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Modelling demand in restricted parking zones

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  • Ibeas, Ángel
  • Cordera, Ruben
  • dell'Olio, Luigi
  • Moura, Jose Luis

Abstract

Multiple linear regression (MLR) and geographically weighted regression (GWR) models are used for estimating parking demand in areas with paid short stay parking systems. These models have been applied to the city of Santander (Cantabria, Spain) to check their goodness of fit and their predictive ability. The results show the main advantages and disadvantages of using GWR models. The technique proved to be useful in this case study because it offered a better fit and made better predictions in a scenario showing a certain degree of spatial heterogeneity unexplained by any of the variables introduced into the global model. However, the GWR model also presented situations of local correlation although this was considered moderate given the results provided by the variance inflation factors and the local condition indexes.

Suggested Citation

  • Ibeas, Ángel & Cordera, Ruben & dell'Olio, Luigi & Moura, Jose Luis, 2011. "Modelling demand in restricted parking zones," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(6), pages 485-498, July.
    • Handle: RePEc:eee:transa:v:45:y:2011:i:6:p:485-498
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Arnott, Richard & Rowse, John, 2013. "Curbside parking time limits," Transportation Research Part A: Policy and Practice, Elsevier, vol. 55(C), pages 89-110.
    2. Ibeas, Ángel & Cordera, Ruben & dell’Olio, Luigi & Coppola, Pierluigi & Dominguez, Alberto, 2012. "Modelling transport and real-estate values interactions in urban systems," Journal of Transport Geography, Elsevier, vol. 24(C), pages 370-382.
    3. Milosavljević, Nada & Simićević, Jelena, 2016. "User response to parking policy change: A comparison of stated and revealed preference data," Transport Policy, Elsevier, vol. 46(C), pages 40-45.
    4. Lucas, Karen & Philips, Ian & Mulley, Corinne & Ma, Liang, 2018. "Is transport poverty socially or environmentally driven? Comparing the travel behaviours of two low-income populations living in central and peripheral locations in the same city," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 622-634.
    5. Junji Urata & Adam J. Pel, 2018. "People's Risk Recognition Preceding Evacuation and Its Role in Demand Modeling and Planning," Risk Analysis, John Wiley & Sons, vol. 38(5), pages 889-905, May.
    6. Yifei Cai & Jun Chen & Chu Zhang & Bin Wang, 2018. "A Parking Space Allocation Method to Make a Shared Parking Strategy for Appertaining Parking Lots of Public Buildings," Sustainability, MDPI, vol. 11(1), pages 1-20, December.
    7. Simićević, Jelena & Vukanović, Smiljan & Milosavljević, Nada, 2013. "The effect of parking charges and time limit to car usage and parking behaviour," Transport Policy, Elsevier, vol. 30(C), pages 125-131.
    8. Kalahasthi, Lokesh Kumar & Sánchez-Díaz, Iván & Pablo Castrellon, Juan & Gil, Jorge & Browne, Michael & Hayes, Simon & Sentís Ros, Carles, 2022. "Joint modeling of arrivals and parking durations for freight loading zones: Potential applications to improving urban logistics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 307-329.
    9. Gragera, Albert & Albalate, Daniel, 2016. "The impact of curbside parking regulation on garage demand," Transport Policy, Elsevier, vol. 47(C), pages 160-168.
    10. Chang, Ching-Ter & Chung, Cheng-Kung & Sheu, Jiuh-Biing & Zhuang, Zheng-Yun & Chen, Huang-Mu, 2014. "The optimal dual-pricing policy of mall parking service," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 223-243.
    11. Andrés Rodríguez & Luigi dell’Olio & José Luis Moura & Borja Alonso & Rubén Cordera, 2023. "Modelling Parking Choice Behaviour Considering Alternative Availability and Systematic and Random Variations in User Tastes," Sustainability, MDPI, vol. 15(11), pages 1-18, May.

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