Prediction of parking accumulation for different land uses in Abu Dhabi city from an existing survey

• Autorzy: Khliefat Ibrahim

Identyfikatory

DOI

10.20858/tp.2022.17.3.10

• Języki publikacji: EN

Abstrakty

EN

Parking accumulation profiles are time-dependent quantities at car parks that need data from longitudinal observational surveys. Car parks can be grouped into clusters based on the similarity in the shape of their accumulation profiles. This study investigates factors affecting parking accumulation and attempts to devise a methodology to construct parking accumulation profiles for different land uses in Abu Dhabi city. The study uses data available from Dubai city, which has a similar land-use type and distribution pattern as Abu Dhabi. A snapshot survey was conducted to observe the portion of the sampled car parks to calibrate the predicted parking accumulation. The estimation of parking accumulation profiles based on different land uses in Abu Dhabi city was done using the planning of the survey conducted in Dubai. The results suggest that a preliminary parking survey can help determine the initial accumulation profile and predict a robust parking accumulation profile that is applicable across different land-use types in Abu Dhabi.

Słowa kluczowe

EN

parking accumulation profile; parking arrival; parking duration; parking rates; traffic

PL

profil akumulacji parkingowej; przyjazd na parking; czas trwania parkowania; wskaźniki parkowania; ruch drogowy

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Transport Problems
• Rocznik: 2022
• Tom: T. 17, z. 3
• Strony: 111--124
• Opis fizyczny: Bibliogr. 23 poz.

Twórcy

autor

Khliefat Ibrahim

• Al-Balqa Applied University, Faculty of Engineering, Civil Engineering Department; 2PF8+XPM, As-Salt, Jordan

• https://orcid.org/0000-0003-1051-8228

Bibliografia

• 1. Dibas, M. & Al Jassmi, A. & Ibrahim, M. Solving parking issues: a case study of Abu Dhabi city. WIT Transactions on The Built Environment. 2015. Vol. 146. P. 179-190.
• 2. Millard-Ball, A. & Weinberger, R.R. & Hampshire, R.C. Is the curb 80% full or 20% empty? Assessing the impacts of San Francisco’s parking pricing experiment. Transportation Research Part A: Policy and Practice. 2014. Vol. 63. P. 76-92.
• 3. Al-Sahili, K. & Hamadneh, J. Establishing parking generation rates/models of selected land uses for Palestinian cities. Transportation Research Part A: Policy and Practice. 2016. Vol. 91. P. 213-222.
• 4. Chayan, M.M.H. Travel Characteristics-Based Parking Demand Models for Institutional Urban Areas. Doctoral dissertation. University of Pittsburgh. 2019.
• 5. Mazlum, Y. & Bayata, H.F. & Baş, F.İ. & et al. Analysis of car park etudes with different statistical methods and modeling with GIS: Erzincan province case. Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi. Vol. 11. No. 2. P. 497-509.
• 6. Parmar, J. & Das, P. & Dave, S.M. A machine learning approach for modelling parking duration in urban land-use. Physica A: Statistical Mechanics and its Applications. 2021. Vol. 572. Paper No. 125873.
• 7. Banu, M.M. & Rahman, M.M. Demand and supply of parking facility and the effects of on-street parking on roadway capacity. In: Proceedings of 3rd International Conference on Advances in Civil Engineering. 2016. P. 21-23.
• 8. Douglass, M. & Abley, S. Trips and Parking Related to Land Use (Report No. 453). 2011. NZ Transport Agency Research.
• 9. Bu, Y. & Pershouse, T. A practical application of modelling remote parking behaviour. In: 37th Australasian Transport Research Forum (ATRF). 2015.
• 10. Axhausen, K.W. & Polak, J.W. Choice of parking: stated preference approach. Transportation. 1991. Vol. 18. No. 1. P. 59-81.
• 11. Marsden, G. The evidence bases for parking policies—a review. Transport policy. 2006. Vol. 13. No. 6. P. 447-457.
• 12. Morency, C. & Trépanier M. Characterizing parking spaces using travel survey data. Canada: Cirrelt. 2008.
• 13. Shatnawi, I.M. Abu Dhabi parking rates requirements. Institute of Transportation Engineers. ITE Journal. 2010. Vol. 80. No. 9. P. 42-45.
• 14. Boamah, E.F. Modeling parking demand: A systems approach to parking policy analysis on campus. Minnesota State University, Mankato. 2013.
• 15. Sen, S. & Ahmed, M. A. & Das, D. A case study on on-street parking demand estimation for 4-wheelers in urban CBD. Journal of Basic and Applied Engineering Research. 2016. Vol. 3. No. 3. P. 254-258.
• 16. Silva, M.H.R. Predicting space occupancy for street paid parking. Doctoral dissertation. 2017. 17. Tong, C.O. & Wong, S.C. & Leung, B.S.Y. Estimation of parking accumulation profiles from survey data. Transportation. 2004. Vol. 31. No. 2. P. 183-202.
• 18. Bates, J.A. & Skinner, G. & Bradley, R. Study of Parking and Traffic Demand II. A demand Traffic Restraint Analysis Model (TRAM). Traffic Engineering and Control. 1997. Vol. 38. No. 3. P. 135-141.
• 19. Khattak, A. & Polak, J. Effect of parking information on travelers’ knowledge and behavior. Transportation. 1993. Vol. 20. No. 4. P. 373-393.
• 20. Hazime, H. From city branding to e-brands in developing countries: An approach to Qatar and Abu Dhabi. African Journal of Business Management. 2011. Vol. 5. P. 4731-4745.
• 21. Park, K. & Choi, D.A. & Tian, G. & et al. Not parking lots but parks: A joint association of parks and transit stations with travel behaviour. International journal of environmental research and public health. 2019. Vol. 16. No. 4. P. 547.
• 22. Jermsurawong, J. & Ahsan, U. & Haidar, A. & Haiwei, D.O.N.G. & Mavridis, N. One-day long statistical analysis of parking demand by using single-camera vacancy detection. Journal of Transportation Systems Engineering and Information Technology. 2014. Vol. 14. P. 33-44.
• 23. Blumer, K. & Halaseh, H.R. & Ahsan, M.U. & Dong, H. & Mavridis, N. Cost-effective single camera multi-car parking monitoring and vacancy detection towards real-world parking statistics and real-time reporting. In: International Conference on Neural Information Processing. 2012. P. 506-515. Springer. Berlin, Heidelberg.

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).