Analyzing Location-Based Advertising for Vehicle Service Providers Using Effective Resistances

Vehicle service providers can display commercial ads in their vehicles based on passengers' origins and destinations to create a new revenue stream. We study a vehicle service provider who can generate different ad revenues when displaying ads on different arcs (i.e., origin-destination pairs). The provider needs to ensure the vehicle flow balance at each location, which makes it challenging to analyze the provider's vehicle assignment and pricing decisions for different arcs. To tackle the problem, we show that certain properties of the traffic network can be captured by a corresponding electrical network. When the effective resistance between two locations is small, there are many paths between the two locations and the provider can easily route vehicles between them. We derive the provider's optimal vehicle assignment and pricing decisions based on effective resistances.

[1]  Lin Gao,et al.  Public Wi-Fi Monetization via Advertising , 2017, IEEE/ACM Transactions on Networking.

[2]  Songwu Lu,et al.  Secure Incentives for Commercial Ad Dissemination in Vehicular Networks , 2007, IEEE Transactions on Vehicular Technology.

[3]  Francesco Bullo,et al.  Electrical Networks and Algebraic Graph Theory: Models, Properties, and Applications , 2018, Proceedings of the IEEE.

[4]  Vincent Jost,et al.  Vehicle Sharing System Pricing Regulation: A Fluid Approximation , 2012 .

[5]  Ermin Wei,et al.  Analyzing Location-Based Advertising for Vehicle Service Providers Using Effective Resistances , 2019, ArXiv.

[6]  Wayne L. Winston,et al.  Optimal price skimming by a monopolist facing rational consumers , 1990 .

[7]  Enrico Bozzo,et al.  Effective and efficient approximations of the generalized inverse of the graph Laplacian matrix with an application to current-flow betweenness centrality , 2012, ArXiv.

[8]  Lin Zhang,et al.  Bus-Ads: Bus Trajectory-Based Advertisement Distribution in VANETs Using Coalition Formation Games , 2017, IEEE Systems Journal.

[9]  Thodoris Lykouris,et al.  Pricing and Optimization in Shared Vehicle Systems: An Approximation Framework , 2016, EC.

[10]  Adam Wierman,et al.  Prices and Subsidies in the Sharing Economy , 2016, WWW.

[11]  Ricardo Lagos.  An Alternative Approach to Search Frictions , 2000, Journal of Political Economy.

[12]  Xuying Zhao,et al.  Economic Analysis of Reward Advertising , 2017, Production and Operations Management.

[13]  Chia-Wei Kuo,et al.  Dynamic Pricing of Limited Inventories When Customers Negotiate , 2011, Oper. Res..

[14]  Haiyan Chen,et al.  Random walks and the effective resistance sum rules , 2010, Discret. Appl. Math..

[15]  Carlos Riquelme,et al.  Pricing in Ride-Sharing Platforms: A Queueing-Theoretic Approach , 2015, EC.

[16]  Jie Wu,et al.  Optimizing Roadside Advertisement Dissemination in Vehicular Cyber-Physical Systems , 2015, 2015 IEEE 35th International Conference on Distributed Computing Systems.

[17]  Lei Ying,et al.  Empty-Car Routing in Ridesharing Systems , 2016, Oper. Res..

[18]  Kostas Bimpikis,et al.  Spatial Pricing in RideSharing Networks , 2017 .

[19]  Zhi-Li Zhang,et al.  Incremental Computation of Pseudo-Inverse of Laplacian , 2013, COCOA.

[20]  David C. Parkes,et al.  Spatio-Temporal Pricing for Ridesharing Platforms , 2018, EC.

[21]  Ermin Wei,et al.  A Business Model Analysis of Mobile Data Rewards , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[22]  Sergei Vassilvitskii,et al.  To Match or Not to Match , 2015, ACM Trans. Economics and Comput..

[23]  Kostas Bimpikis,et al.  Spatial pricing in ride-sharing networks , 2016, NetEcon@EC.

[24]  Minglu Li,et al.  POST: Exploiting Dynamic Sociality for Mobile Advertising in Vehicular Networks , 2016, IEEE Trans. Parallel Distributed Syst..