To observe or not to observe: Queuing game framework for urban parking

2018 Annual American Control Conference (ACC)

et al. 2018

Self Cite

We study the equilibrium behavior in a multicommodity 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.

Section: A Proof Of Lemmamentioning

confidence: 78%

Section: B Impact Of Uncertain Users On Users Without Uncertaintymentioning

confidence: 99%

Uncertainty in Multi-Commodity Routing Networks: When does it help?

Sekar

Zheng

2018 Annual American Control Conference (ACC)

et al. 2018

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“…7b, farthest west red block-face along the waterfront in Fig. 10), 2) block-face 185 is on the boundary of an isolated section 6 See github.com/cpatdowling/net-queue/data/ simulation/belltownsims/belltowndata/data_notes.txt for additional information Fig. 10: Locations of average occupancy error between data and simulation across weekdays, March -June 2016 Fig.…”

Section: Monte Carlo Simulation Of Rate Parametersmentioning

confidence: 99%

“…Further, in transportation literature, cruising for parking also results from drivers searching for an available curbside parking space to avoid garage prices [4]. In the aggregate this behavior creates potentially significant congestion [5], but city planners have until recently lacked high resolution (block-face by blockface, per hour) models of such costs [6]. Congestion caused by drivers cruising for parking is non-trivial, historically cited as composing up to 30% of through-traffic [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Modeling Curbside Parking as a Network of Finite Capacity Queues

Dowling

IEEE Trans. Intell. Transport. Syst.

Zhang

2020

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With the increasing availability of transaction data collected by digital parking meters, paid curbside parking can be advantageously modeled as a network of interdependent queues. In this article we introduce methods for analyzing a special class of networks of finite capacity queues, where tasks arrive from an exogenous source, join the queue if there is an available server or are rejected and move to another queue in search of service according to the network topology. Such networks can be useful for modeling curbside parking since queues in the network perform the same function and drivers searching for an available server are under combinatorial constraints and jockeying is not instantaneous. Further, we provide a motivating example for such networks of finite capacity queues in the context of drivers searching for parking in the neighborhood of Belltown in Seattle, Washington, USA. Lastly, since the stationary distribution of such networks used to model parking are difficult to satisfactorily characterize, we also introduce a simulation tool for the purpose of testing the assumptions made to estimate interesting performance metrics. Our results suggest that a Markovian relaxation of the problem when solving for the mean rate metrics is comparable to deterministic service times reflective of a driver's tendency to park for the maximum allowable time.

“…Occupancy and other data lend themselves to discrete and probabilistic models that may potentially better reflect flow on surface streets as compared to flow on highways or through spatially homogeneous regions, as in [Arnott, 2006] and [Arnott and Rowse, 2009]. Hence, classical methods of queueing theory have recently been applied to parking areas: garage and curbside alike [Ratliff et al, 2016, Larson and Sasanuma, 2010, Caliskan et al, 2007, Ceballos and Curtis, 2004.…”

Section: Related Workmentioning

confidence: 99%

Optimizing curbside parking resources subject to congestion constraints

Dowling

Fiez

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

et al. 2017

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To gain theoretical insight into the relationship between parking scarcity and congestion, we describe block-faces of curbside parking as a network of queues. Due to the nature of this network, canonical queueing network results are not available to us. We present a new kind of queueing network subject to customer rejection due to the lack of available servers. We provide conditions for such networks to be stable, a computationally tractable "single node" view of such a network, and show that maximizing the occupancy through price control of such queues, and subject to constraints on the allowable congestion between queues searching for an available server, is a convex optimization problem. We demonstrate an application of this method in the Mission District of San Francisco; our results suggest congestion due to drivers searching for parking stems from an inefficient spatial utilization of parking resources.