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This paper presents a mathematical programming model and solution method for the path-constrained traffic assignment problem, in which route choices simultaneously follow the Wardropian equilibrium principle and yield the distance constraint imposed on the path. This problem is motivated by the need for modeling distance-restrained electric vehicles in congested networks, but the resulting model and solution method can be applied to various conditions with similar path-based constraints. The equilibrium conditions of the problem reveal that any path cost in the network is the sum of corresponding link costs and a path-specific out-of-range penalty term. The suggested method, based on the classic Frank–Wolfe algorithm, incorporates an efficient constrained shortest-path algorithm as its subroutine. This algorithm fully exploits the underlying network structure of the problem and is relatively easy to implement. Numerical results from the examples of problems provided show how the equilibrium conditions are reshaped by the path constraint and how the traffic flow patterns are affected by different constraint tightness levels.

This article addresses a new network equilibrium problem with mode and route choices for the emerging need of modeling regional transportation networks that accommodate both gasoline and electric vehicles. The two transportation modes (or vehicle types) distinguish from each other in terms of driving distance limit and travel cost composition. In view of the advantages (e.g., low fuel expenses and vehicle emissions) and disadvantages (e.g., limited driving range and long charging time) pertaining to driving electric vehicles, it is anticipated that a large number of households/motorists may prefer to own both gasoline and electric vehicles (although, of course, many households/motorists still only own gasoline vehicles (GVs) and some households may choose to own electric vehicles only) in the transition period from the petroleum era to the electricity era. The purpose of this article is to offer a traffic equilibrium modeling tool for networks that serve households/motorists who can choose between gasoline and electric vehicles. Specifically, we present a convex optimization model for characterizing such mixed equilibrium traffic networks with both gasoline and electric vehicles, which are expected to exist for a long period in the future. Two competing solution algorithms, a linear approximation algorithm of the Jacobi type and a quadratic approximation algorithm taking the form of the Gauss–Seidel decomposition, are implemented and evaluated. Experimental results clearly show that, from the model behavior perspective, the produced network flow patterns replicate the anticipated combined mode–route choice results, that is, the higher the distance limit or the gasoline price is, the more travelers choose battery electric vehicles (BEVs) when both BEVs and GVs are available to them; and, from the solution efficiency perspective, the quadratic approximation algorithm exhibits linear convergence and can reach higher solution precision in shorter time.

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In many countries across the world, fossil fuels, especially petroleum, are the largest energy source for powering the socioeconomic system, and the transportation sector dominates the consumption of petroleum in these societies. As the petroleum price continuously climbs and the threat of global climate changes becomes more evident, the world is now facing critical challenges in reducing petroleum consumption and exploiting alternative energy sources. A massive adoption of plug-in electric vehicles (PEVs), especially battery electric vehicles (BEVs), offers a very promising approach to changing the current energy consumption structure and diminishing greenhouse gas emissions and other pollutants. Understanding how individual electric vehicle drivers behave subject to the technological restrictions and infrastructure availability and estimating the resulting aggregate supply-demand effects on urban transportation systems is not only critical to transportation infrastructure development, but also has determinant implications in environmental and energy policy enactment. This paper presents an Accepted by EURO Journal on Transportation and Logistics for publication.

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This article defines, formulates, and solves a new equilibrium traffic assignment problem with side constraints-the traffic assignment problem with relays. The relay requirement arises from the driving situation that the onboard fuel capacity of vehicles is lower than what is needed for accomplishing their trips and the number and distribution of refueling infrastructures over the network are under the expected level. We proposed this problem as a modeling platform for evaluating congested regional transportation networks that serve plug-in electric vehicles (in addition to internal combustion engine vehicles), where battery-recharging or battery-swapping stations are scarce. Specifically, we presented a novel nonlinear integer programming formulation, analyzed its mathematical properties and paradoxical phenomena, and suggested a generalized Benders decomposition framework for its solutions. In the algorithmic framework, a gradient projection algorithm and a labeling algorithm are adopted for, respectively, solving the primal problem and the relaxed master problem-the shortest path problem with relays. The modeling and solution methods are implemented for solving a set of example network problems. The numerical analysis results obtained from the implementation clearly show how the driving range limit and relay station location reshape equilibrium network flows.

As a key approach to securing large networks, existing anomaly detection techniques focus primarily on network traffic data -the sheer volume of such data often render detailed analysis very expensive and reduce the effectiveness of these tools. In this paper, we propose a light-weight anomaly detection approach based on unproductive DNS traffic, namely, the failed DNS queries, with a novel tool -DNS failure graphs -which captures the interactions between hosts and failed domain names. We apply a tri-nonnegative matrix factorization technique to recursively extract coherent coclusters (dense subgraphs) from DNS failure graphs. By analyzing the co-clusters in the daily DNS failure graphs from a 3-month DNS trace captured at a large campus network, we find these co-clusters represent a variety of anomalous activities, e.g., spamming, Trojans, bots, etc., which often exhibit distinguishable subgraph structures. In addition, by exploring the temporal properties of the co-clusters, we have identified new anomalies that likely correspond to unreported domain-flux bots.