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Urban Traffic Management Utilizing Soft Measures: A Case Study of Volos City

  • Maria Karatsoli
  • Ioannis Karakikes
  • Eftihia Nathanail
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 879)

Abstract

This paper examines the current and the future performance of the traffic network around the center of the city of Volos in Greece, after the implementation of local traffic management measures and the introduction of innovative Intelligent Transportation System (ITS) services.

The study focuses on the urban road of two main streets Iasonos (up to Fillelinon street) and Dimitriados (section between Fillelinon and Athanasiou Diakou streets) where during the peak hours, congestion results in high delays, bottlenecks and conflicts. System performance is based on specific indicators, which have been set to evaluate the traffic situation in the three main areas of interest: traffic quality, safety and environment.

An investigation on the current and potential problems of the study area has been performed, by modeling the current situation (base scenario) in the microsimulation software VISSIM and using the “Surrogate Safety Assessment Model” (SSAM) to assess the traffic safety. The findings were low quality of signal control, low compliance of drivers to traffic laws (illegal and unregulated parking, trespassing of the bus lane), critical safety hotspots and increased emissions. “Soft” countermeasures are simulated and evaluated in VISSIM. Such “soft” countermeasures are the ban of access to Urban Freight Transport (UFT) vehicles during the peak hours, the adoption of ITS to prevent illegal parking, the adjustment of the coordination time offset.

Apart from evaluating the impact of the countermeasures, the paper constitutes also a roadmap for achieving overall improvement of an urban traffic network without resulting into the construction of new transport infrastructure.

Keywords

Traffic flow Traffic safety Assessment Network performance 

References

  1. 1.
    Cairns, S., Sloman, L., Newson, C., Anable, J., Kirkbride, A., Goodwin, P.: Smarter choices: assessing the potential to achieve traffic reduction using “Soft Measures”. Transp. Rev. 28(5), 593–618 (2008)CrossRefGoogle Scholar
  2. 2.
    Möser, G., Bamberg, S.: The effectiveness of soft transport policy measures: a critical assessment and meta-analysis of empirical evidence. J. Environ. Psychol. 28(1), 10–26 (2008)CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Richter, J., Friman, M., Gärling, T.: Soft transport policy measures: gaps in knowledge. Int. J. Sustain. Transp. 5(4), 199–215 (2011)CrossRefGoogle Scholar
  5. 5.
    Medina, J., Moreno, M., Cabrera, M., Royo, E.: Traffic signals in traffic circles: simulation and optimization based efficiency study. In: International Conference on Computer Aided Systems Theory, pp. 453–460 (2009)Google Scholar
  6. 6.
    Nathanail, E., Hatziioannidou, F.: Microscopic simulation for the assessment of the sustainability of the transportation measures. In: 5th International Congress on Transportation Research in Greece, Volos, September 2010Google Scholar
  7. 7.
    Huang, F., Liu, P., Yu, H., Wang, W.: Identifying if VISSIM simulation model and SSAM provide reasonable estimates for field measured traffic conflicts at signalized intersections. Accid. Anal. Prev. 50, 1014–1024 (2013)CrossRefGoogle Scholar
  8. 8.
    Hellenic Statistical Authority. http://www.statistics.gr/en/home. Accessed 16 June 2017
  9. 9.
    ANEVO L.T.D. http://www.anevo.gr/active_article.php?id=43&cat=4. Accessed 18 Jan 2018
  10. 10.
    Greek Association of motor vehicle importers - representatives. http://www.seaa.gr/. Accessed 18 Aug 2017
  11. 11.
    Karakikes, I., Mitropoulos, L., Savrasovs, M.: Evaluating smart urban freight solutions using microsimulation. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds.) Reliability and Statistics in Transportation and Communication. RelStat 2017. Lecture Notes in Networks and Systems, vol. 36. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-74454-4_53
  12. 12.
    Municipal Bus Service Company. https://astikovolou.gr/. Accessed 12 Aug 2017
  13. 13.
    Karakikes, I., Spangler, M., Margreiter, M.: Motorway simulation using bluetooth data. Transp. Telecommun. J. 17(3), 242–251 (2016).  https://doi.org/10.1515/ttj-2016-0022CrossRefGoogle Scholar
  14. 14.
    Wisconsin Department of Transportation, Microsimulation Guidelines. http://www.wisdot.info/microsimulation/index.php?title=Model_Calibration#The_GEH_Formula. Accessed 30 May 2017

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringUniversity of ThessalyVolosGreece

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