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Quantification of potential cruising time savings through intelligent parking services

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  • Cao, Jin
  • Menendez, Monica

Abstract

Intelligent parking services (IPS) use modern technology to help travelers find parking. Local authorities also recognize IPS as the new solution to reduce cruising-for-parking in urban areas. While many cities are considering to invest in the system, the potential of IPS in reducing cruising is often unknown. This makes it very difficult to assess the investment and evaluate the performance of IPS.

Suggested Citation

  • Cao, Jin & Menendez, Monica, 2018. "Quantification of potential cruising time savings through intelligent parking services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 151-165.
    • Handle: RePEc:eee:transa:v:116:y:2018:i:c:p:151-165
    DOI: 10.1016/j.tra.2018.06.010
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    References listed on IDEAS

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    Cited by:

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    2. Junheng Yang & Xiaodong Zang & Qiang Luo & Liming Shao, 2021. "Analysis on Stability of Roadside Parking System in a Rail-Integrated Transport Hub," Sustainability, MDPI, vol. 13(9), pages 1-16, April.
    3. Amaya, Johanna & Encarnación, Trilce & Delgado-Lindeman, Maira, 2023. "Understanding Delivery Drivers’ Parking Preferences in Urban Freight Operations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    4. Dave, Sanjaykumar M. & Joshi, Gaurang J. & Ravinder, Kayitha & Gore, Ninad, 2019. "Data monitoring for the assessment of on-street parking demand in CBD areas of developing countries," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 152-171.
    5. Huanmei Qin & Ning Xu & Yonghuan Zhang & Qianqian Pang & Zhaolin Lu, 2023. "Research on Parking Recommendation Methods Considering Travelers’ Decision Behaviors and Psychological Characteristics," Sustainability, MDPI, vol. 15(8), pages 1-22, April.
    6. Yang, Kaidi & Roca-Riu, Mireia & Menéndez, Mónica, 2019. "An auction-based approach for prebooked urban logistics facilities," Omega, Elsevier, vol. 89(C), pages 193-211.
    7. Abdelghaffar, Hossam M. & Batista, S.F.A. & Rehman, Abdur & Cao, Jin & Menéndez, Mónica & Jabari, Saif Eddin, 2024. "Comparison of probabilistic cruising-for-parking time estimation models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 184(C).
    8. Fan Wu & Wei Ma, 2022. "Clustering Analysis of the Spatio-Temporal On-Street Parking Occupancy Data: A Case Study in Hong Kong," Sustainability, MDPI, vol. 14(13), pages 1-25, June.
    9. Gu, Ziyuan & Safarighouzhdi, Farshid & Saberi, Meead & Rashidi, Taha H., 2021. "A macro-micro approach to modeling parking," Transportation Research Part B: Methodological, Elsevier, vol. 147(C), pages 220-244.
    10. Yan, Xiang & Levine, Jonathan & Marans, Robert, 2019. "The effectiveness of parking policies to reduce parking demand pressure and car use," Transport Policy, Elsevier, vol. 73(C), pages 41-50.
    11. Najmi, Ali & Bostanara, Maryam & Gu, Ziyuan & Rashidi, Taha H., 2021. "On-street parking management and pricing policies: An evaluation from a system enhancement perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 146(C), pages 128-151.

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