Modeling of small sea floaters in the central Mediterranean Sea: seasonality of at-sea distributions

Floating marine debris represent a threat to marine and coastal ecology. Since the Mediterranean basin is one of the highly impacted regions, both by the coastal pollution as well as from sea traffic, the potential harm of a floating pollution on the marine ecology could be overwhelming in this area. Our study area covers the central Mediterranean crossing that connects the western and eastern Mediterranean and is one of the areas impacted by a high intensity of sea traffic. To identify regions in the central Mediterranean that could be more exposed by high concentration of floating marine pollutants we use Leeway model for lower windage small-size particles. We perform numerical simulation of a large ensemble of Lagrangian particles that approximate at-sea debris. The particles are forced by high-resolution sea kinematics from the Copernicus Marine Environment Monitoring Service (CMEMS) and 10 m atmospheric wind from the European Centre for Medium-Range Weather Forecasts (ECMWF) for two reference periods in summer and winter of 2013--2016. We identify the regions with a high accumulation of particles in terms of particle surface densities per unit area. Although seasonal and annual variability of ocean current and atmospheric wind is an important factor that influences accumulation regimes across the central Mediterranean, we found that the border of the Libyan shelf harbors larger percentage of particles after 30 days of simulation

MATSim-T : Architecture and Simulation Times

Micro-simulations for transport planning are becoming increasingly important in traffic simulation, traffic analysis, and traffic forecasting. In the last decades the shift from using typically aggregated data to more detailed, individual based, complex data (e.g. GPS tracking) andthe continuously growing computer performance on fixed price level leads to the possibility of using microscopic models for large scale planning regions. This chapter presents such a micro-simulation. The work is part of the research project MATSim (Multi Agent Transport Simulation, http://matsim.org). In the chapter here the focus lies on design and implementation issues as well as on computational performance of different parts of the system. Based on a study of Swiss daily traffic – ca. 2.3 million individuals using motorized individual transport producing about 7.1 million trips, assigned to a Swiss network model with about 60,000 links, simulated and optimized completely time-dynamic for a complete workday – it is shown that the system is able to generate those traffic patterns in about 36 hours computation time

Evaluation of Performance of Bus Lanes on Urban Expressway Using Paramics Micro-simulation Model

AbstractUrban expressway, as the main skeleton of the road network, is the aorta between urban regions and urban external traffic communication, but also bears the commuter channel. It makes a large amount of traffic flow into the expressway, resulting the congestion in the expressway in many big cities, including Beijing. Taking the Beijing southwest third ring expressway for example, a simulation model was built using Paramics. The simulation model was pre-evaluated before and after the bus lanes set, and the model was post-evaluated to verify the validity of the model after the bus lanes were implemented, it has important theoretical and practical value

Optimizing High Volume Traffic Surges using Discrete Event Simulation

The purpose of this applied research study is to determine the fidelity of a discrete event simulation tool called the Evacuation Simulation Prediction Tool (ESP) in predicting transit times during a high volume surge in traffic flow. The ESP tool was developed for the purpose of predicting and optimizing large-scale evacuations of counties or regions as an aide in emergency and disaster preparedness planning. The goal of the ESP model is to ascertain the balance of traffic flow capacity by managing the human factor events that impinge upon orderly highway travel without immobilizing the travel route. The objective of this discrete-event simulation is the application of optimization techniques to create models with a variety of outcome reliabilities. For this study, evacuation of a large number of vehicles was estimated by the traffic surge that results annually from the Daytona International Speedway (approximately 100,000) immediately following the NASCARTM Nextel Cup Daytona 500. These results were used to determine the effectiveness of the ESP predictions before it could be used to recommend ways to optimize traffic surges during emergencies. The results of this study indicated that the ESP tool accurately predicted the outcome of the Daytona 500 traffic surge under the study conditions. After the predictability of the ESP tool in predicting traffic flow during the race-day surge was validated, optimization techniques were applied to further study the usefulness of the model for other large traffic problems. The parameters were incorporated into the ESP tool to determine the accuracy of the outcome. The results of this study may be useful in considering modifications to traffic flow during real world emergencies such as hurricanes or other potential disasters

A three-dimensional macroscopic fundamental diagram for mixed bi-modal urban networks

Recent research has studied the existence and the properties of a macroscopic fundamental diagram (MFD) for large urban networks. The MFD should not be universally expected as high scatter or hysteresis might appear for some type of networks, like heterogeneous networks or freeways. In this paper, we investigate if aggregated relationships can describe the performance of urban bi-modal networks with buses and cars sharing the same road infrastructure and identify how this performance is influenced by the interactions between modes and the effect of bus stops. Based on simulation data, we develop a three-dimensional vehicle MFD (3D-vMFD) relating the accumulation of cars and buses, and the total circulating vehicle flow in the network. This relation experiences low scatter and can be approximated by an exponential-family function. We also propose a parsimonious model to estimate a three-dimensional passenger MFD (3D-pMFD), which provides a different perspective of the flow characteristics in bi-modal networks, by considering that buses carry more passengers. We also show that a constant Bus-Car Unit (BCU) equivalent value cannot describe the influence of buses in the system as congestion develops. We then integrate a partitioning algorithm to cluster the network into a small number of regions with similar mode composition and level of congestion. Our results show that partitioning unveils important traffic properties of flow heterogeneity in the studied network. Interactions between buses and cars are different in the partitioned regions due to higher density of buses. Building on these results, various traffic management strategies in bi-modal multi-region urban networks can then be integrated, such as redistribution of urban space among different modes, perimeter signal control with preferential treatment of buses and bus priority

An agent-based DDM for high level architecture

The Data Distribution Management (DDM) service is one of the six services provided in the Runtime Infrastructure (RTI) of High Level Architecture (HLA). Its purpose is to perform data filtering and reduce irrelevant data communicated between federates. The two DDM schemes proposed for RTI, region based and grid based DDM, are oriented to send as little irrelevant data to subscribers as possible, but only manage to filter part of this information and some irrelevant data is still being communicated. Previously (G. Tan et al., 2000), we employed intelligent agents to perform data filtering in HLA, implemented an agent based DDM in RTI (ARTI) and compared it with the other two filtering mechanisms. The paper reports on additional experiments, results and analysis using two scenarios: the AWACS sensing aircraft simulation and the air traffic control simulation scenario. Experimental results show that compared with other mechanisms, the agent based approach communicates only relevant data and minimizes network communication, and is also comparable in terms of time efficiency. Some guidelines on when the agent based scheme can be used are also give

Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization

Statistical traffic data analysis is a hot topic in traffic management and control. In this field, current research progresses focus on analyzing traffic flows of individual links or local regions in a transportation network. Less attention are paid to the global view of traffic states over the entire network, which is important for modeling large-scale traffic scenes. Our aim is precisely to propose a new methodology for extracting spatio-temporal traffic patterns, ultimately for modeling large-scale traffic dynamics, and long-term traffic forecasting. We attack this issue by utilizing Locality-Preserving Non-negative Matrix Factorization (LPNMF) to derive low-dimensional representation of network-level traffic states. Clustering is performed on the compact LPNMF projections to unveil typical spatial patterns and temporal dynamics of network-level traffic states. We have tested the proposed method on simulated traffic data generated for a large-scale road network, and reported experimental results validate the ability of our approach for extracting meaningful large-scale space-time traffic patterns. Furthermore, the derived clustering results provide an intuitive understanding of spatial-temporal characteristics of traffic flows in the large-scale network, and a basis for potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013