Green Route: An Ecofriendly Route Suggestion and Description Based on Congestion and Air quality
Neena Thomas1, Athira Balagopal2, Surekha Mariam Varghese3
1Neena Thomas, Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam (Kerala), India.
2Athira Balagopal, Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam (Kerala), India.
3Surekha Mariam Varghese, Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam (Kerala), India.
Manuscript received on 07 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 414-419 | Volume-8 Issue-5, March 2019 | Retrieval Number: E3037038519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Travelling is a part of our daily life. We all travel from one place to another. The ultimate aim of people is to reach the destination as soon as possible. In wide-scale urbanization processes the route planning is the problem, whilst addressed thoroughly for a single traveler in terms of shortest path computation, becomes quickly unwieldy when dealing with a set of travelers. Smart cities and developed countries are now taking efforts to tackle urban pollution and current travel planners concentrate mainly on travel time and distance to be covered. Here, in addition to distance, air quality and road congestion that affect travel time, crowd sourcing is also considered for finding the best and healthier route. Ant colony optimization is used to select among different routes also used to find optimal route considering the air quality, congestion, distance are used as the parameters for pheromone updation. Here we provide a congestion-less and eco-friendlier route with the help of Google map API and ant colony optimization by exploiting feedback-driven on participation and route suggestions from personal interest. In this sense, collective intelligence and swarm based paradigms are adapted to an innovative crowd sourcing pattern towards the data storage is cloud. In particular the stigmergic algorithm for probabilistic route planning, including the distributed crowd sourcing paradigm based on number of participants, has been used to find the optimal path finding using ACO (ant colony optimization).
Keyword: Traffic Congestion, GSM, DTSP, ACO (Ant Colony Optimization).
Scope of the Article: Aspect-Based Software Engineering