Vehicle-to-vehicle-to-infrastructure (V2V2I) intelligent transportation system architecture

In this paper, I describe the vehicle-to-vehicle-to-infrastructure (V2V2I) architecture, which is a hybrid of the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) architectures. The V2V2I architecture leverages the benefits of fast queries and responses from the V2I architecture, but with the advantage of a distributed architecture not having a single point-of-failure from the V2V architecture. In the V2V2I architecture, the transportation network is broken into zones in which a single vehicle is known as the super vehicle. Only super vehicles are able to communicate with the central infrastructure or with other Super Vehicles, and all other vehicles can only communicate with the super vehicle responsible for the zone in which they are currently traversing. I describe the super vehicle detection (SVD) algorithm for how a vehicle can find or become a super vehicle of a zone and how super vehicles can aggregate the speed and location data from all of the vehicles within their zone to still ensure an accurate representation of the network. I perform an analysis using FreeSim to determine the trade-offs experienced based on the size and number of zones within a transportation network and describe the benefits of the V2V2I architecture over the pure V2I or V2V architectures.

[1]  Máire O'Neill,et al.  MONET Special Issue on Next Generation Hardware Architectures for Secure Mobile Computing , 2007, Mob. Networks Appl..

[2]  Michel Pasquier,et al.  POP-TRAFFIC: a novel fuzzy neural approach to road traffic analysis and prediction , 2006, IEEE Transactions on Intelligent Transportation Systems.

[3]  Donald B. Johnson,et al.  Efficient Algorithms for Shortest Paths in Sparse Networks , 1977, J. ACM.

[4]  John Rice,et al.  Accurate estimation of travel times from single-loop detectors 1 1 Funding for this research was pro , 1998 .

[5]  Ellis Horowitz,et al.  FreeSim - a free real-time freeway traffic simulator , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[6]  Raúl Aquino-Santos,et al.  A location-based routing algorithm for vehicle to vehicle communication , 2004, Proceedings. 13th International Conference on Computer Communications and Networks (IEEE Cat. No.04EX969).

[7]  M.G.H. Bell,et al.  Reliable pre-trip multi-path planning and dynamic adaptation for a centralized road navigation system , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[8]  T. Lindvall ON A ROUTING PROBLEM , 2004, Probability in the Engineering and Informational Sciences.

[9]  D. R. Fulkerson,et al.  Flows in Networks. , 1964 .

[10]  Giuseppe F. Italiano,et al.  A new approach to dynamic all pairs shortest paths , 2003, STOC '03.

[11]  Daiheng Ni Determining Traffic-Flow Characteristics by Definition for Application in ITS , 2007, IEEE Transactions on Intelligent Transportation Systems.

[12]  Ellis Horowitz,et al.  FreeSim – A V2V and V2R Freeway Traffic Simulator , 2007 .

[13]  Eleni I. Vlahogianni,et al.  Pattern-Based Short-Term Urban Traffic Predictor , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[14]  Azim Eskandarian,et al.  A Reliable Link-Layer Protocol for Robust and Scalable Intervehicle Communications , 2007, IEEE Transactions on Intelligent Transportation Systems.

[15]  Ellis Horowitz,et al.  Algorithms for real-time gathering and analysis of continuous-flow traffic data , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[16]  Chao Chen,et al.  The PeMS algorithms for accurate, real-time estimates of g-factors and speeds from single-loop detectors , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[17]  Klaus Bogenberger,et al.  Reliable Pretrip Multipath Planning and Dynamic Adaptation for a Centralized Road Navigation System , 2007, IEEE Transactions on Intelligent Transportation Systems.

[18]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.