A 2-Dimensional Cellular Automaton for Agents Moving from Origins to Destinations

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 $"\mu"$ is also introduced to control the probability that the movement of all agents involved to the same site (conflict) is denied at one time step. This model displays two states; namely the freely moving and the jamming state. If $\mu$ is large and $p$ is low, the system is in the jamming state even if the density is low. However, if $\mu$ 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 $\mu$ is large and $p$ is low (jamming state); but long travel times are suppressed if $p$ becomes large (freely moving state).Comment: 10 pages, 12 figure

Biham-Middleton-Levine Traffic Model With Origin-Destination Trips

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 consider two variants of the model. In conserved particles model (Model A), drivers continue their travelling even if they reached their destinations. In non-conserved particles model (Model B), a driver which reaches its destination disappears with rate $\beta$. It is found that the traffic dynamics in model A and the evacuation processes in model B are greatly influenced by the ODDPD. On one hand, we found that we can adjust the ODDPD to enhance the road capacity of the city and to minimize the arrival times of drivers in particles conserved system and to optimize the evacuation time of drivers in non-conserved case. On the other hand, we find that, independently on the ODDPD, the evacuation time $T$ of drivers diverges in the form of a power law $T \propto \beta^{-\nu}$, with $\nu=1$.Comment: 13 pages, 14 figure

Un modèle d'automate cellulaire pour le trafic urbain avec plusieurs ronds-points

International audienceUrban transportation with multiple roundabouts is facing significant challenges such as traffic congestion, gridlock and traffic accidents. In order to understand these behaviors, we propose a two-dimensional cellular automata (CA) model, where all streets are two-way, with one lane in each direction. To allow the turning movement, a roundabout is designed for each intersection where four roads meet. The distance between each pair of roundabouts is configured with the parameter K while the turning behavior of drivers is modeled by a parameter γ. To study the impact of these different parameters on the urban traffic, several traffic metrics are considered such as traffic flow, average velocity, accident probability and waiting time at the entrance of roundabout. Our simulation results show that the urban traffic is in free flow state when the vehicle's density is low enough. However, when the density exceeds a critical density ρ c , the urban traffic will be in gridlock state whenever γ is nonzero. In the case where γ = 0, the urban traffic presents a phase transition between free flow and congested state. Furthermore, detailed analysis of the traffic metrics shows that the model parameters (γ, K) have a significant effects on urban traffic dynamics

Hydrological Characterization of Mediterranean Catchments

A climatic, physiographic, and hydrological homogeneity across the Mediterranean has been detected but not yet confirmed. Mediterranean climate is known for its precipitation seasonality and the alternation of humid winters and dry summers which conditions rivers flow regimes, landcover, agriculture and consequently any water resources management plan. Several physiographic traits could be also observed across the Mediterranean, like the elevated and exposed karstic features, and the cultivated and managed areas. Hydrologically, global river regimes were classified based on monthly average flows only, and Mediterranean regimes were identified under 3 of Haines' 15 global classes with a clear relation to Köppen's Mediterranean climate. Thus, we first studied the flow regimes of 55 Mediterranean catchments to verify if Mediterranean rivers fall into same regime class. Second, we characterized the Mediterranean hydrological response through different water balance functional models as advanced by Budyko, L'vovich and elaborated by Ponce &amp; Shetty and Sivapalan. The water balance analysis highlighted the Mediterranean trend following the general climatic setting from the wet Northern region to the arid Southern region; it also showed hydrological homogeneity for mountainous karstic, and snow influenced catchments which yield the highest baseflows and runoff coefficients, especially if located in moderate climate.</p

Impact of Mobility Design on Network Connectivity Dynamics in Urban Environment

International audienceVehicular ad hoc networks have emerged as a promising research area for enabling various types of applications. However, VANETs are facing the biggest challenges in providing reliable communications due to unstable network connectivity caused by vehicle mobility. Several efforts have been carried out to analyze this problem. However, these efforts ignore the impact of intelligent mobility designs. This work highlights the relationship between intelligent mobility for transportation systems and connectivity dynamics. For this purpose, a framework of different mobility models is proposed based on the cellular automata (CA) approach to simulate vehicles mobility in a Manhattan two-dimensional network of roundabouts. To take into account intelligent aspects of mobility for drivers, a centralized path planning strategy based on the Bellman-Ford algorithm analyzes the travel time at road segments to provide the shortest paths for vehicles. Besides, we categorize three mobility models: In the first mobility model, vehicles are assigned the shortest paths in real-time with a periodic update. Each shortest path is defined as a set of turning movements (i.e., right, left, straight,. . .), where each turning step represents the driver's decision at the next roundabout. At roundabouts, vehicles follow priority rules to avoid conflict with other traffics. The second mobility model is similar to the first one, but vehicles do have not the possibility to update their assigned shortest paths. The third model extends the first one by using traffic lights instead of priority rules at roundabouts. Extensive simulations based on both generated mobility traces and NS-2 analyze both network connectivity and reliability under several effective factors, including vehicles' mobility model, vehicles' speed, vehicles' density, transmission range, and RSUs deployment strategy in terms of RSUs' position and number. Several performance metrics of interest are introduced (such as packet delivery ratio, paths length, link duration, and end-to-end delay). The simulation results show that both the network performance and connectivity condition are sensitive to multiple factors, which reflect the V2V and V2I communications under varying conditions