Stackelberg Routing on Parallel Transportation Networks

Krichene, Walid; Reilly, Jack; Amin, Saurabh; Bayen, Alexandre M.

doi:10.1007/978-3-319-27335-8_26-1

Cited by 17 publications

(20 citation statements)

References 36 publications

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“…We can then write the latency as a function of vehicle flow as well as a binary argument s i , which indicates whether the road is congested [23], [30]: congested. An equilibrium has an associated equilibrium latency experienced by all selfish users.…”

Section: A Function Of the Density (N Hmentioning

confidence: 99%

“…As shown in Fig. 2, equilibria may have one road in free-flow and rest congested, or all may be congested [23], [30].…”

Section: Human Driver Choice Modelmentioning

confidence: 99%

“…If the set of Nash Equilibria contains a routing with positive flow only on roads [m], then there exists a routing in , fraction of autonomous cars that will not tolerate latency , multiple of lowest latency 1 κ We define the set of Best-case Nash Equilibria (BNE) as the set of feasible routings in equilibrium that minimize the total latency for flow demand (λ h , λ a ), denoted BNE(λ h , λ a ). The following theorem (proof deferred to the appendix) provides properties of the set of BNE for mixed-autonomy roads (for roads with a single vehicle type, see [23]).…”

Section: Performance Benchmarksmentioning

confidence: 99%

“…However, the approaches taken in these works cannot incorporate a FDTbased model for road congestion, in which latency is no longer an increasing function of vehicle flow on a road. Also relatedly, [23] considers a Stackelberg game (meaning a planner can route a portion of the vehicle flow) on parallel roads with flow dictated by the FDT when there is a single vehicle type. Hence, a major novelty of our work lies in that we consider a mixed-autonomy network where autonomous service users have different preferences.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Incentivizing Efficient Equilibria in Traffic Networks with Mixed Autonomy

Bıyık¹,

Lazar²,

Pedarsani³

et al. 2021

Preprint

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Traffic congestion has large economic and social costs. The introduction of autonomous vehicles can potentially reduce this congestion by increasing road capacity via vehicle platooning and by creating an avenue for influencing people's choice of routes. We consider a network of parallel roads with two modes of transportation: (i) human drivers, who will choose the quickest route available to them, and (ii) a ride hailing service, which provides an array of autonomous vehicle route options, each with different prices, to users. We formalize a model of vehicle flow in mixed autonomy and a model of how autonomous service users make choices between routes with different prices and latencies. Developing an algorithm to learn the preferences of the users, we formulate a planning optimization that chooses prices to maximize a social objective. We demonstrate the benefit of the proposed scheme by comparing the results to theoretical benchmarks which we show can be efficiently calculated.

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Section: A Function Of the Density (N Hmentioning

confidence: 99%

“…As shown in Fig. 2, equilibria may have one road in free-flow and rest congested, or all may be congested [23], [30].…”

Section: Human Driver Choice Modelmentioning

confidence: 99%

Section: Performance Benchmarksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Incentivizing Efficient Equilibria in Traffic Networks with Mixed Autonomy

Bıyık¹,

Lazar²,

Pedarsani³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…While the focus on parallel road networks may appear restrictive, such networks are of theoretical and practical significance. For instance, Pigou's network [9], a two-arc parallel network, has been used extensively in the selfish routing literature to study price of anarchy bounds [10] and more general parallel networks have been used to study Stackelberg routing strategies [11]. More broadly, analogues of parallel networks have been considered in several online resource allocation problems, including online knapsack [12] and job scheduling [13].…”

Section: Introductionmentioning

confidence: 99%

Online Traffic Routing: Deterministic Limits and Data-driven Enhancements

Jalota,

Paccagnan,

Schiffer

et al. 2021

Preprint

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Over the past decade, GPS enabled traffic applications, such as Google Maps and Waze, have become ubiquitous and have had a significant influence on billions of daily commuters' travel patterns. A consequence of the online route suggestions of such applications, e.g., via greedy routing, has often been an increase in traffic congestion since the induced travel patterns may be far from the system optimum. Spurred by the widespread impact of traffic applications on travel patterns, this work studies online traffic routing in the context of capacity-constrained parallel road networks and analyzes this problem from two perspectives. First, we perform a worst-case analysis to identify the limits of deterministic online routing and show that the ratio between the total travel cost of the online solution of any deterministic algorithm and that of the optimal offline solution is unbounded, even in simple settings. This result motivates us to move beyond worst-case analysis. Here, we consider algorithms that exploit knowledge of past problem instances and show how to design data-driven algorithms whose performance can be quantified and formally generalized to unseen future instances. We present numerical experiments based on an application case for the San Francisco Bay Area to evaluate the performance of our approach. Our results show that the data-driven algorithms we develop outperform commonly used greedy online-routing algorithms.

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Altruistic Autonomy: Beating Congestion on Shared Roads

Bıyık

Lazar

Pedarsani

et al. 2020

Springer Proceedings in Advanced Robotics

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Traffic congestion has large economic and social costs. The introduction of autonomous vehicles can potentially reduce this congestion, both by increasing network throughput and by enabling a social planner to incentivize users of autonomous vehicles to take longer routes that can alleviate congestion on more direct roads. We formalize the effects of altruistic autonomy on roads shared between human drivers and autonomous vehicles. In this work, we develop a formal model of road congestion on shared roads based on the fundamental diagram of traffic. We consider a network of parallel roads and provide algorithms that compute optimal equilibria that are robust to additional unforeseen demand. We further plan for optimal routings when users have varying degrees of altruism. We find that even with arbitrarily small altruism, total latency can be unboundedly better than without altruism, and that the best selfish equilibrium can be unboundedly better than the worst selfish equilibrium. We validate our theoretical results through microscopic traffic simulations and show average latency decrease of a factor of 4 from worst-case selfish equilibrium to the optimal equilibrium when autonomous vehicles are altruistic.

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Stackelberg Routing on Parallel Transportation Networks

Cited by 17 publications

References 36 publications

Incentivizing Efficient Equilibria in Traffic Networks with Mixed Autonomy

Incentivizing Efficient Equilibria in Traffic Networks with Mixed Autonomy

Online Traffic Routing: Deterministic Limits and Data-driven Enhancements

Altruistic Autonomy: Beating Congestion on Shared Roads

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