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An Improved Ant Colony Optimization Based Parking Algorithm with Graph Coloring

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 451)

Abstract

In an era which is going towards the spread of smart cities and intelligent transportation systems, vehicular ad hoc networks are an interesting framework to propose innovative solutions. One of the most tedious problems for drivers in a urban environment is the parking process. Indeed, drivers looking for an available parking slot keep being the main cause of traffic congestion, which also involves a high stress and air pollution level. In this work, we provide a smart parking solution, aiming at a higher context awareness for drivers, by relying on a well known optimization problem, the ant colony. By choosing an opportune criterion to update the pheromone, we push drivers to choose possibly uncrowded paths, ending up with a solution which guarantees a fair node distribution with respect to the available parking slots.

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  • DOI: 10.1007/978-3-030-99619-2_8
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Acknowledgments

This paper has been produced with the financial support of the Justice Programme of the European Union, 101046629 CREA2, JUST-2021-EJUSTICE, JUST2027 Programme. The contents of this report are the sole responsibility of the authors and can in no way be taken to reflect the views of the European Commission.

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Correspondence to Walter Balzano .

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Agizza, M., Balzano, W., Stranieri, S. (2022). An Improved Ant Colony Optimization Based Parking Algorithm with Graph Coloring. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 451. Springer, Cham. https://doi.org/10.1007/978-3-030-99619-2_8

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