Urban Microcirculation Traffic Network Planning Method Based on Fast Search Random Tree Algorithm

Authors

  • Ning YU Qiqihar University, School of Architecture and Civil Engineering

DOI:

https://doi.org/10.7307/ptt.v36i6.749

Keywords:

fast search random tree algorithm, microcirculation traffic, road network planning, shortest path, growth guidance function

Abstract

Unbalanced urban development causes complex and diverse urban traffic conditions, which complicates microcirculation traffic network planning. To address this, a method based on fast search random tree algorithm is proposed. An urban microcirculation traffic network is constructed using directed graphs, and road network interference intensity and capacity are calculated. The interpolation collision detection method is used to determine the shortest path while considering constraint conditions. By incorporating target gravity into the RRT algorithm, a growth guidance function is obtained, optimising the planned path and completing urban microcirculation traffic network planning. Experimental results demonstrate accurate shortest path calculation with up to 11% delay reduction compared to existing methods. Energy consumption during planning is lower than 10 kJ, ensuring fair resource distribution within the urban microcirculation transportation network. These advantages highlight the practicality and effectiveness of this research method.

References

Chen Z, Zhen G. A bidirectional context-aware and multi-scale fusion hybrid network for short-term traffic flow prediction. Promet – Traffic&Transportation: Scientific Journal on Traffic and Transportation Research. 2022;34(3):407–420. DOI: 10.7307/ptt.v34i3.3957.

Sun Y, Xu J, Wu H, et al. Deep learning based semi-supervised control for vertical security of maglev vehicle with guaranteed bounded airgap. IEEE Transactions on Intelligent Transportation Systems. 2021;22(7):4431-4442. DOI: 10.1109/TITS.2020.3045319.

Boeing G. Off the grid... and back again? The recent evolution of American street network planning and design. Journal of the American Planning Association. 2021;87(1):123–137. DOI: 10.1080/01944363.2020.1819382.

Yu N, Lu H J, Deng L, et al. Prediction simulation of spatial evolution of urban and rural road network based on remote sensing data. Computer Simulation. 2022;39(4):110–113.

Sreekumar M, Mathew T V. Modelling multi-class disordered traffic flow subject to varying vehicle composition using the concept of traversable distance. International Journal of Modern Physics C. 2020;31(12):97–118. DOI: 10.1142/S0129183120501703.

Hussain Q, et al. Improved traffic flow efficiency during yellow interval at signalized intersections using a smart countdown system. IEEE Transactions on Intelligent Transportation Systems. 2022;23(3):1959–1968. DOI: 10.1109/TITS.2020.3030130.

Sundhari R, Murali L, Baskar S, PM Shakeel. MDRP: Message dissemination with re-route planning method for emergency vehicle information exchange. Peer-to-Peer Networking and Applications. 2020;14(4):2285–2294. DOI: 10.1007/s12083-020-00936-z.

Victor S, Receveur JB, Melchior P, Lanusse P. Optimal trajectory planning and robust tracking using vehicle model inversion. IEEE Transactions on Intelligent Transportation Systems. 2021;23(5):4556–4569. DOI: 10.1109/TITS.2020.3045917.

Eichler M. Linking incidents to customers (LINC): An algorithm for linking incidents to rail customer delays inspired by traffic flow theory. Transportation Research Record. 2022;2676(3):598–607. DOI: 10.1177/03611981211054831.

Victor S, Receveur JB, Melchior P, Lanusse P. Optimal trajectory planning and robust tracking using vehicle model inversion. IEEE Transactions on Intelligent Transportation Systems. 2021;23(5):4556–4569. DOI: 10.1109/TITS.2020.3045917.

Zhou C, et al. A method for traffic flow forecasting in a large-scale road network using multifeatures. Promet – Traffic&Transportation: Scientific Journal on Traffic and Transportation Research. 2021;33(4):593–608. DOI: 10.1177/0361198121105483.

Lu B, et al. Make more connections: Urban traffic flow forecasting with spatiotemporal adaptive gated graph convolution network. ACM transactions on intelligent systems and technology. 2022;13(2):235–259. DOI: 10.1145/3488902.

Chen C, et al. An edge traffic flow detection scheme based on deep learning in an intelligent transportation system. IEEE transactions on intelligent transportation systems. 2021;22(3):1840–1852. DOI: 10.1109/TITS.2020.3025687.

Nilsson G, Como G. Generalized proportional allocation policies for robust control of dynamical flow networks. IEEE Transactions on Automatic Control. 2022;67(1):32–47. DOI: 10.1109/TAC.2020.3046026.

Sundhari R, Murali L, Baskar S, Shakeel PM. MDRP: Message dissemination with re-route planning method for emergency vehicle information exchange. Peer-to-Peer Networking and Applications. 2020;14(4):2285–2294. DOI: 10.1007/s12083-020-00936-z.

Dalal J, Ster H. Robust emergency relief supply planning for foreseen disasters under evacuation-side uncertainty. Transportation Science. 2021;55(3):791–813. DOI: 10.1287/trsc.2020.1020.

Kljaić Z, et al. Scheduling of traffic entities under reduced traffic flow by means of fuzzy logic control. Promet _ Traffic&Transportation: Scientific Journal on Traffic and Transportation Research. 2021;33(4):621–632. DOI: 10.7307/ptt.v33i4.3686.

Karpagalakshmi RC, et al. An effective traffic management system using connected dominating set forwarding (CDSF) framework for reducing traffic congestion in high density VANETs. Wireless personal communications: An International Journal. 2021;119(3):2725–2754. DOI: 10.1007/s11277-021-08361-y.

Sandamali, GGN, Su R, Sudheera KLK, Zhang Y. A safety-aware real-time air traffic flow management model under demand and capacity uncertainties. IEEE Transactions on Intelligent Transportation Systems. 2022:23(7):8615–8628. DOI: 10.1109/TITS.2021.3083964.

Artunedo A, Villagra J, Godoy J. Jerk-limited time-optimal speed planning for arbitrary paths. IEEE Transactions on Intelligent Transportation Systems. 2021;23(7):8194–8208. DOI: 10.1109/TITS.2021.3076813.

Jasim I, et al. The relationship between traffic congestion and land uses: A case study of Al-Kut city, Iraq. Journal of urban regeneration and renewal. 2021;14(3):264–271. DOI: 10.69554/AGYB2553.

Pi M, et al. Visual cause analytics for traffic congestion. IEEE transactions on visualization and computer graphics. 2021;27(3):2186-2201. DOI: 10.1109/TVCG.2019.2940580.

Downloads

Published

20-12-2024

How to Cite

YU, N. (2024). Urban Microcirculation Traffic Network Planning Method Based on Fast Search Random Tree Algorithm. Promet - Traffic&Transportation, 36(6), 1120–1132. https://doi.org/10.7307/ptt.v36i6.749