A scalable non-myopic atomic game for a smart parking mechanism

Abstract We propose a novel non-myopic smart parking mechanism which aims to decrease the cruising time spent in searching for parking, with the assumption of elastic demand for both on-street parking lots and parking garages. A non-myopic atomic game is formulated to address competition for parking through assignment of vehicles to candidate parking facilities that takes into account the differences in travel times for the vehicles from their point of origin to the parking facilities and the differences in walking times for the drivers from the parking facilities to their final destination, as well as dynamic pricing, cruising times, and occupancies of the parking facilities. This study integrates a socially efficient price that accounts for the waiting times of drivers in their search for parking. We incorporate a game model into the social optimum problem by considering the competition of drivers for parking spaces where the drivers’ preferences are reflected in a collective decision such as social welfare. Using actual parking data for the city of San Francisco, we found that under our proposed dynamic parking system the average social welfare per vehicle improved by up to 54% compared to other parking strategies.

[1]  Carl M. Harris,et al.  Fundamentals of queueing theory , 1975 .

[2]  Niels Chr. Knudsen.  Individual and Social Optimization in a Multiserver Queue with a General Cost-Benefit Structure , 1972 .

[3]  Ioannis Stavrakakis,et al.  Leveraging Information in Parking Assistance Systems , 2013, IEEE Transactions on Vehicular Technology.

[4]  Reijo Sulonen,et al.  Non-myopic vehicle and route selection in dynamic DARP with travel time and workload objectives , 2012, Comput. Oper. Res..

[5]  Amnon Rapoport,et al.  The price of anarchy in social dilemmas: Traditional research paradigms and new network applications , 2013 .

[6]  P. Naor.  The Regulation of Queue Size by Levying Tolls , 1969 .

[7]  Ronnie Schöb,et al.  Alleviating Urban Traffic Congestion , 2005 .

[8]  Refael Hassin,et al.  To Queue or Not to Queue: Equilibrium Behavior in Queueing Systems , 2002 .

[9]  Yafeng Yin,et al.  Parking Reservation for Managing Downtown Curbside Parking , 2015 .

[10]  Panta Lucic,et al.  Intelligent parking systems , 2006, Eur. J. Oper. Res..

[11]  Fang He,et al.  Pricing of parking games with atomic players , 2015 .

[12]  Caroline J Rodier,et al.  Transit-based smart parking: An evaluation of the San Francisco Bay area field test , 2010 .

[13]  Ronald W. Wolff,et al.  Stochastic Modeling and the Theory of Queues , 1989 .

[14]  Antonio Origlia,et al.  Smart Parking: Using a Crowd of Taxis to Sense On-Street Parking Space Availability , 2020, IEEE Transactions on Intelligent Transportation Systems.

[15]  Yanfeng Ouyang,et al.  Dynamic pricing and reservation for intelligent urban parking management , 2017 .

[16]  K. R. Krishnan.  Joining the right queue: a state-dependent decision rule , 1990 .

[17]  Emmanouil Chaniotakis,et al.  Drivers’ parking location choice under uncertain parking availability and search times: A stated preference experiment , 2015 .

[18]  Ouri Wolfson,et al.  A mechanism design based approach to solving parking slot assignment in the information era , 2015 .

[19]  C. Fisk.  Some developments in equilibrium traffic assignment , 1980 .

[20]  Xiaoning Zhang,et al.  Optimal parking supply in bi-modal transportation network considering transit scale economies , 2019, Transportation Research Part E: Logistics and Transportation Review.

[21]  Christos G. Cassandras,et al.  New “Smart Parking” System Based on Resource Allocation and Reservations , 2013, IEEE Transactions on Intelligent Transportation Systems.

[22]  Hamid R. Sayarshad,et al.  A non-myopic dynamic inventory routing and pricing problem , 2018 .

[23]  Felix Caicedo.  Real-time parking information management to reduce search time, vehicle displacement and emissions , 2010 .

[24]  Adel W. Sadek,et al.  Modeling Parking Behavior Under Uncertainty: A Static Game Theoretic versus a Sequential Neo-additive Capacity Modeling Approach , 2013 .

[25]  Lili Du,et al.  Stochastic Poisson game for an online decentralized and coordinated parking mechanism , 2016 .

[26]  D. Shoup.  Cruising for Parking , 2006 .

[27]  Qiang Meng,et al.  Allocation of street parking facilities in a capacitated network with equilibrium constraints on drivers’ traveling and cruising for parking , 2019, Transportation Research Part C: Emerging Technologies.

[28]  Eiichi Taniguchi,et al.  Modeling city logistics using adaptive dynamic programming based multi-agent simulation , 2019, Transportation Research Part E: Logistics and Transportation Review.

[29]  Xianhao Xu,et al.  Optimal design and planning for compact automated parking systems , 2019, Eur. J. Oper. Res..

[30]  H. Oliver Gao,et al.  Non-myopic dynamic routing of electric taxis with battery swapping stations , 2020 .

[31]  Jasbir S. Arora,et al.  Survey of multi-objective optimization methods for engineering , 2004 .

[32]  Lillian J. Ratliff,et al.  Gaussian Mixture Models for Parking Demand Data , 2020, IEEE Transactions on Intelligent Transportation Systems.

[33]  Warren B. Powell,et al.  “Approximate dynamic programming: Solving the curses of dimensionality” by Warren B. Powell , 2007, Wiley Series in Probability and Statistics.

[34]  H. Oliver Gao,et al.  A scalable non-myopic dynamic dial-a-ride and pricing problem for competitive on-demand mobility systems , 2018, Transportation Research Part C: Emerging Technologies.

[35]  Ram Rajagopal,et al.  Optimal occupancy-driven parking pricing under demand uncertainties and traveler heterogeneity: A stochastic control approach , 2014 .