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Microsimulation Modelling of the Impacts of Double-Parking Along an Urban Axis

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 879))

Abstract

Illegal parking, particularly in urban areas, can cause severe delays and contribute significantly to traffic congestion. The main objective of the present work is to assess, using microsimulation modeling, the impacts of the phenomenon of double parking along an urban axis. A case study of an urban axis of the city of Thessaloniki, that daily serves heavy traffic, with a dedicated bus lane, is presented. Two different scenarios are modelled; one where only legal on-street parking along the axis is considered and a second one, representing the actual situation, where also a number of double-parking events are modelled; and the interaction with the traffic the axis serves is studied. Data on vehicle speed, average travel time, delay and stopped time are compared and the results show that all traffic indicators are affected by the phenomenon of double parking. Additionally, energy consumption and emissions of air pollutants are also compared to assess the impacts of double parking on the environment. The findings assist in quantifying the impacts of double parking, highlighting the importance of enforcement and measures aimed at reducing and eventually eliminating illegal parking, in order to improve traffic conditions and the quality of the atmosphere along the axis and consequently upgrade the quality of life of its residents, employers and travelers.

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Acknowledgement

The present work has been implemented in the framework of the co-financed by the European Regional Development Fund project REMEDIO «Regenerating mixed – use MED urban communities congested by traffic through innovative low carbon solutions» of the Interreg MED programme.

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Correspondence to Katerina Chrysostomou .

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Chrysostomou, K., Petrou, A., Aifadopoulou, G., Morfoulaki, M. (2019). Microsimulation Modelling of the Impacts of Double-Parking Along an Urban Axis. In: Nathanail, E., Karakikes, I. (eds) Data Analytics: Paving the Way to Sustainable Urban Mobility. CSUM 2018. Advances in Intelligent Systems and Computing, vol 879. Springer, Cham. https://doi.org/10.1007/978-3-030-02305-8_20

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  • DOI: https://doi.org/10.1007/978-3-030-02305-8_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02304-1

  • Online ISBN: 978-3-030-02305-8

  • eBook Packages: EngineeringEngineering (R0)

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