Latest revision as of 06:42, 2 February 2021

Abstract

Traffic congestion is always a major issue in urban planning, especially when the vehicles in the roadway keep growing and the local authorities are lack of solutions to manage or distribute the traffics in the city. Although there are several factors that may cause traffic congestion, inefficiency in traffic management is always the main issue. Additionally, the most traditional methods of resolving traffic congestion or rerouting algorithm are mainly designed for individuals' benefits, by simply planning a driver's route based on minimum travel time or shortest path accordingly. There is lack of consideration in group benefit or urban development. However, with the development of technologies in Internet of Things (IoT), vehicle to vehicle (V2V) or Vehicle to Infrastructure (V2I) communications, group based routing becomes achievable. Instead of optimizing the routing path for individual drivers, this paper studies how to develop a new method to provide new routing method based on vehicles' similarities in a specific urban's transportation environment.

Original document

The different versions of the original document can be found in:

• http://researchonline.ljmu.ac.uk/6604/1/Study%20of%20Group%20Route%20Optimization%20for%20IoT%20enabled%20Urban%20Transportation%20Network.pdf under the license http://www.rioxx.net/licenses/all-rights-reserved

• http://researchonline.ljmu.ac.uk/id/eprint/6604/1/Study%20of%20Group%20Route%20Optimization%20for%20IoT%20enabled%20Urban%20Transportation%20Network.pdf

• http://xplorestaging.ieee.org/ielx7/8275085/8276662/08276856.pdf?arnumber=8276856,

http://dx.doi.org/10.1109/ithings-greencom-cpscom-smartdata.2017.137 under the license http://www.rioxx.net/licenses/all-rights-reserved

• https://dblp.uni-trier.de/db/conf/ithings/ithings2017.html#SangZYY17,

https://academic.microsoft.com/#/detail/2786866020

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