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Computer Science > Computers and Society

arXiv:2203.13976 (cs)
[Submitted on 26 Mar 2022]

Title:Driver Side and Traffic Based Evaluation Model for On-Street Parking Solutions

Authors:Qianyu Ou, Wenjun Zheng, Zhan Shi, Ruizhi Liao
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Abstract:Parking has been a painful problem for urban drivers. The parking pain exacerbates as more people tend to live in cities in the context of global urbanization. Thus, it is demanding to find a solution to mitigate d rivers' parking headaches. Many solutions tried to resolve the parking issue by predicting parking occupancy. Their focuses were on the accuracy of the theoretical side but lacked a standardized model to evaluate these proposals in practice. This paper develops a Driver Side and Traffic Based Evaluation Model (DSTBM), which provides a general evaluation scheme for different parking solutions. Two common parking detection methods, fixed sensing and mobile sensing are analyzed using DSTBM. The results indicate first, DSTBM examines different solutions from the driver's perspective and has no conflicts with other evaluation schemes; second, DSTBM confirms that fixed sensing performs better than mobile sensing in terms of prediction accuracy.
Comments: 7 pages, 4 figures
Subjects: Computers and Society (cs.CY)
Cite as: arXiv:2203.13976 [cs.CY]
  (or arXiv:2203.13976v1 [cs.CY] for this version)
  https://doi.org/10.48550/arXiv.2203.13976

Submission history

From: Ruizhi Liao [view email]
[v1] Sat, 26 Mar 2022 03:00:23 UTC (2,586 KB)
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