Identifying key determinants for parking management to reduce road traffic congestion for congested cities – A Structural Equation Modelling approach
J. Ujjwal, V. Bandyopadhyaya, R. Bandyopadhyaya
Pages: 143-158
Abstract:
Road traffic congestion is a major concern worldwide, particularly in old cities with narrow roads and mixed land-use. Increased parking demand is one of the major reasons for this problem. As increasing infrastructure may not be feasible, managing available parking spaces efficiently and designing measures discouraging personal vehicle use may reduce this problem. This work aims to assess the actual parking demand-supply gap in a typical congested city of Patna, India and determine the factors important for reducing this gap. To identify the factors, an exhaustive list of variables were identified from literature and a structured questionnaire was developed. Perception survey was conducted in 5-point Likert scale with 203 respondents from Patna using the questionnaire. It contained five variables related to awareness, four related to ascription of responsibility, six related to social norm for self-driving, eleven related to parking management and enforcement strategies and eight related to parking pricing effectiveness. Exploratory factor analysis (EFA) was conducted with 120 responses for the five sets of measured variables to identify the underlying factors. Four behavior measurement factors namely awareness, responsibility, self-control and social-influence and five management and enforcement factors namely infrastructure, differential pricing, penalty, general effect and personal effect were identified from EFA. Confirmatory factor analysis (CFA) was conducted with remaining 83 responses using Structural Equation Modeling. It could be observed from regression weights and covariance between identified factors, obtained from CFA, that higher awareness about congestion problems lead to greater willingness of people to accept responsibility and proactively act to reduce congestion. Thus, awareness programs will have positive effects in reducing congestion. However, such programs are not likely to improve acceptability of increased parking pricing. People’s willingness to pay will increase with improved parking facilities in congested places. The proposed survey may be used as a standard questionnaire for survey in other places.
Keywords: road congestion; parking management; factor analysis; Structural Equation Modeling
2025 ISSUES
2024 ISSUES
LXII - April 2024LXIII - July 2024LXIV - November 2024Special 2024 Vol1Special 2024 Vol2Special 2024 Vol3Special 2024 Vol4
2023 ISSUES
LIX - April 2023LX - July 2023LXI - November 2023Special Issue 2023 Vol1Special Issue 2023 Vol2Special Issue 2023 Vol3
2022 ISSUES
LVI - April 2022LVII - July 2022LVIII - November 2022Special Issue 2022 Vol1Special Issue 2022 Vol2Special Issue 2022 Vol3Special Issue 2022 Vol4
2021 ISSUES
LIII - April 2021LIV - July 2021LV - November 2021Special Issue 2021 Vol1Special Issue 2021 Vol2Special Issue 2021 Vol3
2020 ISSUES
2019 ISSUES
Special Issue 2019 Vol1Special Issue 2019 Vol2Special Issue 2019 Vol3XLIX - November 2019XLVII - April 2019XLVIII - July 2019
2018 ISSUES
Special Issue 2018 Vol1Special Issue 2018 Vol2Special Issue 2018 Vol3XLIV - April 2018XLV - July 2018XLVI - November 2018
2017 ISSUES
Special Issue 2017 Vol1Special Issue 2017 Vol2Special Issue 2017 Vol3XLI - April 2017XLII - July 2017XLIII - November 2017
2016 ISSUES
Special Issue 2016 Vol1Special Issue 2016 Vol2Special Issue 2016 Vol3XL - November 2016XXXIX - July 2016XXXVIII - April 2016
2015 ISSUES
Special Issue 2015 Vol1Special Issue 2015 Vol2XXXV - April 2015XXXVI - July 2015XXXVII - November 2015
2014 ISSUES
Special Issue 2014 Vol1Special Issue 2014 Vol2Special Issue 2014 Vol3XXXII - April 2014XXXIII - July 2014XXXIV - November 2014
2013 ISSUES
2012 ISSUES
2011 ISSUES
2010 ISSUES
2009 ISSUES
2008 ISSUES
2007 ISSUES
2006 ISSUES
2005 ISSUES
2004 ISSUES
2003 ISSUES