Finding effective parameters for mitigating traffic congestion near universities

Navid Nadimi*, Amin Khoshdel Sangdeh, Mohammadjavad Berangi

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
57 Downloads (Pure)

Abstract

This paper intends to assess the effect of different parameters on traffic congestion around universities. On the basis of the model outputs, it is possible to propose economic countermeasures for reducing traffic congestion, especially in developing countries. Structural equation modelling was used to assess the relevance between characteristics of students, features of different modes, environmental conditions and daily demand variations with traffic congestion. The Shahid Bahonar University of Kerman in Iran was considered as a case study. The results showed that it is necessary to decrease the demand first. For this purpose, rescheduling courses is essential to distribute classes more effectively within a week. Virtual classes can be used more frequently as a substitute for traditional on-campus courses. The probability of using buses should be increased by reducing waiting time and fares, and promoting their safety. Similarly, taxi use can be increased by improving safety and waiting time. To reduce the likelihood of using private cars, pricing strategies must establish more limitations for using university carparks.

Original languageEnglish
Article number2200009
Pages (from-to)63-73
Number of pages11
JournalProceedings of the Institution of Civil Engineers: Municipal Engineer
Volume176
Issue number2
DOIs
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Modeling
  • Public policy
  • Statistical analysis
  • Transport planning
  • UN SDG 11: Sustainable cities and communities

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