, and heuristically optimizing each link's weight for the shortest path routing strategy can strongly improve the traffic capacity of network. In this work, we propose an optimal routing strategy in which the weight of each link is regulated incrementally to enhance the network traffic capacity by minimizing the maximum link betweenness of any link in the network. We also estimate more suitable value of the tunable parameter β for the efficient routing strategy under the link congestion based traffic model. The traffic load of network can be significantly balanced at the expense of increasing a bit average path length or average traffic load.

Although the on-ramp system has been widely studied, the influence of heavy vehicles is unknown because researchers only investigate the traffic dynamics around on-ramp system under homogeneous traffic conditions, which is different in real-world settings. This paper uses an improved cellular automaton model to study the heterogeneous traffic around on-ramp system. The forward motion rules are improved by considering the differences of driving behavior in different vehicle combinations. The lane change rules are improved by reflecting the aggressive behavior in mandatory lane changes. The phase diagram, traffic flow, capacity and spatial-temporal diagram are analyzed under the influences of heavy vehicles. The results show that by increasing the percentage of heavy vehicles, there will be more severe traffic congestion around on-ramp system, lower saturated flow and capacity. Also, the interactions between main road and on-ramp have been investigated. Increasing the percentage of heavy vehicles at the upstream of the conflict area on the main road or restricting heavy vehicles on the outside lane of the main road will deteriorate the performance of on-ramp. While the main road will have better performance as the percentage of heavy vehicles on the on-ramp increases when the on-ramp inflow rate is not low.

The development of modern economy makes the problem of traffic congestion increasingly serious. Many real traffic systems can be abstracted as that a variety of networks coupled and interconnected with each other. In this paper, the traffic dynamics on a double layer coupled network system is studied based on cellular automata model considering physical queuing. We explore the effect of maximal velocities in the two layer networks on the network capacity, and the mean and standard deviation of travel time. The results show that the increase of upper network velocity is beneficial to the traffic capacity and the efficiency of long-distance travel, but will also lead to larger deviation and lower reliability. We explain the phenomena by studying the usage of upper network. Finally, we investigate the vehicle distribution by adopting the Gini coefficient. It is found that the increase of upper network speed will make the traffic load distributed more uniformly in the system.

In recent years, the transportation systems have to face the increasing challenges of congestion and inefficiency, and therefore the research on traffic dynamics of complex networks has become a significant area. When the total node capacity of the network is fixed, a reasonable queue resource reallocation strategy is effective in improving the network traffic capacity. In this paper, a new queue resource allocation method is proposed based on the betweenness centrality and the degree centrality of nodes. With the proposed strategy, the node queue length is allocated accurately to enhance the transport efficiency. Simulation results show that the proposed strategy can effectively improve the traffic capacity of the scale-free networks.

In complex networks, how topology structures influence the traffic dynamics on networks is a hot topic. The existence of structure within communities may affect the transmission of data packets in the network. In this paper, we investigate the impact of community structure on traffic dynamics in random networks which are homogeneous. It is found that three key factors influence traffic capacity in such community networks: community number, community strength, and average node degree. The increase of average node degree increases traffic capacity, while the increase of community number and community strength decreases traffic capacity.

The mixed traffic of conventional vehicles (CVs) and connected and autonomous vehicles (CAVs) will dominate a long period in the popularization of CAVs and its dynamics are still not clear, especially in some complicated traffic scenarios. In this paper, an improved cellular automaton model was applied to investigate the CVs–CAVs mixed traffic dynamics around freeway merging area. The lane-changing rules were improved to consider the difference between CAVs and CVs and also the variation of drivers’ aggressive driving during the merging process. The simulation has been conducted with different traffic demands and percentages of CAVs while the phase diagram, spatial-temporal diagram, characteristics of traffic volume, velocity profiles, stability and traffic conflict around the merging area were obtained. The results show that CAVs contribute to improving the traffic operation of freeway merging area in most conditions. However, more CAVs in the merging area without proper management will have a negative impact on traffic if the traffic demand for the main road is great. For example, the stability can decrease and particularly, the on-ramp traffic will be completely blocked when the percentage of CAVs is one. The findings can provide a basis for further research on the CVs–CAVs mixed traffic.

This paper proposed a novel routing strategy with two tunable parameters, ${\alpha }_1$, $\beta$, based on the dynamic local information in a finite buffer network. The system behaves differently from that with a local routing strategy based on the degree with an adjustable parameter ${\alpha }_2$. Simulations show that, under dynamic local strategy, the maximal capacity of the network system increases with $\beta$ whereas it increases with the decrement of ${\alpha }_1$ in the case of all the nodes having identical delivering ability. The dynamic local routing performs much better than the local routing, which is demonstrated by a larger value of the critical packet generation rate. We found that buffer coefficient has a limited impact on the performance of network system. We also demonstrate that a smaller ${\alpha }_1$ would be better if we want to have an excellent traffic capacity considering the travel time, average path length and waiting time. Our study will be helpful to improve traffic performance in finite buffer networks.

The study of traffic dynamics on multi-layer network contributes to revealing the traffic law of the actual network and suppressing the occurrence of network congestion. Focusing on actual network traffic characteristics, we construct a three-layer network model which consists of one logical layer and two physical layers. The traffic dynamics of the three-layer network model are carefully compared with different logical layer routing strategies. In addition, three indicators of load average, load standard deviation and load reduction rate are proposed to more intuitively observe the load conditions of physical layer nodes. We also study the impact of the degree distribution of physical layer on traffic capacity of three-layer network. It is found that when the preference of packets selection for small degree nodes is insufficient or excessive, it is not conducive to achieving higher traffic capacity.

The triggered stop-and-go traffic states are investigated within the hydrodynamic approach. The detailed phase boundaries are obtained. Spatial–temporal profile of the congestion is analyzed. The smooth tail of the density profile provides a characteristic mechanism to trigger subsequent traffic jams.

The nodes with the largest degree are very susceptible to traffic congestion, thus an effective way to improve traffic and control congestion can be redistributing traffic load in hub nodes to others. We proposed an efficient routing strategy, which can remarkably enhance the network throughput. In addition, by using detrended fluctuation analysis, we found that the traffic rate fluctuation near the critical point exhibits the 1/f scaling in the power spectrum, which is in accordance with the empirical data.

It is important to study the effects of pedestrian crossing behaviors on traffic flow for solving the urban traffic jam problem. Based on the Nagel–Schreckenberg (NaSch) traffic cellular automata (TCA) model, a new one-dimensional TCA model is proposed considering the uncertainty conflict behaviors between pedestrians and vehicles at unsignalized mid-block crosswalks and defining the parallel updating rules of motion states of pedestrians and vehicles. The traffic flow is simulated for different vehicle densities and behavior trigger probabilities. The fundamental diagrams show that no matter what the values of vehicle braking probability, pedestrian acceleration crossing probability, pedestrian backing probability and pedestrian generation probability, the system flow shows the "increasing–saturating–decreasing" trend with the increase of vehicle density; when the vehicle braking probability is lower, it is easy to cause an emergency brake of vehicle and result in great fluctuation of saturated flow; the saturated flow decreases slightly with the increase of the pedestrian acceleration crossing probability; when the pedestrian backing probability lies between 0.4 and 0.6, the saturated flow is unstable, which shows the hesitant behavior of pedestrians when making the decision of backing; the maximum flow is sensitive to the pedestrian generation probability and rapidly decreases with increasing the pedestrian generation probability, the maximum flow is approximately equal to zero when the probability is more than 0.5. The simulations prove that the influence of frequent crossing behavior upon vehicle flow is immense; the vehicle flow decreases and gets into serious congestion state rapidly with the increase of the pedestrian generation probability.

There always exist a conflict between vehicles and pedestrians on urban streets. To study the complicated behaviors of vehicles and pedestrians is significant to understand the dynamics of traffic flow and explore efficient strategies for solving traffic problems. Based on this, this paper studies the effect of pedestrian’s jaywalking behavior on vehicle flow with no crosswalks and no traffic lights, and constructs a cellular automata model to describe the dynamic traffic flow by considering some significant traffic behaviors of pedestrians and vehicles. Experimental results show that the maximum vehicle flow will be greatly affected as long as there are jaywalking pedestrians; retreating behavior may lead to the fluctuation in vehicle flow; the maximum vehicle flow is greater if more pedestrians choose the behavior of fast crossing. This research is helpful to further study the interaction between pedestrians and vehicles.

Modeling the dynamics of a traffic system involves using the principles of both physical and social sciences since it is composed of vehicles as well as drivers. A novel car-following model is proposed in this paper by incorporating the socio-psychological aspects of drivers into the dynamics of a purely physics-based spring–mass–damper mechanical system to represent the driver–vehicle longitudinal movements in a traffic stream. The crux of this model is that a traffic system can be viewed as various masses interacting with each other by means of springs and dampers attached between them. While the spring and damping constants represent the driver behavioral parameters, the mass component represents the vehicle characteristics. The proposed model when tested for its ability to capture the traffic system dynamics both at micro, driver, and macro, stream, levels behaved pragmatically. The stability analysis carried out using perturbation method also revealed that the proposed model is both locally and asymptotically stable.

Modeling multiple complex systems as multiplex networks has refreshed our opinions about the structure and dynamics of assorted real-world systems. By studying the traffic dynamics of two-layer multiplex networks, it can be found that the limited buffer queue length of each node in both the logical and physical layers is one of the factors that restrict the whole network traffic capacity. Since high degree nodes in the network may bear heavier load, it is necessary to allocate a larger buffer queue to them. In order to enhance the transport efficiency, a new queue allocation strategy is proposed to alleviate the congestion of the two-layer multiplex networks. By extensive computer simulations, the proposed strategy can effectively improve the traffic capacity of the multiplex networks.

In order to study traffic dynamics on multilayer networks, it is of great significance to build a network model which can more exactly reflect the actual network layered structure characteristics. In this paper, a three-layer network model in which two logical layers are mapped on one physical layer is established, and the traffic capacities of three kinds of multilayer networks with different combinations of logical layers are compared. Simulation results show that when the physical layer is the same random network, the network whose logical layers are two random networks has the optimal traffic capacity, the network with one random network and one scale-free network in the logical layers has the better traffic capacity than the network whose logical layers are two scale-free networks.

We study the Car-Oriented Mean-Field approximation (COMF) to the Nagel–Schreckenberg model in the case of v_max=3. The self-consistent equations are obtained. The solution is reached by the method of iteration. When the stochastic noise is small, the numerical simulations can be well described by the mean-field theory. When the stochastic noise is large, the flux around critical density is overestimated. The overshooting of the free flow can be attributed to the collective effect of the stochastic noise.

Competitive services have large effects on traffic congestion. Based on the investigation on Chinese railway network, a competitive services model is proposed to capture traffic correlations between different services. According to the value of the weight parameter $𝜃$, competitive services are grouped into assortative, disassortative and neutral ones, in which their traffic loads show strongly positive, weakly positive and non-significant correlations, respectively. Simulation results illustrate that our model can effectively capture the service correlations by adjusting the weight parameter. Using this model, we analyze the influence of different types of competitive services on network congestion. Our results indicate that both capacity and efficiency of network is the lowest when it supports assortative services.

We study the traffic dynamics on two-layer complex networks, and focus on its delivery capacity allocation strategy to enhance traffic capacity measured by the critical value $R_c$. With the limited packet-delivering capacity, we propose a delivery capacity allocation strategy which can balance the capacities of non-hub nodes and hub nodes to optimize the data flow. With the optimal value of parameter ${\alpha }_c$, the maximal network capacity is reached because most of the nodes have shared the appropriate delivery capacity by the proposed delivery capacity allocation strategy. Our work will be beneficial to network service providers to design optimal networked traffic dynamics.