Traffic Flow Analysis and Parking Demand Estimation in Tourist Areas Using Moving Average and Location Backtracking Methods

Xi Gong; Tinghua Lu; Jie Zeng

doi:10.54097/569h9x02

Authors

Xi Gong
Tinghua Lu
Jie Zeng

DOI:

https://doi.org/10.54097/569h9x02

Keywords:

Traffic congestion; Peak-period differences; Parking demand estimation; Traffic signal configuration optimization.

Abstract

With the rapid development of tourism, traffic congestion and parking demand issues in scenic areas have become increasingly prominent, particularly at the intersection of Jingzhong Road and Weizhong Road in a renowned scenic town. Significant peak-period differences exist: the morning peak (7:00-9:00) witnesses 385,495 vehicles in the north-to-east direction, while the evening peak (17:00-19:00) records 122,671 vehicles in the south-to-north direction, with peak traffic accounting for 35% of daily volume. This study uses methods like moving averages, time discretization, and location backtracking to analyze data, identifying distinct peak periods and directional flow variations while quantifying 283,885 cruising vehicles (via a 300-second time threshold) to estimate a demand for 340,662 temporary parking spaces during the May Day Golden Week. The findings enable scientific optimization of traffic signal configurations—such as allocating 40% more green light time to peak directions—and rational parking resource allocation, providing a data-driven framework to alleviate congestion, enhance traffic management efficiency, and improve travel experiences in scenic areas and urban destinations.

Downloads

Download data is not yet available.

References

[1] Lu H, Zhang J, Xu Z, et al. Prediction of tourist flow based on multi‐source traffic data in scenic spot[J]. Transactions in GIS, 2021, 25(2): 1082-1103.

[2] Gao F, Liu Y, Zhang L. Research on the Forecast Model of Tourist Flow in Scenic Spots Based on Big Data Analysis[C]//2021 2nd Artificial Intelligence and Complex Systems Conference. 2021: 198-201.

[3] Litman T. Parking management best practices[M]. New York: Routledge, 2020.

[4] Bilotta S, Nesi P. Traffic flow reconstruction by solving indeterminacy on traffic distribution at junctions[J]. Future Generation Computer Systems, 2021, 114: 649-660.

[5] Medina-Salgado B.Urban traffic flow prediction techniques: A review [J]. Sustainable Computing: Informatics and Systems, 2022, 35: 100739.

[6] Qadri S S S M, Gökçe M A, Öner E. State-of-art review of traffic signal control methods: challenges and opportunities[J]. European Transport Research Review, 2020, 12: 1-23.

[7] Abbood A N, Ahmed A R I, Ajam H K. Evaluation of parking demand and future requirement in the urban area[J]. Civil Engineering Journal, 2021, 7(11): 1898-1908.

[8] Zhou L, Chen H. Research on the Construction and Planning of Industrial Complexes in Scenic Areas[J]. Sustainable Development Research, 2025, 7(2): 1.

[9] Pillera R, Bissaldi E, Omodei N, et al. GRB 221009A: Fermi-LAT refined analysis[J]. GRB Coordinates Network, 2022, 32658: 1.

[10] Dudek A E, Hurd H, Wójtowicz W. Periodic autoregressive moving average methods based on Fourier representation of periodic coefficients[J]. Wiley Interdisciplinary Reviews: Computational Statistics, 2016, 8(3): 130-149.

[11] Peng S, Jun R, Lei L, et al. A Scene Matching Backtracking Location Method Based on Convergence Criterion[C]//China Conference on Command and Control. Singapore: Springer Nature Singapore, 2023: 326-336.

[12] Khan A B F, Ivan P. Integrating machine learning and deep learning in smart cities for enhanced traffic congestion management: an empirical review[J]. Journal of Urban Management, 2023, 2(4): 211-221.

[13] Rosenblum J, Hudson A W, Ben-Joseph E. Parking futures: An international review of trends and speculation[J]. Land Use Policy, 2020, 91: 104054.

[14] Zhu Q, Liu Y, Liu M, et al. Intelligent planning and research on urban traffic congestion[J]. Future Internet, 2021, 13(11): 284.

[15] Alshayeb S, Stevanovic A, Mitrovic N, et al. Traffic signal optimization to improve sustainability: A literature review[J]. Energies, 2022, 15(22): 8452.