1,043 research outputs found
Uncertainty in Multi-Commodity Routing Networks: When does it help?
We study the equilibrium behavior in a multi-commodity selfish routing game
with many types of uncertain users where each user over- or under-estimates
their congestion costs by a multiplicative factor. Surprisingly, we find that
uncertainties in different directions have qualitatively distinct impacts on
equilibria. Namely, contrary to the usual notion that uncertainty increases
inefficiencies, network congestion actually decreases when users over-estimate
their costs. On the other hand, under-estimation of costs leads to increased
congestion. We apply these results to urban transportation networks, where
drivers have different estimates about the cost of congestion. In light of the
dynamic pricing policies aimed at tackling congestion, our results indicate
that users' perception of these prices can significantly impact the policy's
efficacy, and "caution in the face of uncertainty" leads to favorable network
conditions.Comment: Currently under revie
A Study of Truck Platooning Incentives Using a Congestion Game
We introduce an atomic congestion game with two types of agents, cars and
trucks, to model the traffic flow on a road over various time intervals of the
day. Cars maximize their utility by finding a trade-off between the time they
choose to use the road, the average velocity of the flow at that time, and the
dynamic congestion tax that they pay for using the road. In addition to these
terms, the trucks have an incentive for using the road at the same time as
their peers because they have platooning capabilities, which allow them to save
fuel. The dynamics and equilibria of this game-theoretic model for the
interaction between car traffic and truck platooning incentives are
investigated. We use traffic data from Stockholm to validate parts of the
modeling assumptions and extract reasonable parameters for the simulations. We
use joint strategy fictitious play and average strategy fictitious play to
learn a pure strategy Nash equilibrium of this game. We perform a comprehensive
simulation study to understand the influence of various factors, such as the
drivers' value of time and the percentage of the trucks that are equipped with
platooning devices, on the properties of the Nash equilibrium.Comment: Updated Introduction; Improved Literature Revie
Improving Approximate Pure Nash Equilibria in Congestion Games
Congestion games constitute an important class of games to model resource
allocation by different users. As computing an exact or even an approximate
pure Nash equilibrium is in general PLS-complete, Caragiannis et al. (2011)
present a polynomial-time algorithm that computes a ()-approximate pure Nash equilibria for games with linear cost
functions and further results for polynomial cost functions. We show that this
factor can be improved to and further improved results for
polynomial cost functions, by a seemingly simple modification to their
algorithm by allowing for the cost functions used during the best response
dynamics be different from the overall objective function. Interestingly, our
modification to the algorithm also extends to efficiently computing improved
approximate pure Nash equilibria in games with arbitrary non-decreasing
resource cost functions. Additionally, our analysis exhibits an interesting
method to optimally compute universal load dependent taxes and using linear
programming duality prove tight bounds on PoA under universal taxation, e.g,
2.012 for linear congestion games and further results for polynomial cost
functions. Although our approach yield weaker results than that in Bil\`{o} and
Vinci (2016), we remark that our cost functions are locally computable and in
contrast to Bil\`{o} and Vinci (2016) are independent of the actual instance of
the game
The Network Improvement Problem for Equilibrium Routing
In routing games, agents pick their routes through a network to minimize
their own delay. A primary concern for the network designer in routing games is
the average agent delay at equilibrium. A number of methods to control this
average delay have received substantial attention, including network tolls,
Stackelberg routing, and edge removal.
A related approach with arguably greater practical relevance is that of
making investments in improvements to the edges of the network, so that, for a
given investment budget, the average delay at equilibrium in the improved
network is minimized. This problem has received considerable attention in the
literature on transportation research and a number of different algorithms have
been studied. To our knowledge, none of this work gives guarantees on the
output quality of any polynomial-time algorithm. We study a model for this
problem introduced in transportation research literature, and present both
hardness results and algorithms that obtain nearly optimal performance
guarantees.
- We first show that a simple algorithm obtains good approximation guarantees
for the problem. Despite its simplicity, we show that for affine delays the
approximation ratio of 4/3 obtained by the algorithm cannot be improved.
- To obtain better results, we then consider restricted topologies. For
graphs consisting of parallel paths with affine delay functions we give an
optimal algorithm. However, for graphs that consist of a series of parallel
links, we show the problem is weakly NP-hard.
- Finally, we consider the problem in series-parallel graphs, and give an
FPTAS for this case.
Our work thus formalizes the intuition held by transportation researchers
that the network improvement problem is hard, and presents topology-dependent
algorithms that have provably tight approximation guarantees.Comment: 27 pages (including abstract), 3 figure
Efficiency of Restricted Tolls in Non-atomic Network Routing Games
An effective means to reduce the inefficiency of Nash flows in non-
atomic network routing games is to impose tolls on the arcs of the network. It is a well-known fact that marginal cost tolls induce a Nash flow that corresponds to a minimum cost flow. However, despite their effectiveness, marginal cost tolls suffer from two major drawbacks, namely (i) that potentially every arc of the network is tolled, and (ii) that the imposed tolls can be arbitrarily large.
In this paper, we study the restricted network toll problem in which tolls can be imposed on the arcs of the network but are restricted to not exceed a predefined threshold for every arc. We show that optimal restricted tolls can be computed efficiently for parallel-arc networks and affine latency functions. This generalizes a previous work on taxing subnetworks to arbitrary restrictions. Our algorithm is quite simple, but relies on solving several convex programs. The key to our approach is a characterization of the flows that are inducible by restricted tolls for single-commodity networks. We also derive bounds on the efficiency of restricted tolls for multi-commodity networks and polynomial latency functions. These bounds are tight even for parallel-arc networks. Our bounds show that restricted tolls can significantly reduce the price of anarchy if the restrictions imposed on arcs with high-degree polynomials are not too severe. Our proof is constructive. We define tolls respecting the given thresholds and show that these tolls lead to a reduced price of anarchy by using a (\lambda,\mu)-smoothness approach
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