Bayesian Probabilistic Prediction Based Parking Lot Sharing Model for Residential Neighborhoods

DOI

10.2991/978-94-6463-514-0_60How to use a DOI?

Keywords

Shared parking; neighborhood parking; Bayesian networks; parking spatial-temporal coverage; parking turnover rate

Abstract

To develop idle parking resources, enhance existing parking efficiency, and alleviate urban parking challenges, this paper focuses on residential community parking lots. It constructs a Bayesian probability matrix to predict the combination of vehicle arrival times and required parking durations. Based on this, it proposes two parking sharing modes: simultaneous opening and batch opening. It compares the differences in parking temporal and spatial coverage and turnover rate under different sharing modes, and conducts sensitivity analysis on key influencing factors to justify the rationality of the settings. The research findings indicate that under a fixed sharing scale, the average parking temporal and spatial coverage rates for the two sharing modes are 73.57% and 86.60% respectively. The second sharing mode yields higher parking temporal and spatial coverage rates compared to the former, albeit with lower turnover rates. This suggests higher utilization of parking spaces during sharing periods but lower adaptability to parking demands. Under this mode, parking lot utilization throughout the day increases by 35.88% compared to before sharing. Moreover, on the basis of the second sharing mode, sensitivity analysis is conducted on the key setting parameter of “the number of parking spaces opened per hour.” It is found that under a fixed sharing scale, parking temporal and spatial coverage rates are positively correlated with the number of parking spaces opened per hour. When the sharing scale is 50 and the number of parking spaces opened per hour reaches 9 or more, the variance in parking temporal and spatial coverage rates gradually decreases and stabilizes, indicating a more economical and practical approach. Additionally, this paper provides suggestions for relatively reasonable sharing modes for shared parking lots, laying a foundation for the development and construction of urban shared parking lots.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yihang Shen
AU  - Yixuan Wang
PY  - 2024
DA  - 2024/09/28
TI  - Bayesian Probabilistic Prediction Based Parking Lot Sharing Model for Residential Neighborhoods
BT  - Proceedings of the 2024 7th International Symposium on Traffic Transportation and Civil Architecture (ISTTCA 2024)
PB  - Atlantis Press
SP  - 624
EP  - 634
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-514-0_60
DO  - 10.2991/978-94-6463-514-0_60
ID  - Shen2024
ER  -