Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Clustered swarm: a live swarm-based traffic load balancing algorithm against traffic jams

© The Institution of Engineering and Technology
Received 31/01/2016, Accepted 12/08/2016, Revised 17/06/2016, Published 23/08/2016

Traffic jam. A phenomenon representing one of the biggest and most interesting problems of the 21st century. More than 60% are caused by too many vehicles using the common resource ‘road’. General countermeasures and individual route calculation approaches could not solve the problem until now and can even cause traffic jams under certain circumstances. Current research and developments in the area of mobile communication and car-IT like vehicle to vehicle communication open up new perspectives in the fight against the expected traffic gridlock. This study introduces a concept and a first implementation of a live swarm-based algorithm which pursues the aim of a global traffic optimisation by performing a massive load balancing of all road participants to improve the individual routes for the users and thereby represents a potential solution for the traffic jam problem.

Inspec keywords: optimisation; resource allocation; traffic engineering computing; road traffic

Other keywords: clustered swarm; live swarm-based traffic load balancing algorithm; mobile communication; traffic jam problem; vehicle to vehicle communication; global traffic optimisation

Subjects: Optimisation techniques; Traffic engineering computing

References

    1. 1)
      • 16. Leutzbach, W.: ‘Einführung in die Theorie des Verkehrsflusses’ (Springer Verlag, 1972).
    2. 2)
      • 19. Kühne, R.D., Langbein, K.: ‘Optimierung der Parameter einer Linienbeeinflussungsanlage’, Tagungsbericht Heureka, 1993, 93, pp. 116–139.
    3. 3)
      • 12. Cisco Systems: ‘ Cisco visual networking index: global mobile data traffic forecast update, 2013–2018’, White Paper, 2014.
    4. 4)
      • 5. Central Intelligence Agency: ‘Country comparison: roadways’, The world factbook. https://www.cia.gov/library/publications/the-world-factbook/rankorder/2085rank.html, accessed July 2015.
    5. 5)
      • 17. Zeng, W., Church, R.L.: ‘Finding shortest paths on real road networks: the case for A*’, Int. J. Geograph. Inf. Sci., 2009, 23, (4), pp. 531–543.
    6. 6)
      • 11. Vinel, A.: ‘3GPP LTE versus IEEE 802.11p/WAVE: which technology is able to support cooperative vehicular safety applications’, IEEE Wirel. Commun. Lett., 2012, 1, (2), pp. 125–128.
    7. 7)
      • 1. Center for Economics and Business Research: ‘The future economic and environmental costs of gridlock in 2030’, report for INRIX, 2014.
    8. 8)
      • 10. Encyclopedia.com: ‘fail-soft’, http://www.encyclopedia.com/doc/1O11-failsoft.html, accessed the 17.01.2015.
    9. 9)
      • 6. Statista – The Statistics Portal: ‘Ranking of the countries with the highest road quality in 2013/14’, http://www.statista.com/statistics/268157/ranking-of-the-20-countries-with-the-highest-road-quality/, accessed July 2015.
    10. 10)
      • 8. Duranton, G., Turner, M.A.: ‘The fundamental law of road congestion: evidence from US cities’, Am. Economic Rev., 2011, 101, pp. 2616–2652.
    11. 11)
      • 3. Bundesanstalt für Straßenwesen (BASt): ‘Projektplan Straßenverkehrstelematik’, http://www.bast.de/DE/FB-V/Fachthemen/v5-verkehrsbeeinflussungsanlagen/verkehrsbeeinflussungsanlagen.html?nn=605096, accessed July 2015.
    12. 12)
      • 14. Strategy & formerly Booz & Company: ‘In the fast lane – The bright future of connected cars’, 2014.
    13. 13)
      • 15. Kennedy, J., Eberhart, R.C.: ‘Swarm intelligence’ (Morgan Kaufmann, 1997).
    14. 14)
      • 18. Cormen, T.H., Stein, C., Leiserson, C.E., et al: ‘Introduction to algorithms’ (MIT Press, 2001, 2nd edn.).
    15. 15)
      • 4. Nash, J.: ‘Non-Cooperative Games’. Dissertation, Faculty of Princeton University in Candidacy for the Degree of Doctor of Philosophy, 1950.
    16. 16)
      • 7. Downs, A.: ‘The law of peak-hour expressway congestion’, Traffic Quarterly, 16, (3), 1962, pp. 393–409.
    17. 17)
      • 13. Jiang, D., Delgrossi, L.: ‘IEEE 802.11p: towards an international standard for wireless access in vehicular environments’. IEEE Vehicular Technology Conf. (VTC), 2008.
    18. 18)
      • 22. ‘GDF 3.0 Reference Manual v42.0’, Nokia, April 2012.
    19. 19)
      • 21. Statista – The Statistics Portal: ‘Wie of nutzen Sie Ihr Pkw- Navigationssystem’, http://www.de.statista.com/statistik/daten/studie/180738/umfrage/nutzungshaeufigkeit-von-pkw-navigationssystemen-in-deutschland/, accessed January 2016.
    20. 20)
      • 20. Nagel, K., Schreckenberg, M.: ‘A cellular automaton model for freeway traffic’, J. Phys. I France, 1992, 2, pp. 2221–2229.
    21. 21)
      • 2. Bundesministerium für Verkehr, Bau und Stadtentwicklung (BMVBS): ‘Projektplan Straßenverkehrstelematik 2015’, BMVBS, Referat 12, 2011.
    22. 22)
      • 9. Statista – The Statistics Portal: ‘Anteil der häufigsten Ursachen für Stau auf deutschen Fernstraßen im Jahr 2008’, http://www.de.statista.com/statistik/daten/studie/12684/umfrage/anteil-der-haeufigsten-stauursachen-auf-deutschen-fernstrassen/, accessed July 2015.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2016.0031
Loading

Related content

content/journals/10.1049/iet-its.2016.0031
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address