Biblio

In this paper, a planning method of transportation-power coupled system based on road expansion model is proposed. First of all, based on the Wardrop equilibrium state, the traffic flow is distributed, to build the road expansion model and complete the traffic network modeling. It is assumed that the road charging demand is directly proportional to the road traffic flow, and the charging facilities will cause a certain degree of congestion on the road. This mutual influence relationship to establish a coupling system of transportation network and power network is used for the planning. In the planning method, the decision variables include the location of charging facilities, the setting of energy storage systems and the road expansion scheme. The planning goal is to minimize the investment cost and operation cost. The CPLEX solver is used to solve the mixed integer nonlinear programming problem. Finally, the simulation analysis is carried out to verify the validity and feasibility of the planning method, which can comprehensively consider the road expansion cost and travel time cost, taking a coupled system of 5-node traffic system and IEEE14 node distribution network as example.

Traffic signals were originally standalone hardware devices running on fixed schedules, but by now, they have evolved into complex networked systems. As a consequence, traffic signals have become susceptible to attacks through wireless interfaces or even remote attacks through the Internet. Indeed, recent studies have shown that many traffic lights deployed in practice have easily exploitable vulnerabilities, which allow an attacker to tamper with the configuration of the signal. Due to hardware-based failsafes, these vulnerabilities cannot be used to cause accidents. However, they may be used to cause disastrous traffic congestions. Building on Daganzo's well-known traffic model, we introduce an approach for evaluating vulnerabilities of transportation networks, identifying traffic signals that have the greatest impact on congestion and which, therefore, make natural targets for attacks. While we prove that finding an attack that maximally impacts congestion is NP-hard, we also exhibit a polynomial-time heuristic algorithm for computing approximately optimal attacks. We then use numerical experiments to show that our algorithm is extremely efficient in practice. Finally, we also evaluate our approach using the SUMO traffic simulator with a real-world transportation network, demonstrating vulnerabilities of this network. These simulation results extend the numerical experiments by showing that our algorithm is extremely efficient in a microsimulation model as well.