Let's Connect
Follow Us
Watch Us
(+385) 1 2380 262
journal.prometfpz.unizg.hr
Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

Sign In or Register Submit

PDF
PUBLISHED
31.08.2023
DOI
https://doi.org/10.7307/ptt.v35i4.217
ISSUE
2023 (Vol 35), Issue 4
LICENSE
Copyright (c) 2024 Ronghu Zhou, Qin Ge
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Design and Calculation of Evaluation Index for Urban Road Anti-Blocking Ability

Authors:Ronghu Zhou, Qin Ge

Abstract

Aiming at the lack of an anti-clogging ability index in the road network traffic evaluation index, an anti-clogging ability index was proposed to measure the anti-clogging ability of urban road traffic network: Κ-anti-clogging coefficient, which is used to measure the shortest path between any pair of starting and ending points on the urban road traffic network. After the current edge of the shortest path is blocked, the shortest path is selected from the current node of the shortest path. If the current edge of the shortest path is blocked again, the selection continues until the shortest path to the ending point is selected. In the case of unrecoverable congestion, the properties of the anti-clogging coefficient vector on any origin-destination pair, a path, and the whole traffic network are analysed, and the algorithm of the anti-clogging coefficient and its complexity are given. Finally, an example analysis is carried out using a local traffic network in a city.

Keywords:urban road, traffic network, Κ-anti-clogging coefficient, anti-clogging ability

References

  1. [1] Kim Y, et al. Scalable learning with a structural recurrent neural network for short-term traffic prediction. IEEE Sensors Journal. 2019(23):11359-11366. DOI: 10.1109/JSEN.2019.2933823.
  2. [2] Wei W, et al. An autoencoder and LSTM-based traffic flow prediction method. Sensors. 2019;19(13):2946. DOI: 10.3390/s19132946.
  3. [3] Sun B, Yin C. Impacts of a multi-scale built environment and its corresponding moderating effects on commute duration in China. Urban Studies. 2020:57(10):2115-2130. DOI: 10.1177/00420980198711.
  4. [4] Yin C, et al. Relationships of the multi-scale built environment with active commuting, body mass index, and life satisfaction in China: A GSEM-based analysis. Travel Behaviour and Society. 2020;21:69-78. DOI: 10.1016/j.tbs.2020.05.010.
  5. [5] Oyama T. Weight of shortest path analyses for the optimal location problem. Journal of the Operations Research Society of Japan. 2000;43(1):176-196. DOI: 10.15807/jorsj.34.187.
  6. [6] Liu X, Long Y. Automated identification and characterization of parcels with OpenStreetMap and points of interest. Environment and Planning B: Planning and Design. 2016;43(2):341-360. DOI: 10.1177/0265813515604767.
  7. [7] Oyama T, Morohosi H. Applying the shortest-path-counting problem to evaluate the importance of city road segments and the connectedness of the network-structured system. International Federation of Operational Research Societies. 2004;11(5):555-573. DOI: 10.1111/j.1475-3995.2004.00476.x.
  8. [8] Veronesi F, et al. Automatic selection of weights for GIS-based multicriteria decision analysis: Site selection of transmission towers as a case study. Applied Geography. 2017;83:78-85. DOI: 10.1016/j.apgeog.2017.04.001.
  9. [9] Su B, Xu Q. Finding the anti-block vital edge of a shortest path between two nodes. Journal of Combinatorial Optimization. 2007;12(16):173-181. DOI: 10.1007/s10878-007-9120-2.
  10. [10] Yang H, et al. A GIS‐based method to identify cost‐effective routes for rural deviated fixed route transit. Journal of Advanced Transportation. 2016;50(8):1770-1784. DOI: 10.1002/atr.1428.
  11. [11] Gunay A, et al. Building a semantic based public transportation geoportal compliant with the INSPIRE transport network data theme. Earth Science Informatics. 2014;7(1):25-37. DOI: 10.1007/s12145-013-0129-z.
  12. [12] Nardelli E, et al. A faster computation of the most vital edge of a shortest path between two nodes. Information Processing Letters. 2001;79(2):81-85. DOI: 10.1016/S0020-0190(00)00175-7.
  13. [13] Asakura Y, Kashiwadani M. Road network reliability caused by daily fluctuation of traffic flow. PTRC Summer Annual Meeting, 19th, 1991, University of Sussex, United Kingdom. 1991. p. 73-84.
  14. [14] Shen F, et al. Short-term traffic flow prediction of expressway based on CEEMD-GRU combined model. Journal of Hebei University of Science and Technology. 2021;(05):454-461. DOI: 10.7535/hbkd.2021yx05003. Chinese.
  15. [15] Huang Y, et al. Analysis of traffic congestion characteristics of road network based on data mining. Science Technology and Engineering. 2022;22(29):13083-13089. Chinese.
  16. [16] Shen J, et al. Research on urban rail transit resource allocation based on K-shortest paths algorithm. Advanced Materials Research. 2013;748:1285-1289. DOI: 10.4028/www.scientific.net/AMR.748.1285.
  17. [17] Xu W, et al. Finding the K shortest paths in a schedule-based transit network. Computers and Operations Research. 2012;39(8):1812-1826. DOI: 10.1016/j.cor.2010.02.005.
  18. [18] Hershberger J, et al. Finding the K shortest simple paths. ACM Transactions on Algorithms (TALG). 2007;3(4):45. DOI: 10.1145/1290672.1290682.
Show more

SPECIAL ISSUE CALL

Innovation and New Technologies in Transport and Logistics

Guest Editor: Eleonora Papadimitriou, PhD

Editors: Dario Babić, PhD; Marko Matulin, PhD; Marko Ševrović, PhD.

RECENT PAPERS

Browse through the selection of our recent research
Wireless Channel Propagation Model for Inland Waterway Bridge ScenarioA Deep Learning Approach for Enhanced Real-Time Prediction of Winter Road Surface Temperatures in High-Altitude Mountain AreasA Review of Expert Systems Integration in Signal Plan OptimisationImplications of Retrograde Behaviours on Visual and Cycling Behaviour of Normal Cyclists

Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal