Integrating Truck Emissions Cost in Traffic Assignment

The adverse impacts of greenhouse gasses (GHG) and the imperative for reducing the existing rate of GHG production are well established. In the United States, the largest source of GHG emissions from human activities is from burning fossil fuels, primarily for the generation of electricity and transportation. The transportation sector accounts for 28% of all U.S. GHG production. Heavy-duty vehicles, such as large freight trucks, account for nearly one-fifth of the U.S. total, and this fraction is expected to grow rapidly. Consequently, many efforts are being used to reduce the total emissions of freight trucks. Most efforts emphasize one of four areas: engineering improvements to improve fuel economy or reduce emissions, shifts to other transport modes, improved logistics to reduce the movement of partially full or empty containers, and reduced travel costs for individual trucks. A few studies have assessed modifications to route choice considerations as a means of improving the fuel economy of individual vehicles and show potential gains. In this study, the potential gains of emissions-based route choice were assessed by integrating the U.S. Environmental Protection Agency motor vehicle emission simulator with a macroscopic regional traffic demand model. For this integration, route choices included a simplified emissions calculation within the repeated model iteration runs of an algorithm of the Frank–Wolfe type. The analyses suggested that reductions of freight truck emissions were possible and showed an example in which the total system’s truck emissions were reduced by up to 0.61% (88.8 tons)

Distribution Analysis of Freight Transportation with Gravity Model and Genetic Algorithm

The application of a gravity model in freight modeling work on both short-haul and long-haul trips is discussed. A commodity-based gravity model was developed to assess the distribution of freight by long-haul trucks in southeastern Virginia. Although gravity models have been used extensively in transportation studies, little work has been done to address the special characteristics of freight transportation, such as the definition of friction factors and the differences between long-haul and short-haul trips. Results of a recent study of these and similar problems provide valuable insight into freight distribution modeling. A new calibration method that used a genetic algorithm was applied, various commodities were modeled, and the impact of the commodities on the accuracy of the gravity model was studied. Both travel time and travel distance were tested to generate the impedance for friction factors; results showed that for commodity-based long-haul models, travel times were more appropriate for friction factor calculations. In addition, results showed that the gamma function was more suitable than the exponential function for friction factor calculations. Extensive analyses of the causes of variation between observed values and the gravity model outputs are provided. The analyses and conclusions may help modelers better understand characteristics specific to freight transportation and can promote model constructions with greater accuracy and efficiency

Integrating a Simple Traffic Incident Model for Rapid Evacuation Analysis

Road transportation networks are a segment of society\u27s critical infrastructure particularly susceptible to service disruptions. Traffic incidents disrupt road networks by producing blockages and increasing travel times, creating significant impacts during emergency events such as evacuations. For this reason, it is extremely important to incorporate traffic incidents in evacuation planning models. Emergency managers and decision makers need tools that enable rapid assessment of multiple, varied scenarios. Many evacuation simulations require high-fidelity data input making them impractical for rapid deployment by practitioners. Since there is such variation in evacuation types and the method of disruption, evacuation models do not require the high-fidelity data needed by other types of transportation models. This paper\u27s purpose is to show that decision makers can gain useful information from rapid evacuation modeling which includes a simple traffic incident model. To achieve this purpose, the research team integrated a generic incident model into the Real-time Evacuation Planning Model (RtePM), a tool commissioned by the U.S. Department of Homeland Security to help emergency planners determine regional evacuation clearance times in the United States. RtePM is a simple, web-based tool that enables emergency planners to consider multiple evacuation plans at no additional cost to the user. Using this tool, we analyzed a simple scenario of the United States\u27 National Capital Region (NCR) to determine the impact of traffic incidents when different destination routes are blocked. The results indicate significant variations in evacuation duration when blockages are considered

Generic Incident Model for Investigating Traffic Incident Impacts on Evacuation Times in Large-Scale Emergencies

Traffic incidents cause a ripple effect of reduced travel speeds, lane changes, and the pursuit of alternative routes that results in gridlock on the immediately affected and surrounding roadways. The disruptions caused by the secondary effects significantly degrade travel time reliability, which is of great concern to the emergency planners who manage evacuations. Outcomes forecast by a generic incident model embedded in a microscopic evacuation simulation, the Real-Time Evacuation Planning Model (RtePM), were examined to quantify the change in time required for an emergency evacuation that results from traffic incidents. The incident model considered vehicle miles traveled on each individual segment of the studied road network model. The two scenarios considered for this investigation were evacuations of (a) Washington, D.C., after a simulated terrorist attack and (b) Virginia Beach, Virginia, in response to a simulated hurricane. These results could help the emergency planning community understand and investigate the impact of traffic incidents during an evacuation

A Blockchain Simulator for Evaluating Consensus Algorithms in Diverse Networking Environments

The massive scale, heterogeneity and distributed nature of Internet-of-Things (IoT) presents challenges in realizing a practical and effective security solution. Blockchain empowered platforms and technologies have been proposed to address aspects of this challenge. In order to realize a practical Blockchain deployment for IoT, there is a need for a testing and evaluation platform to evaluate performance and security of Blockchain applications and systems. In this paper, we present a Blockchain simulator that evaluates the consensus algorithms in a realistic and configurable network environment. Though, there are several Blockchain evaluation platforms, they are either wedded to a specific consensus protocol and do not allow evaluation in a configurable and realistic network environment. In our proposed simulator, we provide the ability to evaluate the impact of the consensus and network layer that will inform practitioners on the appropriate choice of consensus algorithms and the impact of network layer events in congested or contested scenarios in IoT. To accomplish this a generalized representation for consensus methods is proposed. The Blockchain simulator uses a discrete event simulation engine for fidelity and increased scalability. We evaluate the performance of the simulator by varying the number of peer nodes and number of messages required to find consensus

Measuring Decentrality in Blockchain Based Systems

Blockchain promises to provide a distributed and decentralized means of trust among untrusted users. However, in recent years, a shift from decentrality to centrality has been observed in the most accepted Blockchain system, i.e., Bitcoin. This shift has motivated researchers to identify the cause of decentrality, quantify decentrality and analyze the impact of decentrality. In this work, we take a holistic approach to identify and quantify decentrality in Blockchain based systems. First, we identify the emergence of centrality in three layers of Blockchain based systems, namely governance layer, network layer and storage layer. Then, we quantify decentrality in these layers using various metrics. At the governance layer, we measure decentrality in terms of fairness, entropy, Gini coefficient, Kullback-Leibler divergence, etc. Similarly, in the network layer, we measure decentrality by using degree centrality, betweenness centrality and closeness centrality. At the storage layer, we apply a distribution index to define centrality. Subsequently, we evaluate the decentrality in Bitcoin and Ethereum networks and discuss our observations. We noticed that, with time, both Bitcoin and Ethereum networks tend to behave like centralized systems where a few nodes govern the whole network