A Linear Programming Model for the Single Destination System Optimum Dynamic Traffic Assignment Problem

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.

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“…Here, we provide a summary of this traffic model; for a more detailed discussion, we refer the reader to [7,8,19].…”

Section: Cell Transmission Modelmentioning

confidence: 99%

Vulnerability of Transportation Networks to Traffic-Signal Tampering

Lászka

Potteiger²,

Vorobeychik³

et al. 2016

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)

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“…To solve the user equilibrium formulation the spatial route allocations are iteratively adjusted until an equilibrium is achieved. Ziliaskopoulos (2000) reformulated a relaxed form of the CTM as a set of linear constraints and hence developed a linear programming model for the single-destination system optimum DTA problem for a network. The model was further analysed and applied by Waller (2000), Li et al (2003), Alecsandru (2006), Ukkusuri and Waller (2008), Zeng (2009 andLiu (2010).…”

Section: Introductionmentioning

confidence: 99%

“…Moving onto that portion of the curve would reduce the traffic outflow and eventually cause a reduction in inflow and throughput and hence increase overall system travel times or costs. Holding back of traffic could be used to reduce or prevent that and hence can be interpreted as a desirable form of traffic flow control (as in Carey (1987) and Ziliaskopoulos (2000)). Such flow controls could potentially be implemented by variable speed controls, ramp metering or other methods associated with future intelligent traffic and transport systems.…”

Section: Introductionmentioning

confidence: 99%

Dynamic traffic assignment approximating the kinematic wave model: System optimum, marginal costs, externalities and tolls

Carey

Watling

2012

Transportation Research Part B: Methodological

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System marginal costs, externalities and optimal congestion tolls for traffic networks are generally derived from system optimizing (SO) traffic assignment models and when these are treated as varying over time they are all referred to as dynamic. In dynamic SO network models the link flows and travel times or costs are generally modelled using so-called "whole link" models. Here we instead develop an SO model that more closely reflects traffic flow theory and derive the marginal costs and externalities from that. The most widely accepted traffic flow model appears to be the LWR (Lighthill, Whitham and Richards) model and a tractable discrete implementation or approximation to that is provided by the cell transmission model (CTM) or a finite difference approximation (FDA). These handles spillbacks, traffic controls and moving queues in a way that is consistent with the LWR model (hence with the kinematic wave model and fluid flow model). An SO formulation using the CTM is already available, assuming a single destination and a trapezoidal flow-density function. We extend the formulation to allow more general nonlinear flow density functions and derive and interpret system marginal costs and externalities. We show that if tolls computed from the DSO solution are imposed on users then the DSO solution would also satisfy the criteria for a dynamic user equilibrium (DUE). We introduce constraints on the link outflow proportions at merges and inflow proportions at diverges. We also extend the model to elastic demands and establish links with previous dynamic traffic assignment (DTA) models.

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“…Ziliaskopoulos, A.K. (2000) used the cell transmission model to formulate the single destination SO-DTA problem as a Linear Program [4]. Shen, W. (2008) reviewed the link-based SO-DTA formulations based on different traffic flow models and explored the impact of modeling details in the traffic flow model on the optimal solution of the SO-DTA model [5].…”

Section: Introductionmentioning

confidence: 99%