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Fine Tuning of Traffic in our Cities with Smart Panels: The Quito City Case Study

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Kiyoshi TanakaAuthors Info & Claims

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

Pages 1013 - 1019

https://doi.org/10.1145/2908812.2908868

Published: 20 July 2016 Publication History

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Abstract

In this article we work towards the desired future smart city in which IT and knowledge will hopefully provide a highly livable environment for citizens. To this end, we test a new concept based on intelligent LED panels (the Yellow Swarm) to guide drivers when moving through urban streets so as to finally get rid of traffic jams and protect the environment. This is a minimally invasive, low cost idea for the city that needs advanced simulations with real data coupled with new algorithms which perform well. Our proposal is to use evolutionary computation in the Yellow Swarm, which will finally help alleviate the traffic congestion, improve travel times, and decrease gas emissions, all at the same time and for a real case like the city of Quito (Ecuador).

References

[1]

R. Armas, H. Aguirre, S. Zapotecas-Martinez, and K. Tanaka. Traffic Signal Optimization: Minimizing Travel Time and Fuel Consumption. In Artificial Evolution, pages 29--43. Springer, 2015.

Digital Library

Google Scholar

[2]

P. P. Dubey and P. Borkar. Review on techniques for traffic jam detection and congestion avoidance. In 2nd International Conference on Electronics and Communication Systems, ICECS 2015, pages 434--439. IEEE, 2015.

Crossref

Google Scholar

[3]

R. Gakenheimer. Urban mobility in the developing world. Transportation Research Part A: Policy and Practice, 33(7--8):671--689, 1999.

Google Scholar

[4]

D. Krajzewicz, J. Erdmann, M. Behrisch, and L. Bieker. Recent Development and Applications ofSUMO - Simulation of Urban MObility. International Journal On Advances in Systems and Measurements, 5(3):128--138, 2012.

Google Scholar

[5]

G. Laporte. Scheduling issues in vehicle routing. Annals of Operations Research, pages 1--12, 2013.

Google Scholar

[6]

S. Lin, B. De Schutter, Y. Xi, and H. Hellendoorn. Integrated urban traffic control for the reduction of travel delays and emissions. IEEE Transactions on Intelligent Transportation Systems, 14(4):1609--1619, 2013.

Digital Library

Google Scholar

[7]

J. B. Rawlings and D. Q. Mayne. Postface to "Model Predictive Control: Theory and Design". Nob Hill Pub., 2012.

Google Scholar

[8]

D. H. Stolfi and E. Alba. Red Swarm: Reducing travel times in smart cities by using bio-inspired algorithms. Applied Soft Computing Journal, 24(0):181--195, nov 2014.

Digital Library

Google Scholar

[9]

D. H. Stolfi and E. Alba. Smart Mobility Policies with Evolutionary Algorithms. In Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15, GECCO '15, pages 1287--1294, New York, NY, USA, 2015. ACM.

Digital Library

Google Scholar

[10]

Y. Sugiyama, M. Fukui, M. Kikuchi, K. Hasebe, A. Nakayama, K. Nishinari, S.-i. Tadaki, and S. Yukawa. Traffic jams without bottlenecks -- experimental evidence for the physical mechanism of the formation of a jam. New J. Phys., 10(3):33001, 2008.

Crossref

Google Scholar

[11]

D. Washburn, U. Sindhu, S. Balaouras, R. A. Dines, N. M. Hayes, and L. E. Nelson. Helping CIOs Understand "Smart City" Initiatives. Growth, 17:2, 2010.

Google Scholar

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Herrera-Herrera NMorillo ACarvajal EToasa R(2023)Obtaining, Analysis and Visualization of Mobility and Traffic data in Quito – Ecuador2023 IEEE Seventh Ecuador Technical Chapters Meeting (ECTM)10.1109/ETCM58927.2023.10309083(1-6)Online publication date: 10-Oct-2023
https://doi.org/10.1109/ETCM58927.2023.10309083
Stolfi DAlba E(2021)Yellow SwarmApplied Soft Computing10.1016/j.asoc.2021.107566109:COnline publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1016/j.asoc.2021.107566
Del Ser JOsaba ESanchez-Medina JFister IFister I(2020)Bioinspired Computational Intelligence and Transportation Systems: A Long Road AheadIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.289737721:2(466-495)Online publication date: Feb-2020
https://doi.org/10.1109/TITS.2019.2897377
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Fine Tuning of Traffic in our Cities with Smart Panels: The Quito City Case Study

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Published In

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

July 2016

1196 pages

ISBN:9781450342063

DOI:10.1145/2908812

Editor:
Tobias Friedrich
Hasso Plattner Institute
,
General Chair:
Frank Neumann
University of Adelaide
,
Program Chair:
Andrew M. Sutton
Hasso Plattner Institute

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 20 July 2016

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GECCO '16: Genetic and Evolutionary Computation Conference

July 20 - 24, 2016

Colorado, Denver, USA

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GECCO '16 Paper Acceptance Rate 137 of 381 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Herrera-Herrera NMorillo ACarvajal EToasa R(2023)Obtaining, Analysis and Visualization of Mobility and Traffic data in Quito – Ecuador2023 IEEE Seventh Ecuador Technical Chapters Meeting (ECTM)10.1109/ETCM58927.2023.10309083(1-6)Online publication date: 10-Oct-2023
https://doi.org/10.1109/ETCM58927.2023.10309083
Stolfi DAlba E(2021)Yellow SwarmApplied Soft Computing10.1016/j.asoc.2021.107566109:COnline publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1016/j.asoc.2021.107566
Del Ser JOsaba ESanchez-Medina JFister IFister I(2020)Bioinspired Computational Intelligence and Transportation Systems: A Long Road AheadIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.289737721:2(466-495)Online publication date: Feb-2020
https://doi.org/10.1109/TITS.2019.2897377
Yamin Siddiqui SAdnan Khan MAbbas SKhan F(2020)Smart Occupancy Detection for Road Traffic Parking using Deep Extreme Learning MachineJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2020.01.016Online publication date: Feb-2020
https://doi.org/10.1016/j.jksuci.2020.01.016
Camero AToutouh JStolfi DAlba E(2018)Evolutionary Deep Learning for Car Park Occupancy Prediction in Smart CitiesLearning and Intelligent Optimization10.1007/978-3-030-05348-2_32(386-401)Online publication date: 31-Dec-2018
https://doi.org/10.1007/978-3-030-05348-2_32
D'Orazio LHalfeld-Ferrari MHara CKozievitch NMusicante M(2017)Graph Constraints in Urban Computing: Dealing with Conditions in Processing Urban Data2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)10.1109/iThings-GreenCom-CPSCom-SmartData.2017.171(1118-1124)Online publication date: Jun-2017
https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.171

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Eco-friendly reduction of travel times in european smart cities

An Intelligent Advisor for City Traffic Policies

Computing new optimized routes for GPS navigators using evolutionary algorithms

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